Both data science and business analytics involve collecting data, modeling, analyzing, and gaining insights. But you may be asking yourself what sets data science and business analytics apart. In this overview, we’ll walk through what business analytics is, what data science is, and how they differ.

What’s Business Analytics?

Business analytics is the statistical study of business data, which has been leveraged by companies for as long as businesses have existed. Generally, it is a technique applied to solve problems that are very specific to the business, like cost and revenue.

What’s Data Science?

Data science presents a much bigger picture of the business. It not only seeks deeper data insights, but also prescribes strategies in a more efficient manner. Data science does not settle on description of facts for business owners to analyze from, but provides a complete strategy by factoring in many exogenous factors from internal and external behaviors reflected by historical or cross-sectional data.

10 Main Differences Between Business Analytics and Data Science

  1. Data Science requires extensive coding skills and statistical knowledge, while business analytics requires minimal coding and statistical process knowledge because the focus is on data visualization and data queries.
  2. The mainstream concepts of data science revolve around machine learning, where traditional parametric and non-parametric estimation disciplines are at the foundation. On the other hand, for business analytics, cognitive analytics is more commonplace.
  3. Business analytics is a necessity where knowledge of business performance is a key to business success. Data science is considered more of a luxury.
  4. Although the mathematical construct of statistics is an old discipline, the practice of data science is a new concept. On the other hand, business analytics have been around since businesses have been around.
  5. Data science is highly insightful and more predictive in nature. It discovers the underlying phenomenon, which results in what business analytics presents at the surface. At the same time, it predicts and suggests optimal strategies, which is not in scope for typical business analytics effort.
  6. Data science typically requires more data, both internal business data and external data like demographics, weather, and social media.
  7. Every business has these two interdependent aspects: Solving immediate problems via rapid fixes and keeping an eye on the larger picture by predicting and proposing new directions. For the first part, business analytics is a great tool for day-to-day decisions, whereas data science adds to the long-term, strategic layer.
  8. Data science can often answer questions that are out of business analytics scope. There are very different types of cases where you use one or the other. Data science often uses more unstructured data than structured. Data science looks for more underlying behavior data and presents causality in processes which can be driven from internal business, social media data, or weather and external data. Conversion of speech, visionary, and text into numeric data is often leveraged in data science. Whereas business analytics uses mostly structured data like transactional, customer, and product data.
  9. Business analytics gives you clear and exact insights to help make critical business decisions. Data science findings are not used by business decision makers directly. Common data science efforts are to validate or invalidate different assumptions, testing hypothesis, risks, and probabilities.
  10. The cost of data science is much higher than business analytics because a lot more data is needed, along with more sophisticated coding skills, and an ability to process large volumes of data.


Different Implementations, Different Results

Data Science vs Business Analytics Chart


These were just a few differences between the two topics, and only the beginning of a series of articles on this vast and rich subject. The recent developments in data science and business analytics are about to change the dynamics of businesses and up the game for people who are looking to stay competitive in the market.
With the rapid growth and proliferation of big data and forecasting, business owners are no longer relying solely on business insights. Instead, they want to make a better understanding of the future so the business can make its mark in the present moment.

Follow us on this deep-dive journey of business analytics and data science in our upcoming blog posts.

About the author

Kiran Sreewastav

Kiran Sreewastav
Kiran is a technology and strategy executive who is a change champion with extensive experience in digital strategy and business transformation, data and business analytics, software development, complex problem-solving, employee coaching, and organizational design. She is passionate about advocating for data-driven business cultures that leverage data assets to optimize performance and generate growth.

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