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Leverage Data Analytics in Software Development to Grow Business Value

The race to turn data into actionable insight is on. According to a McKinsey Global Institute report, high performing companies, or "Superstars", make up 10% of companies worldwide and capture 80% of global profits. The shift to a more data-driven mindset, means leaders are motivated to use their data to gain a better understanding of their audiences, identify opportunities for innovation, and become—or at least close the gap with— "superstar" companies. But, despite great amounts of data coming in, using this information to deliver business value is more difficult than it seems.

So, what's preventing organizations from generating business value through their data?

A Harvard Business Review article stated that the reason may lie in the fact that data scientists often feel overwhelmed by the complexity of the data, and business experts find data processing makes it difficult for them to address issues on time. Add this to the difficulty of finding trained data science professionals in the market and you're faced with considerable hurdles when it comes to creating products and implementing changes driven by data that significantly impact business.

A Few Considerations About Data Science Challenges

Having large amounts of unprocessed data available makes companies feel that they're sitting on a goldmine. Organizations are eager to leverage data quickly to help their business grow, but data science and analytics processes usually take much longer to develop valuable insight than business leaders understand. A few key factors leaders should keep in mind include:

  • Often very brilliant minds will set on the task of producing highly accurate methods that work well but aren't necessarily relevant to the business. It's important to establish that the purpose of data science for an organization is to deliver business value at the start of a data science project to achieve effective results. 

  • Data science's highly experimental nature means a lot of time is spent on identifying and analyzing a multitude of possibilities. A way for an analysis to be valuable to the business is to frame it in a Minimum Viable Product scenario, which requires data scientists to shift from an exploratory mindset to one where an MVP takes precedence.

  • It's difficult for companies to determine whether their data science efforts have produced business value if it's not measured. Understanding whether something delivers business value requires companies to take the time to establish success metrics and evaluate the impact their data science initiatives have on business.


How to Improve Processes to Leverage Data Science in Software Outsourcing projects

Luckily, there are steps organizations can take to better improve their data science efforts and tie analytics projects to tangible business results.

  • Understand your business and connect with business-savvy players
    To directly address the disconnect between data science and business, it's important to bridge the gap. The people in your organization with ample business knowledge will be the best resource to help guide a data science team towards meaningful findings. Maintaining constant contact with them will help the team get a feel for what will impact the customer, product, or market, as well as help correct the team's course if a project is not focusing on relevant aspects of the business.

  • Establish metrics and measure results
    It's hard to say if a project is on track if there's no frame of reference for what's expected or what's considered a success. Having clear metrics (conversion rates, market share, customer lifetime value, etc.) will help give a clear idea of your project's starting point and where it can go. It will also help directly correlate data science efforts with revenue, making the connection between analytics and results that much clearer.

  • Go agile
    Agile methodologies are proven to improve productivity and help teams deliver value quickly and effectively. For a data science and analytics project, agile methodologies can help teams connect with the business requirements through feedback loops and provide the insight needed to keep a project relevant.

  • Center leadership both on business goals and analytics
    It's easy for people to do what they're best at, which is why data science projects can lean towards more math-related results than keeping an eye on business value. Having a person that can combine both the business insight and the analytic potential can help a data science team achieve high-value results. Finding a person that meets this criterion may be difficult, but establishing a framework that emphasizes collaboration between the business and analytics experts will help keep the project on track.


As we've mentioned in previous articles, understanding the skill requirements for data science professionals can be challenging. However, investing the time and resources to find highly skilled professionals can make the difference in ensuring your data science projects deliver business value for your company. A good option to fill these profiles is partnering with an offshore software development partner. An effective software outsourcing engagement can enable you to access talent and power your data analytics projects with a team of experienced data science professionals leveraging agile methodologies within a DevOps mindset.



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Tuesday, 19 November 2019

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