The current working definitions of Data Analytics and Data Science are inadequate for most organizations. But in order to think about improving their characterizations, we need to understand what they hope to accomplish. Data analytics seeks to provide operational observations into issues that we either know we know or know we don’t know. Descriptive analytics, for example, quantitatively describes the main features of a collection of data. Predictive analytics, that focus on correlative analysis, predicts relationships between known random variables or sets of data in order to identify how an event will occur in the future. For example, identifying the where to sell personal power generators and the store locations as a function of future weather conditions (e.g., storms). While the weather may not have caused the buying behavior, it often strongly correlates to future sales.

Data Analytics vs Data Science

The goal of Data Science, on-the-other-hand, is to provide strategic actionable insights into the world were we don’t know what we don’t know. For example, trying to identify a future technology that doesn’t exist today, but will have the most impact on an organization in the future. Predictive analytics in the area of causation, prescriptive analytics (predictive plus decision science), and machine learning are three primary means through which actionable insights can be found. Predictive causal analytics precisely identifies the cause for an event, take for example the title of a film’s impact on box office revenue. Prescriptive analytics couples decision science to predictive capabilities in order to identify actionable outcomes that directly impact a desired goal.

Separating data analytics into operations and data science into strategy allows us to more effectively apply them to the enterprise solution value chain. Enterprise Information Management (EIM) consists of those capabilities necessary for managing today’s large scale data assets. In addition to relational data bases, data warehouses, and data marts, we now see the emergence of big data solutions (hadoop). Data analytics (EDA) leverages data assets to provided day-to-day operational insights. Everything from counting assets to predicting inventory. Data science (EDS) then seeks to exploit the vastness of information and analytics in order to provide actionable decisions that has a meaningful impact on strategy. For example, discovering the optimal price point for products or the means to increase movie theater box office revenues.  Finally, all of these insights are for nothing if they are not operationally fused into the capabilities of the larger enterprise through architecture and solutions.

Data Analytics vs Data Science 2

Data science is about finding revelations in the historical electronic debris of society. Through mathematical, statistical, computational, and visualization, we seek not only to make sense of, but also provide meaningful action through, the zero and ones that constitute the exponentially growing data produced through our electronic DNA. While data science alone is significant capability, its overall valuation is exponentially increased when coupled with its cousin, Data Analytics, and integrated into an end-to-end enterprise value chain.


  1. Analysis is a part of any scientific research and it is a first step in building a theory. The second step is synthesis, that actually builds or creates a theory. It is also important to remember that science is not about results, but a about a methodology to get them. With a global trend to simplification of everything (the best marketing slogan: you don’t need to know anything in order to use . . . ), I do not think that we need to degrade Science to the level of operational techniques.

    • Relying on causations is more ​error prone than relying on correlation because in social setups causal relationship between a set of variables may be moderated by several unknown variables which u might have not considered even. Secondly the relationships may be too complex to ones level of understanding. I guess this is where the role of data scientist lies, which the author wants to refer to but somehow missed to highlight.

    • I agree with you. Don’t trust learned academicians, they create definition and scope in everything.

  2. Reblogged this on BnsTech Report and commented:
    Data analysis and data science are both related to statistics and trying to find answers through data. Professionals of both fields use Python, Java, R, Matlab, and SQL languages to do their job too. However, data analysis is more on cleaning raw data, finding pattern, and presenting the result; meanwhile data science is more on predicting and machine learning through existing data.

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