Utilizing Big Data To Streamline Your Development Process

Application development comes with huge challenges. These problems have grown considerably over time thanks to the ever growing complexity of app and app development.

Big data is increasingly becoming the key to success. Consequently, software developers need to understand how and when to implement big data solutions into their projects as well as their development processes.

Are you trying to make your development processes more effective? If you are, then I will now explain some key areas where you can utilize big data to streamline your development process.


The market for software development
Challenges while undertaking a software development project
An introduction to big data
The global market for big data
Big data use cases
Big data and the development process
Going deeper: Big data in Agile projects involving DevOps
Testing: Another area where big data for developers matters
Analyzing Agile metrics: How big data helps
Exploring big data to improve your development process?

The market for software development

The software development industry is a lucrative market for those who succeed. Just take a look at the following statistics:

  • The custom software development and consulting market in the US will reach $213.487 billion by 2025. Read “Market research, revenues, market size, profits, ranks, growth rate and forecasts, financial benchmarks, historic ratios, business metrics and analysis” for more information.
  • The global market for enterprise applications is estimated to reach $259.51 billion by 2022, with a CAGR of 7.8%. You can read “Enterprise application market size worth $259.51 billion by 2022” for more detailed information.
  • As Agile software development becomes more and more popular, the market for DevOps is also growing. Globally, this market will likely reach $12.85 billion by 2025, as this “Grand View Research” estimate
  • Challenges while undertaking a software development project

    Entrepreneurs undertaking software development projects need to contend with a number of challenges, as follows:

  • People: Businesses need competent project managers and an experienced team. In a highly competitive environment, it can be hard to find such capable human resources.
  • Methodologies: It’s often hard to zero in on the right development methodology.
  • Architecture: Choosing the right software architecture isn’t easy.
  • Tools: Finding the right platforms for the project, choosing the right programming languages, and selecting the right tools/frameworks can be tricky.
  • Productivity: It takes considerable expertise to achieve optimal productivity, moreover, the team needs the right tools to measure their progress.
  • I have explained these challenges in “10 biggest challenges when developing an app”. Big data can help in measuring team progress, however, let’s first define what big data is.

    An introduction to big data

    Big data is a relatively recent term. It has been popularized since the start of the new millennium, however, the concept isn’t new. The following facts and characteristics explain big data:

  • Businesses collect a large amount of data and analyze it.
  • Since the rise of the internet economy, the volume of this data has grown significantly.
  • Big data refers to data sets so large and complex that traditional data processing applications can’t handle them.
  • The rate of data grows is phenomenal.
  • This data comes in a vast range of formats. Some examples include data in traditional databases such as Excel etc., unstructured text documents, videos, audios, etc.
  • The flow of this data isn’t consistent since there are peaks and troughs.
  • Coming from various sources, this data is highly complex. Cleansing, transforming, and linking, etc. are hard.
  • Big data helps in addressing these challenges.
  • Read “Big data: what is it and why it matters” for more information.

    The global market for big data

    The following statistics help understand the global market for big data:

  • In 2005, 0.1 zetabytes data were created globally, however, this grew to 12 zetabytes in 2015. Experts estimate that this volume will grow to 163 zetabytes in 2025, as this Statista report predicts.
  • Global big data market was worth $7.6 billion in 2011, however, market observers expect it to grow to $103 billion in 2027. Read “Big data market size revenue forecast worldwide from 2011 to 2027 (in billion U.S. dollars)” for more insights.
  • Services spending forms the major chunk of this revenue, and IBM, Oracle, Splunk, Dell, and Accenture lead the big data services industry. You can read “Big data – statistics & Facts” for more information about the big data market.

Big data use cases

So, what is driving this impressive growth in the market for big data? Enterprises, as well as small and medium businesses (SMBs) increasingly profiting from the benefits of big data, which is powering massive growth. Let’s review a few big data use cases:

  • Businesses are using big data to build a 360-degree perspective of their customers. To do this, they pull in data from various sources and employ powerful analytics engine to identify patterns.
  • Fraud prevention is one area where big data is important. Banks, financial services institutions, and many other businesses analyze massive customer and transaction data in order to identify potentially fraudulent transactions.
  • Cybersecurity is another area where big data is playing an important part. Cybersecurity solution providers analyze massive data sets containing online interactions and identify potential malicious players.
  • Replacing traditional data warehouses with modern big data solutions such as Hadoop can result in significant cost savings.
  • Price optimization using big data is becoming common with large businesses as well as SMBs.
  • Improving operational efficiency by using big data is another key use case.
  • Every internet user now benefits from recommendation engines, and these are powered by big data.
  • “Internet of Things” (IoT) creates an enormous amount of data. Businesses and other organizations need to extract value from IoT, therefore, big data analytics is key.

Read more about these uses cases in “Big data use cases”.

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Big data and the development process

By now, the importance of big data should be quite clear.

Let’s take a look at why it matters in the software development process. Firstly, we need to understand recent changes in the software development landscape, which are as follows:

  • Unlike traditional software development that focused on updates every quarter, or sometimes even less frequently, Agile demands regular releases/updates.
  • Agile projects often have releases/updates every 2 weeks. You can read about this characteristic of Agile in “Waterfall vs Agile: which methodology is right for your project”.
  • Managing a development project has many control points, and quite a good number of them concern updates/releases.
  • The control points related to release could be about “go/no-go” decisions, whether a fall-back plan exists for a release, etc. Such control points must be present for every release, even if the organization uses different terminologies for them.
  • There are also control points that may not fully concern themselves with releases, but they still are closely linked to a release. Take the example of “User Acceptance Test” (UAT) completion. This concerns testing, however, a software release must have this control point.
  • From this analysis, you can see how the number of control points increases when the releases are more frequent. Once again, the project team might use different terminologies for these control points, however, they can’t omit any. Customer satisfaction is one of the key principles of Agile, therefore, Agile gives no license to dilute project management.
  • Managing any control point in a project, including in the case of software development, requires being in control of the project data. When the control points are more frequent, you have more data being generated. You need to make sense of this data if you really want to be in control of your project development.

This is where big data is making a huge impact. Read “The future of project management in the era of digital transformation” to learn how big data and analytics are changing software development project management.

Quality-conscious and client-focused software development companies are already using big data to manage their development projects. Here at DevTeam.Space, we use our data-driven processes to ensure that we deliver our client’s on time, on budget, and as bug-free as possible.

Going deeper: Big data in Agile projects involving DevOps

Let’s go one step further and review how Agile projects are creating an increased amount of data.

As you know, organizations are increasingly embracing DevOps for Agile projects. This makes perfect sense since regular releases can be hard to implement without DevOps. Read more about it in “Agile and DevOps: Friends or Foes?”.

In fact, experts recommend an Agile framework for implementing DevOps. DevOps has a few other key components:

  • Automated testing
  • Configuration management
  • Regular integration
  • Infrastructure as code

Read “A guide to the key components of DevOps” to learn more about the components of DevOps.

A key characteristic of DevOps is that DevOps tools produce a large amount of data during build management, release management, etc.

With the rising popularity of Agile and DevOps, project managers now get much more data. The project manager needs to make sense of this data. Big data assumes a special importance here by allowing the PM to derive accurate, real-time insights from this data.

Testing: Another area where big data for developers matters

Let’s now dive deeper and analyze the importance of big data in product testing. Modern software development projects use test automation frameworks like Selenium. There are many advantages of using tools like Selenium, e.g.:

  • They support popular languages and frameworks for test automation.
  • Selenium-like tools support many popular browsers and operating systems.
  • It’s easy to use, flexible, enables parallel test execution and has many integrations.

Read more about these advantages in “11 reasons to use Selenium for automation testing”. Note that test automation frameworks like Selenium also generate plenty of data.

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Additionally, testers use tools such as TestNG “listeners”, to collect data from different events in the testing life cycle. This helps them to measure various test metrics. Read more about listeners in “TestNG Listeners in Selenium: ITestListener & ITestResult Example”.

How do test teams make sense of the significant amount of data related to their test efforts? In which ways can they determine if their testing project is proceeding according to plan? This is where big data plays an important part.

Software development project teams often use big data-enabled test reporting tools, which use analytics and delivers insights from the above-mentioned test data. An example is “SeeTest Reporter” from Experitest. This tool consolidates test results for mobile and web app testing and delivers quality dashboards, root cause analysis, etc.

Analyzing Agile metrics: How big data helps

As I have explained, frequent releases in Agile results in a higher number of control points. Now, to manage these control points effectively, and deliver tangible value to clients, an Agile PM needs to closely review the relevant Agile metrics. This is easier said than done!

Agile teams are small, cross-functional teams, which work in short iterations. For e.g., “Scrum” is a popular Agile technique, and here we call these iterations “Sprints”. You can read more about scrum in “How to build a scrum development team?”.

Consistent tracking of Agile metrics requires them to be analyzed during every sprint. Since the sprints are short in duration, a manual analysis of these metrics is simply not practicable. Consider the various metrics that the PM needs to track, e.g.:

  • Sprint burndown;
  • Epic and release burndown;
  • Agile velocity;
  • The number of defects;
  • Test coverage rate;
  • Lead time;
  • Cycle time;
  • “Cumulative Flow Diagram” (CFD).

Since Agile projects use test automation frameworks, DevOps tools, and several other tools, these metrics will need to be derived from a large volume of data. This is where big data analytics plays a key part.

A real-time dashboard is vital in tracking these metrics effectively. DevTeam.Space uses our in-house big data-enabled real-time dashboard to manage our projects.

If you are interested in learning more about what makes a great software development dashboard, then please refer to “How a real-time dashboard can revolutionize your eSports development process?”.

Exploring big data to improve your development process?

You might have a software development project lined up and are consequently exploring various options to improve your development processes.

As I have shown, big data is an important tool here. The growing trend of data-driven Agile project management clearly attests to this.

However, it can be challenging to set-up the right processes and frameworks if you don’t have extensive industry experience.

You need the right people, processes, methodologies, and tools/frameworks.

If you don’t have the experience, then consider engaging the most suitable development and consulting partner. Read our guide on “How to find the best software development company?” to make sure you engage the right development company for your project requirements.

Best of luck with your project.

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