The Synergy Between Data Science and Software Development

When it comes to collaboration, data scientists and software developers work on different parts of the same workflow. The former explores the data for new insights while the latter utilizes the insights in the automation of workflow.

But they both work towards a common goal. And that is to deliver well-constructed apps. This objective is best realized through structured and close collaboration.

The collaboration is important as it ensures that organizations fully capitalize on the potential of the large quantities of data. This in turn makes the process of app creation as efficient as possible.

Defining Data Science and Software Development

Data science is the use of scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. In many organizations, data science is overseen by various managers:

  • Business managers. They work with data science teams to define the issue and create a strategy for analysis. They head different lines of business such as marketing and have data science teams reporting to them.
  • IT managers. They are responsible for the infrastructure that supports data science operations. They also monitor operations and the use of resources to ensure data science teams operate efficiently.
  • Data science manager. They oversee data science teams and their daily operations. They are team builders tasked with balancing team development with project planning and monitoring.

Data science is about teamwork. Apart from a data scientist, the team also includes a data engineer who prepares data and how it’s accessed. There’s a business analyst tasked with defining problems and an IT architect.

The latter oversees the underlying processes and infrastructure. Basically, data science jobs involve making sense of messy, unstructured data. This data is retrieved from smart devices, social media feeds, and emails that don’t neatly fit into a database.

So what is software development? It refers to the creation of software using one or more specific programming languages. The process is known as the software development life cycle. The SDLC includes various phases that provide a technique for creating products that meet certain technical specifications.

The SDLC provides an international standard used by organizations to build and improve computer programs. It also provides a defined structure for development teams to use in the creation and maintenance of high-quality software.

The Connection Between Data Science and Software Development

Data science and software development have become buzzwords in the recent past. This has caused a lot of dilemmas among computer science graduates, not being sure of what career path to take. Should they become data scientists or would software development be a better choice? To help you make the decision, we’ll let you know the synergy between the two disciplines.

1. Utilizing Data to Inform Business Decisions

The innovation potential is enhanced with the skills of these two professions. Take weather data, for instance. Weather data can be analyzed with other datasets found in different sources. This is meant to inform business decisions.

It can also be used to build a system that predicts the likelihood of traffic congestion. Historic data can be related to collisions and road quality data to build a predictive machine learning model. This can get published as an API and used with weather forecast data to build a road safety application.

This app can be used to give authorities insights on how to improve safety on the roads. It’s quite easy to see the possibility for innovation and creativity when you make it easy to gain insights. And collaborate on how to use it, like exploring the power of data for consumer and business insights.

2. Extending Agile Development to Big Data

Software developers prefer failing fast. That’s because they don’t have the luxury to spend a lot of time developing and testing a new application. It’s disappointing to realize later that the app failed to meet the business needs of users. Yet this is something that needs to be realized much sooner and it needs agility.

Data analysts, scientists, and developers collaborating with big data analytics have common requirements. To ensure success, IT organizations must extend the concepts of agile software development to big data. They need to allow data developers and scientists to get the needed data and analytics.

3. Packages and Pair Programming

There may not be a common coding language used by data engineers and data scientists. In such a case, data and SQL can be used as a common language for peer programming.

Assuming there are data science parts where SQL can’t be used, packages can be used instead. The packages can be used for communication. This is between the different data science phases and data engineers. During this collaboration process, data engineers would be responsible for:

  • Deploying the model
  • Monitoring features
  • Helping test and optimizations during the peer programming past

4. Utilizing Big Data in Boosting Software Development Projects

To keep up with technology, invest in developing customized software in integrated data. One of the most important elements of a data analytics approach to software development is collecting good data.

The best place to begin when collecting good data is with one’s completed projects. You can input the project’s start date and the end date, and the metrics collected into SLIM-DataManager.

As present projects wind down, you can perform a postmortem on project completion. While here, you can collect data on all interest metrics. You can use the review tab to enter an accomplished project data for scheduling. You can also enter project data for growth size to calculate a schedule overrun and slippage.


Collaboration and communication between teams working together are very important. And remember that people will always come and go from teams. Their position can also change. And roadmaps can shift your team to a different initiative.

Thus, you must create an environment in which people can work together. Data scientists and software developers are no longer teams handing over the wall to one another. They are one team working towards a common goal. They must collaborate to drive value for their clients.

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