Data Transformation- An Executive’s Guide to Affordable AI

Despite tremendous upside and hype, the dirty little secret is that full-blown Artificial Intelligence Digital Transformation, incorporating deep learning and/or neural networks can be too costly for most companies to justify.  Artificial Intelligence Consulting often fails to take this into consideration. 

For companies without huge AI budgets, knowing the differences and limitations of Machine Learning vs AI vs Data Science is key to choosing the right strategy. A robust Data Transformation initiative could be the catalyst to unlocking tremendous value while avoiding the aforementioned high costs.  By adopting data transformation best practices and incorporating sound feature engineering techniques, companies can generate surprisingly high business value from their data.  Much of this value is already locked in companies’ legacy data, awaiting effective data transformation. 

Data Transformation

For purposes of this article, we will focus primarily on Data Science and Machine Learning strategies, avoiding the more costly deep learning solutions and the associated costs of developing complex neural networks.  The importance of Data Transformation as well as Feature Engineering will be highlighted.

Data Science vs Machine Learning

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 extract the insights required.  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 instead of relying on data science techniques, 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 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.    Finally, 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

Data Transformation

Whether you are planning to use simple statistical modeling to extract insights or planning a sophisticated machine learning initiative, the first step is transforming your data into a usable format optimized for your specific business goals and models.   This process is called Data Transformation, detailed in Fig. 2 below.

Data Transformation Process Flow
Fig. 2:  Data Transformation Process Flow.

Feature Engineering- Critical to Maximizing Machine Learning ROI

High-performing machine learning algorithms are not possible without proper data preparation and expert feature engineering for machine learning.  Feature Engineering is the “Secret Sauce” that turns average machine learning (ML) algorithms into High-Performance ML Algorithms, and can often produce stellar ROI results.  Feature Engineering is typically a collaboration between 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?

In a nutshell, Feature Engineering involves using domain knowledge of the data to select, create, extract or transform the most useful variables (features) to feed into machine learning algorithms.  In addition to various optimization techniques, Feature Engineering involves removing irrelevant features, and prioritizing 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.

How Feature Engineering and Data Transformation Work Together

Machine Learning Pipeline
Figure 3, Machine Learning Pipeline, Image Source:

As seen in Fig. 3, features and models work together to produce high-performing machine learning initiatives.  Selecting the right algorithms is only half the battle.  In properly executed machine learning programs, model selection and feature engineering complement each other.  Bad feature and/or model selection combinations can negatively affect machine learning results.  Bad Data Transformation produces bad feature engineering.  It’s all tied together.

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 A.I. and Machine Learning Accelerator Program.

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’ AI and Machine Learning Accelerator Program 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.

How much will it cost?

A typical AI and ML Accelerator Program as detailed above would cost between $10K and $20K.

TAKE ACTION NOW ….. Message our Data Scientists below to find out more

Blockchain and Insurance: Unlocking $300B in Value

Blockchain Insurance

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

Microservices Migration Plan for Platform Banking

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

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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

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


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.


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. 


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.


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.


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.


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


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.    


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.


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.


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.