Benefits of Data-Driven Development
It’d be downright lying and wild if we believed even for one second that data is static or not growing. According to Statista, “the total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes”.
In 2020, when the health crisis was disrupting every industry in the world, data only grew and replicated at an all-time high. With more and more people working from home and using home entertainment systems as a means to pass the time, the volumes of data reached unprecedented numbers, enabling industries to making data-driven decisions more accurately than ever before.
In any data-driven approach, one needs to consider how to make data valuable as heaps of data can be rendered useless if not cleaned up, sorted out, or processed adequately. Also, one must remember that only a fraction of all the data that’s generated is actually stored and retained, as it would be virtually impossible to save all of the data that’s consumed and produced with current storage systems. For example, in 2020, the storage capacity only reached 6.7 zettabytes.
Nowadays, data-driven web site or data-driven applications have been thrown around a lot, but what does it all actually mean? For companies, it’s crucial that data-driven culture is infused in every single individual that makes up the organization but there’s a real lack of tools and processes to fully comprehend how to become a data-driven company.
Oftentimes, colossal decisions rely on data, and such is the case of data-driven software development. Especially with so many programming paradigms floating around. Programming paradigms are, in simple terms, different styles of programming. Not specifically tied to a unique programming language, paradigms are more about the unique way developers program.
The main types of programming paradigms include imperative, declarative, procedural, logic-based, functional, object-oriented, parallel processing-based, and database processing-based approaches. While some are old and some are new, they all benefit from employing a data-driven approach. In this sense, there are four dimensions to look at when thinking about paradigm categorization which includes data (of course!), actions, logic, and the interface.
Once any data-driven product development is looked at through the lenses of these dimensions, it becomes easier and more transparent to decide which paradigm suits the project better, which is again, benefitted from data-driven decision-making.
In this article, we’re going to touch upon how valuable and essential data-driven development is, as well as the pros and cons of employing a data-driven approach to development. With much to unpack, join us in this data-driven journey to see how data can boost your development significantly.
What is Data-Driven Programming?
Data-driven development or data-driven programming is a data-driven approach to development that lets teams visualize how their work translates into business outcomes and how their development efforts help meet business success goals.
Modern data-driven development contributes to the success of companies as data helps shape and give foundation to full-fledged software development projects with clear and concise Key Performance Indicators (KPIs), also referred to as metrics or Objective and Key Results (OKRs).
Crystal clear, unambiguous, objective, and straightforward metrics empower development teams to strategize for both the long-term and short-term, day-to-day work and business value of data-driven software development.
While the focus of data-driven programming is on statistics, hard numbers, and figures, it’s really made possible by the individuals working on said approach. The metrics-based, data-driven programming only thrives when the people involved understand data, process it successfully, and apply it to generate enhancements and continuous learning.
It is essential that a company’s leadership team aligns KPIs and OKRs in such a way so they cohesively and comprehensively enable the making of data-driven decisions.
Difference between Data Driven and Domain Driven Concepts
Data-driven development leverages and thrives on modern programming paradigms and technologies, with robust metrics in place and an agile framework to deliver results that is flexible, scalable, and reliable.
Data-driven development operates on multiple data points and as mentioned earlier, focuses heavily on KPIs and OKRs so development teams have clear goals for their work, which gaps need to be mitigated, and glean a greater understanding of which improvements need to be made.
All in all, data-driven software development is an enabler of continuous improvement and quicker decision-making.
Now, let’s bring domain-driven into the mix. Domain-driven design is an approach to software development that revolves around specific domain models with a precise understanding of processes and rules. Typically, domain-driven development is favored by development teams working on more complex projects that require a lot of logic and organization. This style of development is rooted in data modeling, object-oriented analysis, and information engineering, making it a key element of much of the work done on databases and object-oriented projects since the 1980s.
To highlight the differences between data-driven and domain-driven development, here are lists of the pros and cons of each approach.
Pros of a data-driven approach:
- Ally of code generation, scheme, and more as it’s based on what works and what doesn’t.
- Quick application or prototype development that mitigates development gaps upfront with clear metrics in place.
- Good fit for small to medium to relatively large projects, depending on the volumes of data available.
Cons of a data-driven approach:
- Potential loss of object-oriented programming (OOP).
- Added complexity for larger-sized projects due to the sheer volume of data available.
Pros of domain-driven approaches:
- More friendly towards object-oriented programming.
- More control over scope (domain) complexity.
- Ally to complex and large-sized solutions.
Cons of domain-driven approaches:
- More resources involved, leading to higher costs.
- Support is more complex when compared to data-driven approaches.
What are Data Driven Tests?
Data-driven applications stem from a steady flow of accurate, actionable data points that help developers visualize and understand where the end result is going and what improvements need to be made along the way.
When used correctly, KPIs and OKRs create visibility and accountability on how individuals and teams contribute to the overall success goals of an organization, which is also where data-driven tests come into play.
Data driven tests, also known as table-driven tests or parameterized tests, are a testing approach that uses a table of conditions as the inputs and verifiable results as the outputs. Data-driven tests use specific criteria stored across multiple data sources, leveraging scripts in a framework that provides the perfect setting for a reusable and repeatable test logic that helps improve test coverage. In short, you can use one test on an array of data.
Pros and Cons of Data-Driven Development
Under its wing, a robust data-driven development approach has benchmarks for added visibility of current development efforts. Thus, development teams can easily visualize the impact of their work as it ties to the success of the company’s overall business metrics, leaving teams with clear and concise data points to measure individual and collective success.
With the help of data, development teams gain access to valuable and actionable insights that can effectively help reduce the time involved in a project, solve routine challenges, streamline continuous improvement, and more. It also helps accelerate the time-to-market of a software release, which is a vital component of any organization’s competitive advantages.
Data-driven development germinates a proficient, proactive, agile, problem-solving environment that allows development units to quickly fix short and long-term setbacks. In the long run, data-driven development approaches can also help organizations tap into comparative patterns and propensity research of unique outcomes at both the organization and the industry levels. This is what we like to call “big picture” thinking, which helps organizations stay ahead of the curve.
On the other face of the coin, data-driven development doesn’t come without its challenges. Oftentimes, we hear about organizations struggling to align individual and organizational KPIs and OKRs, failing to accurately measure and track individual and collective contributions. Another challenge of this approach is the actual bulk of the work which can lead to the substantial effort, unnecessary delays, or rework during development and deployment.
Making data-driven decisions is key for any organization
At Svitla Systems, data-driven development or data-driven programming is an everyday occurrence and mindset of approaching software development projects, be it for a data-driven website, data-driven web application development, data-driven product development, and really, all types of data-driven software development.
Making data driven decisions is key for any organization, and when considering the development of software projects, it takes on a higher meaning as several data points and metrics can severely impact the success of software development projects and the delivery of ongoing value to each of our projects and for every client.
Our experts on deck are fully aligned with the data-driven development approach and are fully equipped to apply effective measures, processes, and frameworks that bring value to each client’s specific metrics, identify pain points, and drive your digital transformation journey forward.
For more information about our data-driven development and how we can take your projects to the next level powered by strategic and impactful data, reach out to our Svitla Systems representatives.
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