What We Have Here is a Failure To Communicate

CEOs, “Cool Hand Luke” & Cribbage

What We Have Here is a Failure To Communicate
“Don’t you ever talk that way to me. NEVER!”

Who can forget the line, “What we have here is a failure to communicate.”, from the classic movie, “Cool Hand Luke” (Click image above for movie clip)

To be a success, whether it is in the Boardroom, courtroom, or prison yard, the #1 factor driving your success is your ability to communicate effectively with other individuals. DUH! Tell me something I don’t know. (Ok, ok, give me a few more seconds & I promise this article will be worth your attention.)

CEOs – Leadership, Vision & Understanding

Leadership is all about inspiring others to take action in accomplishing a shared objective. To do so, you must be able to communicate your vision in a way that is both clear & concise. What’s more, you must be able to understand the needs & wants of your team. This can only be accomplished through continuous & effective communication. To be a successful CEO, you must know how to communicate effectively with your team.

Excellent English & Communication Skills

Cool Hand Luke – Listening & Responding

What does the movie “Cool Hand Luke” have to do with communication? Well, other than the famous line, “What we have here is a failure to communicate”, not much, but most Americans have probably heard the phrase & it’s a good reminder that communication is more than just speaking. It’s about UNDERSTANDING.

Unfortunately, many people misunderstand the meaning of “communication”. They think it’s simply about exchanging information, but real communication only takes place when all of the parties involved have the same understanding of what was intended to be conveyed.

This is why so many people miscommunicate. They assume that because they said something, the other person must have understood it correctly. But that’s not always the case. Just because you speak doesn’t mean you’re communicating. And just because you hear doesn’t mean you understand.

To be an effective communicator, you also need to be…….. a good listener. You need to be able to put yourself in the other person’s shoes & see things from their perspective. Only then will you be able to ensure that they understand what you’re trying to say.

IT Staff Augmentation

Cribbage – Non-verbal Communication

What does a card game played with a board have to do with communication? The answer, everything.

“They may forget what you said, but they will never forget how you made them feel.” – Carl W. Buechner

Not long ago I was playing cribbage with a good friend, Bill, and we were enjoying a few cocktails. (His wife makes a great Margarita🍸) Now, we’d been playing cribbage for many years & it was always a good way to while away the time & have some fun. Well, on this occasion, for some reason, we had a bit of a kerfuffle. Unbeknownst to me, I had somehow forgotten how to properly count my points.

As I said, we were playing cribbage when Bill says, “Dude, you’re counting your points wrong”

“What do you mean I’m counting my points wrong? I’ve been counting my points this way for years & you never said anything before”, I replied.

And for 20 minutes we very politely discussed how big of an idiot I was for counting my points wrong. Finally, being the problem solver that I am, I said, “Let’s play another hand & we’ll figure out what’s going on.”

Now, this was a very curious situation. Bill & I had been playing this very simple card game for over 20 years & only now we’re having a problem? Bill is a super sharp guy, so why is he bringing up this issue only now? I didn’t get it.

Now, Bill & I have about as similar a background as you can get. He’s a brilliant engineer who has succeeded wildly on many amazing projects. We were both born & raised in Silicon Valley, went to rival high schools before university & our cultural influences are essentially identical. To top things off, our careers have been in tech, so what’s going on?!

So, we played another hand & when I started to count the points in my hand Bill exclaimed, “There! Right there, that’s what you’re doing wrong!” I replied, “What are you talking about, I have 15 points in my hand (we counted them to confirm) & I moved my peg 15 holes. I had 25 points + 15 points = 40 points, so how is that wrong?”

It was then that we both realized how we had miscommunicated about a very simple activity we had been enjoying for 20+ years. Bill was linearly counting his points, meaning one at a time, while I had just jumped to the correct score (25+15=40) using the reference marks every 5 points. This was the miscommunication?! How silly! Of course, we may have also enjoyed a few Margaritas! 🎯

Communication Breakdown

The moral of the story is this, if two good friends, both with good intent, who have been doing the same, very simple thing for years can suffer a communication breakdown, you can bet your life it will happen in any relationship, personal or professional. It is inevitable. This is especially true the more complex a scenario, i.e., marriage, children, legal issues, a software engineering project, etc.

Ending All Miscommunication

Eliminating all communication breakdowns is impossible, but we can take steps to minimize them. The first & most important is to always remember that communication is a two-way street. It requires both parties to be active participants, not just the person doing the talking. Secondly, we must try to understand the message from the other person’s perspective. Third, & by no means a final step, is the parties must be of good intent.

Transparent Communications

It has always been my practice to supply all the available information to the other parties as soon as possible, both personal & professional. My family, friends, clients, etc., all have a lot of investment in our relationships & they need to be able to make intelligent, informed decisions for any relationship to be mutually beneficial.

What We Have Here is a Failure To Communicate

We live in a world where it’s easier than ever to connect with people. Social media has made it possible for us to connect & work with people all over the world & with new opportunity comes new challenges.

Effective communication is a major challenge when engaging with engineering resources from different countries. If you’ve used offshore or nearshore resources you know that technical English is not the only challenge. The cultural differences can be major, i.e. Low Context Communication Cultures vs. High Context Cultures, business practices, time zones, etc. If you would like to know more about how to proactively reduce your risk of miscommunication when working with international engineering teams, please reach out to Cloud App Developers, LLC. We have a lot of experience in this area & are happy to guide & assist you on your offshore engineering journey. Thank you!


Contact Cloud App Developers

If you need to supplement or scale your software development teams & would like to know more about the options Cloud App Developers can bring to the table, please describe your objectives using the form below & we will respond shortly. Thank you.

Hungary: A Safe Haven for Offshore Software Development

Budapest, Hungary

With its rich engineering pedigree and EU/NATO membership, Hungary stands out as one of the best offshore software development countries in the world.

We’ve been spoiled. For a few decades, we have not had to worry about political stability when choosing an outsourcing country to build our remote teams. Russian aggression in Eastern Europe reminds us that geopolitical factors can’t be ignored when assessing outsourcing risk.

For several reasons, many now consider Hungary a safe haven for Agile Offshore Software Development.

Why Hungary Tops the Best Offshore Software Development Countries

Hungary is a Member of NATO and the EU

Some Eastern European countries, including the Baltic States, are now at risk from Russian aggression. As a Central European country, and being a member of both the EU & NATO, Hungary is considered a stable outsourcing destination.

Engineering Classes are Taught Exclusively in English

According to EF EPI, the preeminent English Proficiency rating body in the world, Hungary’s English proficiency is rated as “Very High”.

All engineering classes in Hungary are taught in English & students must pass the IELTS test in order to enter Engineering School.

In fact, students can’t even enroll in one of Hungary’s many Engineering Colleges without passing the IELTS test.

The International English Language Testing System (IELTS) is designed to help you work, study or migrate to a country where English is the native language.

English-Only in Hungarian Colleges

Highest # of Developers Per Capita in Europe

Hungary enjoys a long-standing, rich Engineering Culture. Despite its modest 10M population, Hungary has the largest software development pool in Central Europe, resulting in the highest number of Developers per capita in Eastern/Central Europe.

Top Developer Skill Ranking

Hungary ranks near the top of all European countries, ranked by developer tech score, according to a 2021 HackerRank study.

Best Offshore Software Development Countries (by Tech Score)

RANKCOUNTRYDEVELOPER TECH SCORE
1Slovakia100
2Czech Republic93.38
3Hungary92.86
4Poland92.74
5Serbia92.59
6Ireland92.27
7Ukraine91.26
8Switzerland91.08
9Germany90.70
10Austria90.15

Hungary has a Low-Context Communication Culture

In her groundbreaking book, “The Culture Map”, Erin Meyer outlines the importance of having a low-context communication culture when blending multi-cultural, geographically dispersed teams.

Hungary has a similar “low-context communication culture” to North America & Europe, which makes communication efficient & crisp.

For more information on how critical low-context communication is for achieving “Agile @ Speed” with your remote development team, read The Secret to Agile With Offshore Teams.

Summary

Hungary is quickly becoming one of the world’s best offshore software development countries. If you would like to learn more about what Hungary can do for you, please visit Cloud App Developers, LLC to learn more, or contact us below and we will respond within hours.

About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Open a Dialogue With Our Hungarian Development Team

Top 6 Tips For Startup Founders – How to Make Offshoring Work

Most Offshore Software Development partners simply don’t understand these critical realities:

Startups can’t afford failures or delays.

They don’t have the bandwidth to micro-manage remote software dev teams.

Startups need software developers who can “get it right the first time with minimal supervision”

Agile @ Speed
Agile @ Speed

This article delivers a formula for Successful Offshore Software Development for Startups.

The ability to “get it right the first time” with “Agile @ Speed” dramatically reduces the risk associated with external development teams & makes offshoring a “Low Risk” + “Low Cost” winning combination.


What Do Startups Need from Offshore Development Teams?

  • Low-cost & Low-risk Resources
  • Excellent English & Communication Skills
  • Product Ownership & Accountability
  • Problem-solving Mentality
  • Quality Code

Critical Offshoring Success Factors for Startups

  • Startups can’t afford failures or delays.
  • Startups don’t have the bandwidth to micro-manage vendors.
  • Waterfall won’t work for most startups. They need “Agile @ Speed” from offshore teams to succeed.
  • Engineers = Agile. Coders = Waterfall. Startups need Engineers.

6 Top Offshoring Tips For Startups

1. Low-Context Communication is Critical for Success

Communication between you and your offshore team will be much easier if they practice low-context communication. 

What is the Difference Between Low-Context and High Context Communication?

high context vs low context communication

Low/High Context Communication By Country

Image source: Tech Tello

Wherever possible, pick developers from countries with low-context communication.

Again, startups don’t have the bandwidth to micro-manage their offshore partners.

Low-context communication leaves nothing to interpretation and is the only way to ensure clear, crisp communication between multi-cultural teams.

It’s easy to identify low-context communication by interacting with the developers 1:1.

  • Send the prospective development partner a few emails with detailed questions and see how they answer.  You are looking for detailed, thoughtful answers that leave nothing to interpretation.
  • Ask to speak in person with a few of the team members that would be on the team you will be working with, looking for low-context verbal communication capabilities.

More information on low-context communication


2. Screen Vendors for “Agile @ Speed”

With the multitude of changes, dependencies, and processes within Agile methodologies, a certain velocity of communication, interaction, and concurrency is required.

Without this velocity of communication, “Agile @ Speed” is simply not possible.

What is “Agile @ Speed?”

Agile @ Speed is an important metric for integrating a remote team with your internal team.

Many companies practice Agile methodologies. But Agile is only powerful as a methodology if it can be practiced with a certain velocity.

Startup teams require remote developers who communicate and interact with speed.

How to Screen for “Agile @ Speed”

The best way to screen for “Agile @ Speed” is to work with the vendor on a small project, as seen in Tip #3.


3. Vet Prospective Vendors with a Small Proof of Concept Project

If you absolutely can’t afford to have an “Offshore Vendor False Start”, consider a small Proof of Concept to experience the team’s capabilities before committing. 

This can be helpful if you want to evaluate specific technical capabilities or want to really see if their communication culture is a fit for your company. You will also see if they are capable of “Agile at Speed”.

Pick a project or sprint that can be completed in a few weeks.  In 2-4 weeks, you will find out a lot about a potential dev partner’s capability. 


4. Look for Communication Excellence “2-Layers Deep”

Interview the team leaders AND a few of the key engineers one or two layers down to evaluate their communication skills.  

A common practice of some offshoring/nearshoring software houses is to insert a “customer liaison”, a kind of technical lead who can ensure proper communication between your engineers and the remote team.  This should be a red flag that the team in question lacks communication skills.

There are also other problems with the liaison model:

  • The bulk of communication within the development team happens between developers.  The liaison can’t possibly be in every conversation.
  • Much is lost in translation (see telephone game)
  • “Agile at Speed” is required between various dev team members. A liaison only adds more delays.

5. Manage Political Risk

The recent troubles in Ukraine and Eastern Europe have reminded us of the importance of managing geopolitical risk. Pick vendors from countries with political and economic stability.

Hungary and Poland, having EU and NATO membership, are safer destinations for outsourcing than some Eastern European countries, for example.

Read 7 Reasons to Outsource Software Development to Hungary in 2022 if you would like to take advantage of Hungary’s many benefits and avoid political risk.


6. Make Your Expectations Known Up Front

Never assume prospective offshore/nearshore dev partners understand your expectations.  Be specific on what’s important to you.  Some examples are:

  • Do you expect Engineers or Coders?
  • Agile (not Waterfall)?
  • Expected throughput per engineer
  • Availability and Accessibility of team
  • Seniority expectations.  (What do you mean by “Sr. Developer”?)

Contrary to popular belief, most offshore/nearshore development firms do not “bait and switch” to get a contract.  More commonly, expectations are simply not communicated clearly from the beginning, leading to mismatches.

Summary

Startups who work with development partners who understand their needs, and are capable of delivering the value that they seek are much more likely to succeed with offshoring.

If you would like to work with an Offshore Development Partner who delivers value, please contact wes@cloudappdevelopers.com or fill out the form below.

Contact Us for More Information

About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Nearshore vs Offshore Software Development

Nearshore vs Offshore

Nearshore vs Offshore Software Development. That is the decision many Engineering teams are debating as they struggle to scale remote development teams amidst a global shortage of developers. When looking for the best possible fit for your company, it’s not meaningful to simply lump all worldwide software development centers into either Nearshoring vs Offshoring. 

In this article, we examine the pros and cons of offshoring and nearshoring, but with an important distinction: The nearshoring vs offshoring label you give it means very little.

Both Mexico and Argentina are considered Nearshoring, but there are considerable differences between the two.  The same can be said for Central Europe vs Eastern Europe.

To highlight these differences, we compared these 5 regions in several categories:    

Mexico, Latin America, Central Europe, Eastern Europe, and India.

Nearshore vs Offshore Comparison Chart

CriteriaMexicoLatAmCentral
Europe
Eastern
Europe
India
Primary Tech
Centers
MultipleArgentina,
Colombia, 
Costa Rica
Hungary,
Poland
Ukraine
Belarus
Multiple
ModelNearshoreNearshoreOffshoreOffshoreOffshore
Software Dev
Hourly Rates
ModerateModerateLowLowVery
Low
Time Zone
vs USA
0-3 hrs.0-5 hrs.6-9 hrs.7-10 hrs9.5-12.5
hrs.
Size of
Talent Pool
Low/MidLow/MidMidMidHigh
Rapid
Scalability
ModerateModerateModerateModerateHigh
Tech English
Proficiency
HighMediumHighMidMedium
Communication
Culture
Mid
Context
Mid
Context
Low
Context
Low
Context
High
Context
Nearshore vs Offshore Selection Criteria

Global Software Development Hourly Rates

Latin America and Mexico come in at about 50% of the cost of US rates, on average. 

Central and Eastern Europe are next lowest at 40%.

India averages 30% of US rates.

Not included in these calculations are the costs of managing and supporting the remote nearshore vs offshore team, which varies by type of model chosen as well as the capabilities of the dev partner chosen. 

In general, it costs more to manage and support India teams than their counterparts from other regions, due to time zone and cultural communication differences.  

If you want to outsource to India, you will need to allocate more internal engineering resources to manage the Indian dev partner. This cost is often hard to justify for offshore development teams of 25 or less. Nonetheless, India can be a very low-cost option and the best way to scale a large team quickly.

Time Zone Difference vs USA

From a time-zone perspective, a Nearshoring vs Offshoring comparison may not be as straightforward as you think. What may be nearshoring for some may be offshoring for others.  Comparing Argentina and Hungary to EST and PST time zones demonstrates this.   

US Time ZoneGuadalajara,
Mexico
ArgentinaHungaryIndia
PST+2+5+9+13.5
EST-1+2+6+10.5

Latin America spans 6 time zones.  Is it nearshoring or offshoring?  The answer may depend on where your internal developers are located.

If you want to maximize daily real-time collaboration, consider which time zone your teams will be in, rather than focusing on a Nearshore or Offshore label.  Keep in mind, Offshore Development Partners are often willing to skew their working hours to maximize collaboration overlap with your team. 

Integrating a remote software development team into your Agile workflow is difficult with less than 2-3 hours a day minimum workday overlap. Make this your minimum requirement when choosing a partner.

Size of Talent Pool

Mexico, along with several countries in Latin America have been transforming their tech capabilities at an impressive rate.  Their future SW development capabilities seem very bright.  And indeed, you can find many talented developers today from these regions, and the near time zone benefits may be very attractive. 

If you are looking for specific technology SME’s, Eastern/Central Europe may provide attractive options. 

However, the size of the talent pool of Nearshoring regions is not comparable to India’s, and for some technologies, nearshore developers are not yet as experienced as their Eastern/Central European counterparts. If you need to scale a large team quickly, you may need to consider India.  And if you are looking for specific technology SME’s, Eastern/Central Europe may provide attractive options.  In this case, the nearshore vs offshore decision has more to do with the country in question.

Ability to Rapidly Scale

Most Latin American and Central/Eastern European SW development partners will struggle to scale large teams rapidly.  Many simply do not have an adequate pool of available in-house developers, and there are not nearly as many developers in the “hiring pool” as in India.  Universities from these regions are churning out more engineers every year, and this problem may be resolved in the coming years, but the number of new tech graduates is not yet comparable to India.   

If you need to scale to a team of 50+ developers in 90 days, you might need to look towards countries like India.

The “sweet spot” for scaling Latin American and Central/Eastern European Software Development Teams seems to be 5-20 developers within 60-90 days. If you can provide a long-term scaling plan,  larger teams are definitely possible.

Technical English Proficiency

When evaluating Nearshore vs Offshore, you should consider regional differences in Technical English Proficiencies that exist between countries.  Within Nearshoring regions, Mexico outpaces Latin America, owing to proximity to the US and a University system that promotes not only English proficiency but also low-context communication.

Engineering colleges in Hungary teach in English, and students must pass English Proficiency tests to be admitted.

Within Europe, Central European countries tend to be more westernized and have superior technical English proficiency as compared to their Eastern European counterparts.  As an example, engineering colleges in Hungary teach in English and students must first pass English Proficiency tests to be admitted. 

India’s technical English proficiency is high, but cultural communication challenges do exist.

Communication Culture

English proficiency is only one aspect of communication.  Cross-cultural communication differences can and do cause a lot of problems with geographically-dispersed engineering teams. 

Cross-cultural communication challenges are one of the primary sources of outsourcing failure. This is due to low-context vs high context communication differences. 

Summary

We hope we have provided some valuable insights into your Nearshore vs Offshore comparison. As you can see, Nearshoring vs Offshoring isn’t a one-size-fits-all proposition. Look for regional strengths that match your most important requirements. And if you need additional information please visit our Services Page for details on each region covered in this article.


Nearshore vs Offshore Development FAQs

What is nearshore development?

Nearshore development is having services performed by a company from another country with similar time zones as your team. Mexico is an example of this for the USA.

What is offshore development?

Offshore development is having services performed by a company from another country that is typically far away with a large time zone difference. India is an example of this for the USA.


About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Our Architects, Developers, Data Scientists, and Data Engineers are extensively screened and have domain expertise in several industries, including Telecommunications, Financial Services, Insurtech, IoT, Logistics, Industrial Automation, etc.

Global Development Centers

Locations: Argentina, Mexico, Colombia, Peru

Top Rated In Latin America for English Proficiency

Maximum Time Zone Overlap with the USA

Engineering Culture Alignment

Location: Hungary

Top Rated in Central Europe for English Proficiency

Talented Engineering Pool

Strong Engineering Culture Alignment

Location: Cochin, India

Most Westernized Region of India

Very Low Turnover Rate in Region

Ability to Scale Large Teams Rapidly

Interested in Nearshore/Offshore Software Development?

If you’d like to experience “Agile at Speed”, we’d be happy to schedule a brief call to discuss how we can help you.

Communicate! (with Remote Software Development Teams)

With remote software development teams, you need to be excellent at communicating “asynchronously”, across time zones. Much of your communication will not happen in real-time, so you can’t afford to play “Question and Answer Ping Pong”.  Here are some proven best practices to follow as you address how to manage a remote development team.


Set Up a Communication Framework

It’s too easy to lose valuable information when team members are communicating across multiple platforms like Skype, WhatsApp, Slack, Email, Zoom chat, Google Hangouts, text etc.. 

As difficult as it might be, insist on limiting your technical communication channels to two or three

Email can be used for overall communication between managers and other stakeholders, but technical communication channels should be limited. 

Communication Framework for Remote Software Development Teams:

  • Jira or another project/issue tracking platform can be used for technical documentation and overall project information.  It can also be used to store communication protocol and process information. 
  • Slack could be used for instant communication and to post “group chat and notifications”.  This is particularly helpful if direct communication after hours is used to answer bottleneck questions.  Many delays can be avoided in this way.
  • Video chat platforms like Skype, Zoom or similar can be used for video meetings and to share screens for collaboration.   

Establish Clear Communication Protocols

Very early, establish communication protocols and expectations with your remote software development team.  These will cover items such as:

Daily Collaboration Schedules

Work schedules between remote and internal development teams should be structured to maximize the number of daily collaboration hours.  This is critical in the early stages but can be relaxed once the teams are up and running.  A minimum of 2 hours of daily workday overlap with your remote team is suggested, more if possible. 

Meeting Frequency

  • Scrum meeting times and frequency
    • Scrum Masters and relevant Team Leads from both teams
    • Daily at first, less frequently over time.
    • Keep recurring meetings at regularly scheduled times.
  • Monthly Technical Management meetings
    • Attended by Technical Managers from both teams
    • Track key KPIs and manage expectations
  • Quarterly Executive Meetings
    • Executive Engineering Management from both teams
    • Roadmap discussions and executive feedback.

Low-Context Communication Expectations and Standards

Set specific standards and expectations for low-context communication from your remote software development team.

 Avoid the “Question and Answer Ping Pong”

  • Each email and direct message should be precise, meaningful and thoughtful.  Short, vague emails usually trigger Q&A Ping Pong.
  • Coach your teams to write emails with as much information as possible.  Nothing should be left to interpretation. 
  • All team members should develop a habit of reviewing and proofreading emails and direct messages before sending.  The simple question to answer before hitting send is; “If I were the recipient of this message, would I understand it without sending another email to clarify something?”

Once your remote team understands the importance of low-context communication, these improvements become a matter of practice. 

After Hours Communication (for bottleneck questions)

Even with the best asynchronous communication between teams, it’s important to have the ability to get quick answers to bottleneck questions.  Without this in place, project schedules often suffer the “death of a thousand cuts”. 

Project schedules often suffer the “death of a thousand cuts”, waiting for answers to bottleneck questions.

We highly recommended that you get permission and cooperation from your offshore developers to periodically answer quick bottleneck questions sent to them after hours (on slack or another instant message platform).  Being available (only as needed) just a few hours before and after the remote team’s formal workday can save weeks in schedule.  A good remote team will see the value of this and agree.


Consider Developing a Glossary of Terms (Wiki)

Most teams use industry jargon without even realizing it.  In fact, even technical jargon deemed common in the US might be misunderstood by a remote developer.   It’s a good idea to develop a Glossary of Terms or Wiki to define any terms and definitions deemed relevant.  



About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Our Architects, Developers, Data Scientists, and Data Engineers are extensively screened and have domain expertise in several industries, including Telecommunications, Financial Services, Insurtech, IoT, Logistics, Industrial Automation, etc.

Developer Screening, Testing & Training

With the multitude of changes, dependencies & processes within Agile methodologies, a certain velocity of communication, interaction & concurrency is required. Our screening processes ensure our teams can deliver “Agile @ Speed” in your development workflow.

Technical English Proficiency

For Agile, “English At Speed” Is The Standard

Communication Excellence

Developers Trained For “Low-Context” Communication

Problem Solving Aptitude

Our Customers Expect Engineers

Code Testing

Code Quality

Code Reviews For All Developers


Interested in Agile Remote Development at Speed?

If you’d like to experience “Agile at Speed”, we’d be happy to schedule a brief call to discuss how we can help you.

The Secret to Agile with Offshore Teams

Agile Offshore Software Development

The secret to agile with offshore teams lies within the differences between low-context and high-context communication cultures. As many of you may have already experienced; focusing solely on developer skill and experience does not guarantee a successful offshoring experience. Agile offshore development performance requires “Agile at Speed”, which is difficult to accomplish without a certain velocity of communication. This article focuses on how this is accomplished.

“The biggest single problem with communication is the illusion that it has taken place.” 

-George Bernard Shaw

How appropriate this comment is for agile offshore development.  Language barriers and cultural communication differences can lead to misunderstandings that can hamper a development team’s throughput, foster mistrust between teams, and (worst case) contribute to disastrous consequences.  

But in the context of geographically dispersed, culturally diverse engineering teams, what do we mean by effective communication? We examine two important communication elements that are critical to Offshore Agile Software Development: Technical English Proficiency and Cross-Cultural Communication.


Agile Requires Technical English Proficiency

Effective communication clearly requires moderate English proficiency, at a minimum.  However, Conversational English Proficiency is quite different than Technical English Proficiency

There are many tests that can identify technical English proficiency, including the Common European Framework of Reference for Languages (CEFR), offered by Cambridge University’s Cambridge Assessment English (CAE) exam board. 

The Hidden Problem of Cross-Cultural Communication

The hidden problem of achieving agile with offshore teams is the difference between high-context and low-context communication cultures.”

Cross-cultural communication challenges are the root cause of many offshore/nearshore development failures.  This hidden problem causes mismatched expectations between customers and their offshore/nearshore vendors. 

Low-Context vs High-Context Communication

An excellent article detailing the cultural communication differences across the world, which summarizes Erin Meyer’s ground-breaking novel:  The Culture Map, can be found at Tech Tello.  A summary is provided below.

Low-Context vs High-Context Communication

Low-Context Communication Cultures

In low context communication cultures, effective communication must be concise, straightforward, and explicit for the message to be passed on successfully. 

“Low-context culture requires stating it as you mean it.”

The rule-of-thumb for low context communication is as follows: “Tell them what you are going to tell them, then tell them, then tell them what you’ve told them”

This process is designed to quickly identify and correct misunderstandings before they become problems.  It also serves to reduce pointless, time-consuming debates.

In low context cultures, communication occurs assuming that knowledge of each other’s histories and backgrounds is not present. Neither is most of the communication shaped by long-term relationships between speakers.

In low-context communication, the meaning of messages is more dependent on the words being spoken rather than on a shared understanding of subtle cues.

High-Context Communication Cultures

“High-context culture requires reading between the lines.”

In high-context communication cultures, effective communication is nuanced, layered, and sophisticated.  High-context cultures rely upon shared history and experiences to communicate. 

A high percentage of words can be interpreted in multiple ways, depending on how and when they are used.  This can be very effective and efficient if all members of a team have the same background, history and experiences to understand these subtleties. 

However, when this shared history and understanding is absent, as is the case when people from high-context cultures communicate with people from low-context cultures, the communication breaks down. 

In fact, the highest chance of miscommunication exists when two people from different high-context cultures try to communicate.  In this scenario, both parties try to communicate using nuances that neither is aware of. 


Communication Cultures by Country

Image source: Tech Tello

As you can see, the United States is the lowest context communication culture in the world, with most of the westernized countries leaning towards the left of the scale. 

All the countries that speak the Romance languages, including European countries like Italy, France, and Spain, as well as Latin American countries fall in the middle of the scale, while Asian and African countries are solidly on the right of the scale. 

Japan is the highest context communication culture in the world. 

Clearly, excellent software engineering can be found along the entire low/high context continuum.  This scale is not meant to measure communication effectiveness.  It merely points out how well people from two cultures can relate to one another. 


The Secret to Agile With Offshore Teams

If clear communication is the goal, multi-cultural teams need low-context communication and processes.

What matters isn’t where either culture falls on the scale, but rather the relative position of the two cultures that must work together, in this case on Software Development.  The larger the gap, the harder it will be to communicate with your Software Development Partner, on average. 

When US-based companies with low context cultures work with SW Development partners from high-context cultures, there is often little basis (context) for shared non-verbal, nuanced communication, causing frequent miscommunication. 

Agile development programs will suffer if half your team is communicating non-verbally, with assumed shared understanding, and the other half is not.  Even moderate time-zone differences between geographically dispersed teams can magnify this problem. 


Selecting an Agile Offshore Software Development Team with Low-Context Communication

The easiest way to ensure low-context communication is to pick a development partner from a low-context communication culture. 

But you may have other reasons to outsource from a country that is not low-context. In this case, pick a Remote Development Team that:

  • Understands the importance of crisp, clear, low-context communication
  • Has put in place the proper screening, testing, and training to ensure low-context communication
  • Understands it is the responsibility of the offshore/nearshore vendor to adopt a low-context communication style to aid in communication.

In our opinion, it’s the responsibility of the offshore/nearshore vendor to adapt to their US-based customers’ communication culture.  It’s not about who’s right or wrong, but rather about workability.   The high/low context communication combination usually does not work unless low context processes and communication are established.  This is especially true in Agile Development Environments.



About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Our Architects, Developers, Data Scientists, and Data Engineers are extensively screened and have domain expertise in several industries, including Telecommunications, Financial Services, Insurtech, IoT, Logistics, Industrial Automation, etc.


Developer Screening, Testing & Training

With the multitude of changes, dependencies & processes within Agile methodologies, a certain velocity of communication, interaction & concurrency is required. Our screening processes ensure our teams can deliver “Agile @ Speed” in your development workflow.

Technical English Proficiency

For Agile, “English At Speed” Is The Standard

Communication Excellence

Developers Trained For “Low-Context” Communication

Problem Solving Aptitude

Our Customers Expect Engineers

Code Testing

Code Quality

Code Reviews For All Developers


Need Agile Offshore Development?

If you need “Agile at Speed”, send us a message using the form below to schedule a meeting to review how we can assist with your offshore software development efforts. Thank you.

How Can a Remote Developer Team Thrive in Your Agile Environment?

Some Remote Developer Teams struggle in agile environments, while others seem to thrive. But how can you predict which teams are more likely to succeed? Identifying any “Agile Gaps” within a prospective remote development team upfront is critical to your dev team scaling efforts, especially if you can’t afford an “Offshore Vendor False Start”. 

Remote Developer Team


Table of Contents


Beware of the “Waterfall Trap”

As early as possible, you need to determine whether your prospective remote development team can thrive in your Agile environment (with its multitude of changes, dependencies, and processes), or if they are limited to Waterfall (with its clearly defined tasks and clear boundaries between development teams).  

remote software development teams

This “Waterfall Trap” has bitten many unsuspecting Engineering Managers as they scale their remote teams, only to be disappointed with the results.  Keep reading to discover the root causes of remote developer team failure and how to prevent falling into the “Waterfall Trap”

“Agile At Speed” Should Be the Standard

“In the time it took me to explain things, I could have coded it myself.”


-Typical complaint about remote developers

US customers usually expect their offshore dev partners to be an extension to their internal agile team. With the multitude of changes, dependencies, and processes within Agile methodologies, a certain velocity of communication, interaction, and concurrency is required. 

This “Agile at Speed” is difficult to achieve unless your offshore dev team’s communication culture matches yours.  Without it, project schedules will slip, and your offshore partner will be a source of inefficiencies, causing a rippling effect across your entire dev team. 

Effective Cross-Cultural Communication

An excellent article detailing the cultural communication differences across the world, which summarizes Erin Meyer’s ground-breaking novel:  The Culture Map, can be found at Tech Tello.  A brief summary is provided here.

high context vs low context communication
Image Source: Tech Tello

Low-Context Communication Cultures

“Low-context culture requires stating it as you mean it.”

In low context communication cultures, effective communication must be concise, straightforward, and explicit for the message to be passed on successfully. 

The rule-of-thumb for low context communication is as follows: “Tell them what you are going to tell them, then tell them, then tell them what you’ve told them”

This process is designed to quickly identify and correct misunderstandings before they become problems.  It also serves to reduce pointless, time-consuming debates.

In low context cultures, communication occurs assuming that knowledge of each other’s histories and backgrounds is not present. Neither is most of the communication shaped by long-term relationships between speakers. In low-context communication, the meaning of messages is more dependent on the words being spoken rather than on a shared understanding of subtle cues.

High-Context Communication Cultures

“High-context culture requires reading between the lines.”

In high-context communication cultures, effective communication is nuanced, layered, and sophisticated.  High-context cultures rely upon shared history and experiences to communicate.  A high percentage of words can be interpreted in multiple ways, depending on how and when they are used.  This can be very effective and efficient if all members of a team have the same background, history and experiences to understand these subtleties. 

However, when this shared history and understanding is absent, as is the case when people from high-context cultures communicate with people from low-context cultures, the communication breaks down.  In fact, the highest chance of miscommunication exists when two people from different high-context cultures try to communicate.  In this scenario, both parties try to communicate using nuances that neither is aware of. 

Communication Cultures by Country

Image source: Tech Tello

As you can see, the United States is the lowest context communication culture in the world, with most of the westernized countries leaning towards the left of the scale. 

All the countries that speak the Romance languages, including European countries like Italy, France, and Spain, as well as Latin American countries fall in the middle of the scale, while Asian and African countries are solidly on the right of the scale.  Japan is the highest context communication culture in the world. 

Clearly, excellent software engineering can be found along the entire low/high context continuum.  This scale is not meant to measure communication effectiveness.  It merely points out how well people from two cultures can relate to one another. 

The Secret to Agile With a Remote Developer Team

If clear communication is the goal, multi-cultural teams need low-context communication & processes.

What matters isn’t where either culture falls on the scale, but rather the relative position of the two cultures that must work together, in this case on Software Development.  The larger the gap, the harder it will be to communicate with your Software Development Partner, on average. 

When US-based companies with low context cultures work with SW Development partners from high-context cultures, there is often little basis (context) for shared non-verbal, nuanced communication, causing frequent miscommunication. 

Agile development programs will suffer if half your team is communicating non-verbally, with assumed shared understanding, and the other half is not.  Even moderate time-zone differences between geographically dispersed teams can magnify this problem. 

Read our full article The Secret to Agile with Offshore Software Development Teams here.

How to Ensure Low-Context Communication When Selecting a Remote Development Team

The easiest way to ensure low-context communication is to pick a development partner from a low-context communication culture. 

But you may have other reasons to outsource from a country that is not low-context. In this case, pick a Remote Development Team who:

  • Understands the importance of crisp, clear, low-context communication
  • Has put in place the proper screening, testing, and training to ensure low-context communication, and
  • Understands that it is the responsibility of the offshore/nearshore vendor to adapt a low-context communication style to aid in communication.

In our opinion, it’s the responsibility of the offshore/nearshore vendor to adapt to their US-based customers’ communication culture. 

It’s not about who’s right and wrong, but rather about workability. The high/low context communication combination usually does not work unless low context processes and communication are established.  This is especially true in Agile Development Environments.


About Cloud App Developers, LLC

With a team of 1,500+ Engineers from 6 countries, we provide multiple options to optimize what’s most important to our customers. Need to scale a large team rapidly? Need same time-zone development? Looking for particular tech skills? We have you covered.

We also offer IT Staff Augmentation Services for Software Development and Data Science Engineers.

Our Architects, Developers, Data Scientists, and Data Engineers are extensively screened and have domain expertise in several industries, including Telecommunications, Financial Services, Insurtech, IoT, Logistics, Industrial Automation, etc.

Developer Screening, Testing & Training

With the multitude of changes, dependencies & processes within Agile methodologies, a certain velocity of communication, interaction & concurrency is required. Our screening processes ensure our teams can deliver “Agile @ Speed” in your development workflow.

Technical English Proficiency

For Agile, “English At Speed” Is The Standard

Communication Excellence

Developers Trained For “Low-Context” Communication

Problem Solving Aptitude

Our Customers Expect Engineers

Code Testing

Code Quality

Code Reviews For All Developers


Interested in Agile Remote Development at Speed?

If you’d like to experience “Agile at Speed”, we’d be happy to schedule a brief call to discuss how we can help you.

7 Reasons to Outsource Software Development to Hungary in 2022

Budapest, Hungary

While Poland & Ukraine have historically been the most popular software development outsourcing destinations in Central & Eastern Europe, Hungary is emerging as one of the best countries to outsource software development. Quietly, Hungary’s talent pool has been steadily increasing over the past few decades. Compared to Eastern Europe software development outsourcing, Hungary also offers the westernized culture of a Central European country. This has attracted many US companies to establish Hungary as their primary offshore software development destination.

Hungary: The Alternative to Software Development Outsourcing in Eastern Europe

Recent regional instabilities in the Eastern European region have highlighted the need for managing political risks when outsourcing to Eastern Europe Software Development Companies. As a member of the EU & NATO, Hungary provides the stability to safely invest in an offshore development team.

Why Hungary Has Become a Top Offshore Software Development Destination

  1. Tech Talent Ranked 3rd in all of Europe
  2. Largest IT Talent Pool in Central Europe
  3. Developers are Fluent in English (Ranked 2nd in Central & Eastern Europe)
  4. Hungary a Member of the EU & NATO
  5. Labor Laws Comply with European Standards
  6. Low-Context Communication Culture Matches Western Countries
  7. Low Attrition Rates for Developers

Hungary’s Tech Talent Ranking

Hungary ranks near the top of all European countries for developer skills, according to a 2021 HackerRank study.

RANKCOUNTRYDEVELOPER TECH SCORE
1Slovakia100
2Czech Republic93.38
3Hungary92.86
4Poland92.74
5Serbia92.59
6Ireland92.27
7Ukraine91.26
8Switzerland91.08
9Germany90.70
10Austria90.15

Hungary Has a Large Developer Pool

Hungary has been steadily building its IT Talent Pool over the past few decades & now has the largest number of developers in all of Central & Eastern Europe. Here are the top 5:

RANKCOUNTRYPOPULATION# DEVELOPERS
1Hungary10M155,000
2Czech Republic10.5M150,000
3Poland38M140,000
4Romania19.5M125,000
5Ukraine42M115,000
Developer Talent Pool

All Engineering Classes in Hungary are Taught in English

Agile requires Technical English Proficiency. If you are looking for developers who can communicate complex technical concepts in English “at speed”, then Hungary should be at the top of your list.

All engineering classes in Hungary are taught in English & students must pass the IELTS test in order to enter Engineering School. According to EF EPI, the preeminent English Proficiency rating body in the world, Hungary’s English proficiency is rated as “Very High”.

Hungary holds EU & NATO Membership

Often overlooked when choosing a Central or Eastern European development partner is the political stability of the country. Being a member of both the EU & NATO, Hungary is considered a stable outsourcing destination.

Hungary Has Favorable Euro-Centric Labor Laws

Bureaucratic labor laws can make it difficult to do business in some Eastern & Central European countries. Hungary’s labor laws are in line with European Union standards.

Hungary has a Low-Context Communication Culture

In her groundbreaking book, “The Culture Map”, Erin Meyer outlines the importance of having a low-context communication culture when blending multi-cultural, geographically dispersed teams. Hungary has a similar “low-context communication culture” to North America & Europe, which makes communication efficient & crisp.

Hungary Has Low Developer Turnover Rates

Hungarian software development companies have developer turnover rates below 10%, far below most Central & Eastern European countries. Compare that to 22% turnover rate in India & 12% in Latin America.

Conclusion

Once you realize the rich developer talent in Hungary & consider all of the other important variables for choosing an offshore software development destination, the decision is clear. Hungary belongs on every tech manager’s list of potential outsourcing destinations.



About Cloud App Developers, LLC

Cloud App Developers, LLC offers Software Staff Augmentation & Custom Software Development Services from Hungary, Latin America & India. Our Offshoring & Nearshoring Software Development is built upon a foundation of English & Communication Excellence. Visit our About Us page for more information.


Our Services

Dedicated Software Development Teams

Dedicated Software Development Team

An Autonomous team assembled to deliver technology solutions.

Managed by Customer Product Owner & our Scrum Master.

Software Development Staff Augmentation

IT Staff Augmentation

Augment your internal team with developers you don’t have to babysit.

Top developers with English & Communication Excellence.

Custom Software Development

Custom Software Development

End-to-End Custom Software Development, built to your specifications.


For More Information on Outsourcing SW Development to Hungary, please complete form below. Thank you.

The Difference Between Data Cleansing and Data Transformation

There is a lot of confusion about the difference between data cleansing (also known as data screening) and data transformation. Both are important to any data analysis, whether or not machine learning is to be used. In this article, we break down the difference between data cleansing and data transformation.

Data Transformation

What is Data Cleansing and Why is it Important?

Data Cleansing, also known as data cleaning or data screening, is the process of preparing data for analysis, statistical modeling, or machine learning algorithms. This is done by deleting or modifying incomplete, incorrect, irrelevant, or inconsistent data. Data cleaning addresses factors such as outliers, noise, missing data, inconsistency, relevance and redundancy. This process is critical for ensuring consistent data quality and realizing accurate, actionable insights. “Garbage in, garbage out”, as they say.

What is Data Transformation?

Data Transformation is the process of transforming your data into a usable format, optimized for your specific business goals. If you plan to use statistical modeling or machine learning algorithms, the type of data transformation needed will vary. If data is to be fed into machine learning algorithms, it will also involve adding “features” in a process called “feature engineering”. 

Data cleansing and data transformation work hand-in-hand and are absolutely essential to any data pipeline. For a detailed article on Data Transformation, see our blog article: Data Transformation: An Executive’s Guide to Affordable AI.

Data Screening and Transformation Process

Once the business goals have been identified, data cleansing is just one of the stages in the data screening and transformation process shown below.

This process leverages both SEMMA and CRISP-DM principles and is detailed below.

Steps In Data Transformation

Data Fusion

Data usually comes from multiple sources, which makes it difficult to analyze without transforming the data into a homogenous format.  This is known as syntactic heterogeneity

When data sets for the same domain are developed by different people, this can lead to differences in meaning and interpretation. This is known as semantic heterogeneity

Various techniques to address both types of heterogeneity can be used for data fusion.

Data Quality Assessment

Establishing quality criteria for your data involves considering outliers, noise, inconsistency, incompleteness and redundancy within data sets. 

Outliers are results that deviate so far from the rest of the data that its validity is in question. They should be eliminated to avoid skewing the results.

Noise is meaningless data that is irrelevant to the analysis being performed. These data can hinder machine learning and should also be eliminated.

Inconsistency means the data contradicts itself somehow due to mislabeling or mistakes. 

Incompleteness is data with missing values that must be eliminated or filled in by various statistical methods. 

Redundancy is when there are multiple copies of the same data in databases, which can skew results.

Data Selection

Data selection determines what data is used for analysis or modeling and includes an assessment of the data relevance. Lots of data is eliminated in this phase to prevent costly data cleansing and/or feature engineering on irrelevant data.    

Data Cleansing

Data quality is improved, based on criteria set forth in the quality assessment phase. Issues related to outlier, noise, inconsistency, incompleteness and redundancy are fixed. 

Feature Engineering

Feature Engineering uses domain knowledge of the data to extract the most useful features used by algorithms. These are fed into machine learning algorithms to supercharge performance. Feature engineering consists of the creation, transformation, extraction and selection of features from raw data.

Data Screening and Transformation are important steps to ensure the data used for analysis or modeling will produce the most accurate and actionable insights for business or operational decision-making. 


Accelerating your AI and Machine Learning Initiatives

Why dive into an AI or Machine Learning program before making sure you can get the ROI you need?

If you would like to kick-start your Artificial Intelligence or Machine Learning initiative, Cloud App Developers, LLC has created a valuable offering in our Machine Learning Proof of Concept.

Who Would Benefit?

Those who:

  • Have lots of legacy data and want to launch an AI or Machine Learning Program, but don’t know where to start.
  • Need to validate if business goals are achievable through data science, Machine Learning or Deep Learning. 
  • Want to know what is possible with AI and Machine Learning.

What does the program include?

The Cloud App Developers’ Machine Learning Proof of Concept is designed to provide a useful assessment of your data, validate your business goals against available data and models, and to help identify other business goals that might be possible through Machine Learning. Our Machine Learning Consultants will then compile a comprehensive report and present the findings to your stakeholders. A typical program would include the following stages:

Accelerator Program Stages                                       Questions Answered

Review of your Business Goals“What are the business goals you hope to address with AI or Machine Learning?
Assessment of existing data“What is the nature and quality of your existing data?” “Is there any missing data?” “What data preparation is needed?”
Top-Level Validation of Business Goals“Can your existing data support your Business Goals?” “What other goals might be achievable?”
ML Model Recommendations“Which ML Algorithms or Analytical Models are best suited to meet my business goals?”
Data Transformation of subset of data (see below)“How much will it cost to prepare all of my data for modeling?”
Run Models against one business goal and a subset of data“How do I validate how AI and Machine Learning can help me?” “Can I accomplish some goals with Analytical Modeling or do I need sophisticated ML Modeling?”
Generate ML Report with RecommendationsFull Report on Machine Learning Readiness and Goal Validation Includes recommendation and cost estimates for full Data Transformation and ML Program.

Visit our Software Solutioneer Blog for more articles.

Legacy Banking Application Modernization

The future of fintech involves wholesale application modernization of legacy banking platforms to modern Microservices architectures. Myriad banks & financial institutions are modernizing their monolithic architecture to accelerate fintech innovation, seeking benefits such as reduced payment latency and streamlined regulatory compliance. Competition from platform banking innovators is forcing established banks to adapt quickly.  In this article we will examine a 3 Tier roadmap for migrating from monolith to microservices,  incorporating both a service mesh and API Gateway into the architecture. 

API Gateway
Figure 1, Monolith to Microservices: 3-Tier Roadmap

Challenges Facing Banks

Clearly, not all banks are facing the same set of innovation challenges. For some, the starting point is a modern services-based core that can be more readily modernized to offer platform banking services, perhaps with a big-bang approach. Other banks with legacy monolithic application architectures will need to modernize in a more measured fashion, refactoring their core application over time & piece-by-piece. 

In Part 2 of our Microservices SeriesCloud Migration Strategy: Monolith to Microservices we outlined several application modernization & migration strategies to phase out parts of monolithic legacy apps, as microservices are added piece-by-piece. 

Regardless of which approach is taken, financial institutions with legacy monolithic cores will eventually need to re-engineer their core banking architecture to keep up with fast-paced platform banking trends. We offer a 3-tiered roadmap for migrating legacy applications from monolithic to microservices. This roadmap includes incorporating a service mesh, API gateway & eventual legacy core modernization.  (See Figure 1 below.)

Monlolith to Microservices

A transition from monolithic to microservices has its challenges, even when a phased approach is taken. Managing the increased operational overhead and escalating complexity during the transition is critical. We offer several strategies to help manage this chaotic transition. 

By adopting the proper strategy, banks can start offering some leading platform banking products & services almost immediately, even those with monolithic legacy platforms. Key to this strategy is the addition of a Service Mesh, combined with an API Gateway. 

Service Mesh: Near-Term Solution

Although microservices are perfect for most banking applications, there are challenges at scale. By deploying a service mesh early in the application modernization process, dev teams can address increasingly complex communication between services, a strategy that pays off later as the architecture scales. 

What is a service mesh? A service mesh is a configurable, low-latency infrastructure layer that manages the high-volume of communication between microservices.  In microservices, one service must request data from many other services. As microservices scale, this can become a challenge to manage. A properly designed service mesh architecture automatically routes requests between services & optimizes the interactions.   

Why Service Mesh?

As the complexity of a microservices architecture increases, the root cause of problems can be difficult to pinpoint. A service mesh enhances problem identification & mitigation. Furthermore, service meshes measure service-to-service communication quality, so rules for effective communication between microservices can be established & proliferated throughout the platform. This increases efficiency & reliability of the entire platform. 

Service meshes also allow multiple software development teams to work in the same infrastructure more independently. Perhaps the biggest downfall of microservices architectures is the continuous need to integrate with many other microservices even when the simplest features are introduced. Service meshes solve this issue by providing a standard format for the communication infrastructure, so developers don’t have to worry about these tedious integration tasks. The code ends up being simplified as well. In a large financial company where there might be dozens (or hundreds) of developers, this advantage is significant.

Service Mesh Implementations

There are several implementations of a service mesh. The most common involves a sidecar proxy attached to each microservice, which serves as a contact point. Service requests simplify the data path between microservices. (See Figure 2 below.)

Service Mesh - sidecar proxy
Service Mesh – sidecar proxy

You may ask, “What about my Kubernetes Service Mesh?”  To be sure, container orchestration platforms like Kubernetes offer basic management capabilities that are more than adequate for some applications. In a way, they offer primitive service meshes. However, a more robust service mesh in addition to Kubernetes’ services extends these capabilities & offers additional functionality, such as management of security policies & load balancing, which are critical for complex banking/fintech applications. 

API Gateway: Added for Innovation Speed & Security

The combination of an API gateway with a service mesh can provide a powerful blend of speed, security, agility & manageability. As microservices scale, the number of endpoints keep increasing & each endpoint needs to be secured. By using an API gateway, a security proxy level is created allowing threat detection before your applications & data are penetrated. In addition, APIs can be exposed to external partners & developers to enable accelerated development of services. 

This does not solve the inherent scalability problem of a legacy monolithic core architecture, but new services & features can be added by internal & external teams using a service mesh & API Gateway. Most importantly, platform banking features can be developed & deployed while still relying on a legacy core, until the timing is right for the complete legacy core modernization. 

Keep in mind; if the communication infrastructure is built in a way that every public request must go through the API gateway, you will need to specify these rules. This could pose a serious bottleneck, so the communication must be fluent between your various teams. Most importantly, the team responsible for creating these API rules must be scaled along with the developers introducing new features to the architecture, otherwise it’s chaos. Resource planning and PM teams need to live up to the task.

Microservices Core- A Future Necessity

The main goal of converting the banking core application to a microservices architecture is to offer leading edge services to customers. For this to happen, the speed & agility of microservices is needed. 

Banks may also wish to offer services from third parties, rather than re-invent the wheel for each new service. Although some of this can be done with the “near-term architecture” outlined in this article, there are limitations that may become severe. 

Fintech innovation from startups, along with ever-increasing customer expectations, means established financial services players will adapt & change the way they do business with their customers. Delivering on these new requirements will be most difficult with most legacy systems. In the long-run, banks will likely move to a next-generation microservices based core platform in coordination with a service mesh + API Gateway.


Cloud App Developers, LLC offers Legacy Application Modernization Services.  We are Microservices Experts with a mastery of Microservices Design Patterns. To assist in this effort, we also have domain experts in fintech, telecom, insurtech and many other industries.  To learn more about our Microservices Expertise, visit Cloud App Developers, LLC or contact wes@cloudappdevelopers.com

Copyright © 2021 Cloud App Developers, LLC. All Rights Reserved.

Data Transformation – An Executive’s Guide to Affordable AI

Don’t Spend A Fortune To Extract Huge Value From Your Data

Data Transformation can help unlock this value while avoiding the high costs of developing artificial intelligence. By focusing on critical steps in data transformation you can enable powerful data science and machine learning capabilities that will address many business objectives.

Data Transformation
Data Transformation

You can’t walk before you crawl, and you can’t run before you walk. Even if you plan to eventually develop a robust Artificial Intelligence program, the first step on this journey is data transformation of your legacy data. You will be surprised at how much value you can extract along the way.


What is Data Transformation?

Data transformation is the process of transforming your data into a usable format, optimized for your specific business goals. Whether you plan to use data science or machine learning algorithms, the type of data transformation needed will vary. By following these steps, you can transform your data for any type of analysis or modeling you wish to perform.

Steps in Data Transformation

Whether you are planning to use simple statistical modeling or machine learning algorithms, the first step is transforming your existing data into formats that are easily ingested by either type of model. The steps in data transformation are detailed in Fig. 2 below.

Steps in Data Transformation
Fig. 2:  Steps in Data Transformation

Proper data fusion, quality assessment, data selection, and data cleansing will enable powerful business insights, even without machine learning algorithms.

Some of your models may rely upon statistical modeling techniques that do not require feature engineering. However, should you choose to develop and/or deploy machine learning algorithms, expert feature engineering will make them sing!


Feature Engineering – the “Secret Sauce” of Data Transformation

High-performing machine learning algorithms are not possible without proper data preparation and expert feature engineering.  Feature Engineering is the “Secret Sauce” that turns average machine learning (ML) algorithms into High-Performance ML Algorithms.

Feature Engineering is typically a collaboration between two parties:

  • Domain experts who have a deep understanding of the data itself, and
  • Machine learning engineers who are experts at choosing and optimizing machine learning algorithms. 

These two roles are equally important for the success of any machine learning initiative.

What is Feature Engineering?

Feature Engineering uses domain knowledge of the data to extract the most useful features used by algorithms. These are then fed into machine learning algorithms to supercharge performance.  Feature engineering consists of the creation, transformation, extraction, and selection of features from raw data.

Feature engineering uses various data optimization techniques to enhance algorithm performance.

For example, feature engineering might remove irrelevant features, and prioritize the features that are most useful to the models.  The amount of data can also be reduced to a more manageable amount through feature extraction techniques.  All of these are necessary elements of a high-performance machine learning initiative.

Feature Engineering, Data Transformation and Model Selection

Machine Learning Pipeline
Figure 3, Machine Learning Pipeline, Image Source: Oreilly.com

As seen in Fig. 3, features and models work together to produce high-performing machine learning results. Selecting the right algorithms is only half the battle.  In high-performing machine learning programs, model selection and feature engineering complement each other.  Bad feature and/or model selection combinations can negatively affect machine learning performance.  And you can’t have proper feature engineering without data transformation.  It’s all tied together.


Data Transformation Unlocks Affordable Alternatives to A.I.

For companies without huge AI budgets, there are alternatives. Knowing the differences and limitations of Machine Learning and Data Science is key to choosing the right strategy.

Despite tremendous upside and hype, the dirty little secret is that full-blown Artificial Intelligence programs can be extremely costly. Also, they can take years to develop. A.I. requires layers of deep learning algorithms to build the necessary neural networks. This takes a lot of money, time, and effort.  Artificial Intelligence Consulting often fails to take this into consideration. 

For the purposes of this article, we will focus primarily on Data Science and Machine Learning strategies. We will avoid the more costly deep learning solutions required for A.I.  The importance of Data Transformation, as well as Feature Engineering, will be highlighted.

Data Science vs Machine Learning

What is the difference between data science and machine learning and why should you care?

As you kickstart your machine learning program, why not take advantage of the significant benefits of advanced analytic techniques that do not require machine learning algorithms? After all, it will take some time to optimize your machine learning algorithms anyway. A proper data transformation initiative will prepare your data for whichever type of analysis you want to perform.

What is Data Science?

Data Science uses statistical approaches and advanced analytics techniques to extract useful insights from data.  Usually in response to specific requirements from business executives, Data Science uses data analytics, mathematics, and statistics to extract those specific insights. 

Data Science techniques form the core of business intelligence systems that rely partly on humans to spot trends in spreadsheets, charts or graphs. 

Not very sexy, but don’t discount their value.  Even today, companies rely on such methods to drive significant business value, often without machine learning.  Data science case studies can be found to address many important business objectives.

For some of your business objectives, a data science-based business intelligence system may be all you need.  To aid in decision making, Data Analytics Consulting may be helpful to visualize and present the data to stakeholders in your organization.

What is Machine Learning and When Should You Invest in it?

Simply put, Machine Learning is when machines can identify patterns in legacy data and then use those patterns to generate insights or predictions whenever new data is introduced into the machine learning system. 

To decide when to start investing in machine learning, it is helpful to understand the limitations of data science-based Business Intelligence Systems. 

As companies store data in larger quantities, and from more sources, with varying quality levels, data science-based Business Intelligence Systems fail. This is because of the “4 Vs” associated with Big Data:  Volume, Variety, Velocity, and Veracity of data.  At some point, relying upon humans to deal with the 4 Vs becomes untenable.  That’s where introducing Machine Learning begins to make sense. Machines are simply better at handling “Big Data”.


Why Machine Learning?

Machines are far better at dealing with large data sets with disparate sources and varying quality levels. 

A plethora of machine learning algorithms have been developed to handle classification, regression, and clustering tasks for these data sets.    Also, not all business objectives can be accomplished with data science techniques alone. In particular, Unsupervised Learning and Reinforcement Learning algorithms enable powerful insights not possible with standard data science approaches. See Figure 1 below: Data Science vs Machine Learning vs Deep Learning

Data Science vs Machine Learning vs Deep Learning
Figure 1: Data Science vs Machine Learning vs Deep Learning


Accelerating your AI and Machine Learning Initiatives

Why dive into an AI or Machine Learning program before making sure you can get the ROI you need?

If you would like to kick-start your Artificial Intelligence or Machine Learning initiative, Cloud App Developers has created a valuable offering in our Machine Learning Proof of Concept

Who Would Benefit?

Companies would benefit if they:

  • Have lots of legacy data that want to launch an A.I. or Machine Learning Program but don’t know where to start.
  • Need to validate whether if business goals are achievable through data science, Machine Learning or Deep Learning. 
  • Want to find out what else is possible with A.I. and Machine Learning.

What does the program include?

Cloud App Developers’ Machine Learning Proof of Concept is designed to provide a useful assessment of your data, validate your business goals against available data and models, and to identify any other business goals that might be possible through AI or Machine Learning.  Our Machine Learning Consultants will then compile a comprehensive report and present the findings to your stakeholders. A typical program would include the following stages:

Accelerator Program Stages                                       Questions Answered

Review of your Business Goals“What are the business goals you hope to address with A.I. or Machine Learning?
Assessment of existing data“What is the nature and quality of your existing data?”  “Is there any missing data?”  “What data preparation is needed?”
Top-Level Validation of Business Goals“Can your existing data support your Business Goals?” “What other goals might be achievable?”
ML Model Recommendations“Which ML Algorithms or Analytical Models are best suited to meet my business goals?”
Data Transformation of subset of data (see below)“How much will it cost to prepare all of my data for modeling?”
Run Models against one business goal and a subset of data“How do I validate how AI and Machine Learning can help me?” “Can I accomplish some goals with Analytical Modeling or do I need sophisticated ML Modeling?”
Generate ML Report with RecommendationsFull Report on Machine Learning Readiness and Goal Validation Includes recommendation and cost estimates for full Data Transformation and ML Program.


Book a Free Consultation! > Message our Data Scientists to learn more

What is Data Transformation?

Data transformation is the process of transforming your data into a usable format, optimized for your specific business goals. It works hand-in-hand with data cleansing to prepare data for machine learning and data analysis.

Steps in Data Transformation

Steps in Data Transformation include data fusion, data quality assessment, data selection, data cleansing, and feature engineering.

What is data science?

Data Science uses statistical approaches and advanced analytics techniques to extract useful insights from data. Data Science techniques form the core of business intelligence systems that rely partly on humans to spot trends in spreadsheets, charts, or graphs.

Blockchain and Insurance: Unlocking $300B in Value

Blockchain Insurance
Blockchain

Blockchain Insurance use cases are poised to unlock an estimated $300B in value, largely from machine learning and artificial intelligence (AI) applications.  Automated claims processing and fraud detection and prevention are two of the most popular implementations. However, secure access to data needs to be given to various third-party stakeholders for this value to be realized. This value could be transformative if companies can find a way to coordinate, cooperate and share data securely while complying with a growing list of government and industry regulations.

Insurance companies should view Blockchain as a cryptographically secure form of shared record-keeping capable of unlocking hundreds of billions of dollars in value. For example, some estimates indicate that securely sharing claims records between insurance companies could save the industry over $100B yearly in fraud prevention alone. Indeed, Blockchain has a promising future for the insurance industry across a variety of implementations.

Fraud Detection and Prevention
Fraud Detection and Prevention

Blockchain Insurance Use Cases

Enhanced Operational Efficiencies

Automated Claims Processing

Fraud Detection and Prevention

Regulatory Compliance (Data Privacy, Security)

Looking to Hire Blockchain App Developers

Blockchain and Insurance

Certainly, the insurance industry has been slow to react to these trends, but there is increased pressure from Insurtech innovators to adapt. Factors driving Blockchain adoption include:

  • For various reasons, the insurance industry continues to rely heavily on manual processes. Some estimates indicate that manual processes double costs in some insurance sectors. Blockchain promises to enable automation of many time-consuming manual processes.
  • Secure data sharing between insurance companies and other stakeholders could save the industry $300B a year in efficiency gains and fraud prevention alone. 
  • Transformative regulations are on the horizon that will force insurance companies to adapt.  Blockchain will be a major factor in complying with these regulations. Some European regulations mandate sharing of information through a secure API. It is expected that similar US regulations are on the horizon. 

Unlocking this tremendous value depends on digitizing legacy data and storing it in a secure system accessible by APIs. All new data must also be secure throughout the entire data processing chain. Through our Blockchain Consulting, Cloud App Developers, LLC offers creative solutions to real-world challenges within the insurance industry. 

Blockchain and Insurance

Blockchain Insurance Use Cases

Companies in Europe are being forced to comply with new government regulations requiring insurance data to be accessible to consumers (similar initiatives are on the horizon for US insurers). These consumers could be in the government or private sectors. Blockchain is a preferable solution, but devising a workable solution is not straightforward. Implementation is even more challenging because the requirements of each country in Europe can differ greatly, so the solution needs to be flexible and secure.  Our solution is to implement an API and infrastructure, so the registered insurance providers, or delivery companies, can push events to a Blockchain cluster synchronized to all nodes. If the Blockchain remains intact, one can assure consumers data extracted from the Blockchain is consistent.  We’ve conceived of an API that loads house insurance information in a general database that can be queried by appropriate parties on demand.  (See Figure 1)

Blockchain Insurance Use Cases
Figure 1 – Blockchain Case Study

How this solution could work:

How this solution could work:

  1. An insurance event is initiated.
  2. Given events processed based on type and metadata. Necessary data is aggregated.
  3. Event information is encrypted (event can be registration of a new insurance policy).
  4. Encrypted data is loaded into the local Blockchain node.
  5. Synchronization initiated on rest of blockchain nodes.
  6. Data is decrypted and transferred, gathering insights as necessary.
  7. Event data stored in central database, ready to be used.


Cloud App Developers offers Blockchain Consulting, and Blockchain Application Development across multiple industries. 

Looking to Hire Blockchain App Developers

For other Blockchain use cases, or to learn about insurtech data analytics, please visit Cloud App Developers or contact wes@cloudappdevelopers.com

Cloud Migration Strategy: Monolith to Microservices

If your application is “cloud-ready”, then cloud migration can be quite painless. However, this is not the case for migrating most legacy monolithic applications to the cloud.  Several cloud migration strategies have emerged to handle each type of scenario, with best practices evolving every day. Each Cloud Migration Strategy reviewed may not work for all Monolithic Applications, and cloud migration consulting may be needed to ensure proper planning.  The benefits of cloud migration are profound, but the costs can be high.  For some, the cost of not migrating to the cloud will prove to be even higher.  

The term “Legacy Application” conjures up visions of COBOL, C, or some other arcane programming language. Ironically, these legacy systems are often a business’s mission-critical apps and can be difficult to replace. For these companies, a cloud-native rewrite of their application is either too risky or impossible, however, several app modernization strategies can partially leverage the advantages of microservices and enable the integration of new technologies.

Cloud Migration Strategy
Cloud Migration Strategies

Cloud Migration Strategies

  1. Lift & Shift:  Also known as “Rehosting”, this can be a good option for migrating applications that are cloud-ready to some degree.   
  2. Lift, Tinker & Shift:  Making a few technology stack upgrades before migrating to the cloud (without changing the application’s core architecture) is also known as “replatforming”.  This can provide accelerated cloud migration and tangible cost savings.
  3. Partial Refactoring:  Partial refactoring is when specific portions of an application are modified to take advantage of the cloud platform.  This enables some of the new functionality of microservices without the cost & complexity of a complete refactor or rewrite.
  4. Complete Refactoring:  Short of a complete rebuild of your application in cloud-native formats, “refactoring” can be a viable option for moving significant functionality to the cloud.  A gradual approach is possible (and advised), as new microservices can be quickly tested without impacting the reliability of the existing monolithic application. You can use microservices to create new features through the legacy API as you refactor the legacy platform one piece at a time.  The least measured approach of these strategies, but still far less effort than a complete rewrite. 

Application Migration to Cloud: There is No Need to Hurry

Regardless of which application modernization technique you use, retiring parts of your legacy monolithic application can be done thoughtfully over time, making it easier to implement within your organization.  You can determine which parts of your application are easiest to refactor, and execute a little bit at a time. Also, the critical parts of your application not suitable for the cloud can be left on-premise and accessed through well-defined APIs.  Finally, you may decide to “retire” rarely used functionality to lower your total cost of ownership (TCO). 

The right approach for you will likely depend on several factors, including:

  • Cost & time constraints
  • How well-suited your application is to cloud migration (when not to modernize below)
  • Scalability requirements
  • Strong business need for adding functionality not possible with the existing application.
  • Agility requirements

Application Modernization: When Not To Do It

Not all applications are right for the cloud. This is especially true when you consider containerizing and service-enabling the applications. Below are a few guidelines:

  1. The more technical debt you have, the harder it will be to get your application “cloud-ready”. Containers and services leverage a specific set of microservices patterns, and it may be easier and cheaper to start anew if your application does not incorporate these patterns.  This is often the case with companies who have grown by acquisition, having stitched multiple platforms together with countless patches and complex APIs.
  2. Tightly coupled monolithic applications are typically a poor choice for cloud migration.  Decoupling the data from the application layer is required to benefit from Microservices and this often requires a rewrite of most of the application.  
  3. Modernizing outdated applications using old languages and databases may also be more trouble than they’re worth.  It may be cheaper and less risky to do a cloud-native rewrite in these instances. Although new tools are being developed to “easily modernize” these types of applications, proceed with caution as they have significant limitations you should consider.

Cloud Migration for monolithic applications can be daunting, but with the right strategy and thoughtful planning you can mitigate risks, make incremental improvements, and get upper-management support throughout the cloud migration journey. Rehosting, Replatforming and Refactoring are each viable options, depending on your situation. 



Cloud App Developers, LLC offers Cloud Migration Services, as well as Legacy Application Modernization.  We are Microservices Experts with a mastery of Microservices Design Patterns.  To learn more, visit Cloud App Developers, LLC or contact wes@cloudappdevelopers.com

Microservices Solution To The Monolithic Problem


Microservices are still the buzz in the software development world.  Why are so many companies migrating to microservices based architectures?  What is the Microservices Solution To The Monolithic Problem? We begin by analyzing the weaknesses and limitations of monolithic architectures.

Software components are tightly coupled inside monolithic architectures and changes to a single line of code can affect the entire application.  Minor system modifications can require re-deployment of the entire system and can turn small, incremental releases and bug-fixing into complex, time consuming efforts with manual testing of the entire application taking several weeks for each release.  Also, if a small part of the system with specific functionality needs scaling, you may need to scale the whole application.  Finally, as all your code lives in one place, the resource consumption of your most resource-hungry functionality drives up the total costs.  Peak load requirements for one function may be massive overkill for others, making the whole system much less efficient.  Cross-team coordination of these efforts is very challenging. 

In summary, the weaknesses of monolithic architectures include:

  • Difficult to innovate
  • Difficult (and expensive) to scale
  • Difficult to test
  • Low release velocity
  • Difficult to coordinate across teams

Microservices architectures solve these problems by breaking large applications down into small blocks of code that are segmented by specific areas of business logic (or application functionality). These blocks communicate through simplified APIs and look like a single application to end-users.

Typically, code blocks are stored separately, which means they can be created, deployed, tested and updated independently. If one block fails, a “known good” version can we swapped out to restore app functionality. This “hot swap” capability greatly enhances app stability during updates.

Because code is in smaller blocks, it is easier to predict failure scenarios and to create more comprehensive testing. Regression tests of changes is typically limited to a handful of function points, resulting in greatly improved release velocity (by as much as 90%). 

Microservices provides real flexibility, as myriad programming languages, databases, hardware and software environments can be used in the creation of your application.

In summary, the benefits of Microservices architectures include:

  • Easier deployment and maintenance
  • Increased release velocity
  • Increased application quality
  • Reduced downtime
  • Reduced cost at scale
  • Flexible tech stack and infrastructure

If you require a rapidly scalable, easily deployed, resilient application to compete in today’s dynamic application environment, Microservices may be the solution. Hybrid solutions (where you can use key blocks from your monolithic app) are also feasible if you need to take a more measured migration to microservices. Of course, there are challenges. Ultimately, the benefits are significant, especially at scale. We hope you have found this article, Microservices Solution To The Monolithic Problem, interesting and helpful. Our subject matter experts are happy to answer any questions you might have about realizing your Microservices vision.

Are Communication Skills More Important Than Technical Skills?


Are communication skills more important than technical skills? Having worked in Offshoring for 25+ years I can tell you a primary driver of success is the technical communication skills of customer-facing engineers.  English proficiency provides a foundation for this, but that’s only one part of what constitutes effective communication in a modern engineering context.   

In the “New Normal” of remote, geographically dispersed development teams, the need for effective communication is more important than ever.  This is especially true when outsourcing in the Offshoring / Nearshoring models, as it can be challenging to maintain Agile and DevOps processes and practices.

Development Managers, tasked with finding scarce development skills in a hot market, have increasingly turned to Offshoring and Nearshoring.  This strategy can pay off handsomely, provided English proficiency is a major consideration in your vendor selection process.  At a minimum, proficiency in communicative English is essential for engineers when interpreting technical information and creating solutions as a team. However, an effective Agile/DevOps culture requires even higher proficiency standards.  Below are a few suggestions for enabling effective technical communication across your outsourcing base. 


How to Ensure Strong Technical Communication from Nearshoring & Offshoring Partners

  •  Pick the right test

General English tests are considered insufficient for assessing global communication competency in an engineering context.  Assessment exams, like the Common European Framework of Reference for Languages (CEFR), offered by Cambridge University’s Cambridge Assessment English (CAE) exam board can be very useful in measuring technical communication skills.  CAE processes 5.5 million candidates per year and is recognized by 25,000 employers and institutions worldwide.  (See table below)

The Common European Framework divides learners into three broad divisions (A, B, C) that can be further divided into two levels.  For each level, it measures performance in reading, listening, speaking and writing. (Only levels B and C are shown, reflecting relevancy in an engineering context)

CEFR Framework Levels

Level groupLevelDescription
B
Independent user
B1
Threshold or intermediate
Can understand the main points of clear standard input on familiar matters regularly encountered in work, school, leisure, etc.Can deal with most situations likely to arise while travelling in an area where the language is spoken.Can produce simple connected text on topics that are familiar or of personal interest.Can describe experiences and events, dreams, hopes and ambitions and briefly give reasons and explanations for opinions and plans.
B2
Vantage or upper intermediate
Can understand the main ideas of complex text on both concrete and abstract topics, including technical discussions in their field of specialization.Can interact with a degree of fluency and spontaneity that makes regular interaction with native speakers quite possible without strain for either party.Can produce clear, detailed text on a wide range of subjects and explain a viewpoint on a topical issue giving the advantages and disadvantages of various options.
C
Proficient user
C1
Effective operational proficiency or advanced
Can understand a wide range of demanding, longer clauses, and recognize implicit meaning.Can express ideas fluently and spontaneously without much obvious searching for expressions.Can use language flexibly and effectively for social, academic and professional purposes.Can produce clear, well-structured, detailed text on complex subjects, showing controlled use of organizational patterns, connectors and cohesive devices.
C2
Mastery or proficiency
Can understand with ease virtually everything heard or read.Can summarize information from different spoken and written sources, reconstructing arguments and accounts in a coherent presentation.Can express themselves spontaneously, very fluently and precisely, differentiating finer shades of meaning even in the most complex situations.
  • Choose outsourcing partners from countries ranking high in English skills

If you plan to scale-up a team rapidly, it’s important to pick Nearshoring/Offshoring partners from countries ranking high in English skills, as it is easier for the partner to scale a team for you if the pool of engineers with English proficiency is larger.  EF EPI is an English Proficiency Index that covers 100+ countries and regions, based on over 2.2 million test results.  Here’s a snapshot of their current testing results:

  • Make your communication expectations clear with potential outsourcing partners

Sub-par communication is often thought of as a “necessary evil” when working with lower cost regions of the world.  This does not have to be the case.  Universities and development partners are beginning to realize the importance of building technical communication excellence into their organizations.  All potential development partners will claim they are good at communication.  Ask them to show you.  Pick a partner who demonstrates excellent technical communication skills during peer-to-peer meetings and calls as you evaluate them as a potential outsourcing partner.  Don’t settle for “the way things are”.

We hope these suggestions offer some useful guidance to improving your technical communication levels within your outsourcing base.

DevOps – A Super Brief Introduction

WHAT IS DevOps? DevOps practices enable multiple teams (Development, Operations, IT, Quality & Security) to better coordinate their efforts to produce superior products, faster. We hope you find this article, DevOps – A Super Brief Introduction, interesting and helpful.

BENEFITS OF DevOps? DevOps practices enable High-Performing Teams to increase customer satisfaction & facilitate:

  • Accelerated Time-to-Market
  • Improved Adaptation to Market & Competition
  • Maintenance of System Reliability
  • Improved Mean Time of Recovery

DevOps APPLICATION LIFECYCLE

DevOps has four phases; PLAN, DEVELOP, DELIVER & OPERATE. Each phase is involved with & relies upon the others to some extent.

PLAN

During the Plan Phase, DevOps teams define & describe the applications & systems to be built. Progress is tracked at low & high levels of granularity – from single-products tasks to tasks that span portfolios of multiple products. Planning methods include creating backlogs, bug tracking, agile software dev with Scrum, dashboards, etc.

DEVELOP

During the Develop Phase, the DevOps team writes, tests, reviews & integrate the code. This includes building the code into build artifacts usable in multiple environments. To increase the pace of development, the DevOps teams use a variety of productivity & automation tools & practice continuous integration using automated testing. 

DELIVER

Deploying applications into production environments in a consistent & reliable way is what makes DevOps so powerful. During the Deliver Phase, the DevOps team clearly defines the release management process, which include manual approval stages. Automation helps make these processes scalable, repeatable & controllable as the applications move between development phases.

OPERATE

During the Operate Phase, applications in production environments are maintained, monitored & troubleshot/diagnosed as necessary. By adopting DevOps, teams work to ensure high system reliability & availability while aiming to achieve zero downtime. DevOps teams endeavor to identify & mitigate issues before they affect the customer experience.


THE DevOps CULTURE

Cultivating a DevOps culture begins with the people within it. When organizations commit to DevOps they can create the opportunity for high-performing teams to develop, which enables automation & process optimization through technology.

DevOps PRACTICES

Real implementation of DevOps helps to accelerate, automate & improve specific practices during the application lifecycle.

CONTINUOUS INTEGRATION (CI) & CONTINUOUS DELIVERY (CD)

Configuration management tools help development teams roll out changes in a systematic, controlled fashion, which reduces the risk of system modification. These tools also enable dev teams to track system state & configuration drift. Practiced in conjunction with Infrastructure as Code (IaC), system definition & configuration are easily templated & automated.    

DevOps & THE CLOUD

The adoption of The Cloud has transformed the way development teams build, deploy & operate applications. By adopting DevOps practices, development teams have a great opportunity to improve & better serve their customers.

Cloud Agility

Development teams can quickly configure & provision cloud environments & gain great agility in deployment of apps. No longer do teams have to buy, configure & maintain physical servers. Teams can very quickly create complex cloud environments & shut down when no longer needed.

Kubernetes

More applications are using containers & Kubernetes is viewed as the industry solution for containers at scale. Automating the process of building & deploying containers with CI/CD pipelines & monitoring these containers are essential practices in the age of Kubernetes.

Serverless Computing

Moving the management of infrastructure to the cloud provider enables development teams to focus on their apps. Serverless computing can run apps without the need to configure & maintain servers, while reducing the risk & complexity of deployment & operations.


STARTING THE DevOps JOURNEY

At first, DevOps can be a bit overwhelming. A key to success is to start small & to learn from the experiences of others. The Cloud App Developers teams have a enjoyed a great deal of success in building & deploying cloud apps & we are happy to guide & assist you in your business / digital transformation.