5 reasons why data science is not statistics

Statistics play an important role in identifying hidden patterns in data, but a common pitfall in business today is the frequent interchangeable use of statistics with broader terms such as data science, data analytics, and business analytics. This article explains the different terms and shows why Data Science is a collection of practices used in conjunction with statistical methods to address specific data challenges.

The content shows why the understanding of how data science incorporates statistics allows for better planning, risk assessment, and resource allocation in any advanced analytics project. Managers embarking on a data science delivery project must understand a much broader set of considerations to assess the commercial feasibility of analytics investments. The article discusses this from the perspective of business analytics which is a subset of data science as applied specifically to business problems.

The use of data science in determining marketing related activities like customer churn prediction, ad optimization, deciding on the next best action based on past purchase history, and fraud detection, are all examples of business analytics (data science) where statistics is used to glean insights from data. Taking the example of marketing data, the author explains why the ability to deliver business analytics capability requires a range of skills that are not statistical in nature.

The article illustrates why looking at a business issue purely from a statistics point of view will not yield results, and why it is important to ensure data science projects address the original business issue, which can involve numerous business management, legal and commercial considerations. Turning the promise of data science into reality needs good all-round business consulting skills with a strong understanding of company business, data, ETL/data-warehousing techniques, and statistical concepts.

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