Distinguishing Analytics, Business Intelligence, Data Science

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data science v bi x300Continuing developments in the fields of Business Intelligence, Analytics, and Data Science are making it increasingly necessary for organizations to become cognizant of the distinctions between these terms, as they relate to the value they can produce for the enterprise. The latest developments to influence these various aspects of the enterprise include:

  • The Simplification of BI: BI vendors are continuing to develop a multitude of tools and technologies that reduces the complexity of BI and its latency while empowering the business user.
  • The Expansion of Analytics: Analytics is progressing into more and more applications and has developed to the point in which it can actually prescribe appropriate and inappropriate actions for specific industries and business units.
  • The Mutability of Data Scientists: It has become increasingly apparent that Data Scientists must exhibit the skills necessary to convert scientific insight regarding data into uses and boons for the business and upper level management for this field to continue to thrive and prove itself.

Once organizations understand exactly what the capabilities of the aforementioned fields are and how they relate to their business objectives, they can tailor the focus of their particular IT initiatives to best exploit them.


Analytics is probably the single most important aspect of these three frequently confused terms, for the simple fact that both BI and Data Science utilize (and in many cases rely upon) analytics. Gartner’s definition of analytics states that “Analytics has emerged as a catch-all term for a variety of different business intelligence (BI) – and application-related initiatives…Increasingly, ‘analytics’ is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen.”

At the root of the definition of the term is the fact that analytics hinge upon algorithms to statistically determine relationships between data that can yield insight. The key difference between analytics and BI is that the former has predictive capabilities, whereas the latter has traditionally been based on providing analysis of historical data.

The capability of analytics to determine the likelihood of future events is largely possible through tools such as online analytical processing (OLAP), data mining, forecasting, and data modeling. The process involves analyzing current and historical data patterns to determine future ones, while prescriptive capabilities can analyze future scenarios and present the most viable option for dealing with them.

Although analytics is used in a burgeoning array of applications (such as those found on various web sites, computing and mobile devices) it is important to note that these tools can operate independently of one another and can be deployed for specific purposes – such as for calibrating algorithms for Data Science.

Business Intelligence

According to Forrester, BI is:

“A set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery. Research coverage includes executive dashboards as well as query and reporting tools.”


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BI is a comprehensive term that refers to analytics and reporting tools that were traditionally used to determine trends in historical data. Most vendors offer an array of tools in suites unlike analytics, which can be obtained via singular tools or applications. The key distinction between analytics and BI is that the latter actually presents the insights determined by the former in reports, dashboards, or interactive visualizations.

BI also facilitates queries in which individuals can ask data-related questions and obtain results (partly due to analytics). Unlike analytics, which is slated for those mathematically and technologically inclined, BI tools are specifically designed to present the results of analytics in a fashion that laymen understand. The growing trend towards Data Discovery tools reinforces this capability, and helps transfer the potential of data away from IT departments and into the hands of the end user.

Data Science

Data Science is one of the most recent disciplines to emerge within the field of Data Management. This term is highly inclusive and was previously described by DATAVERSITY™ as:

“Data Science combines the allure of Big Data, the fascination of Unstructured Data, the precision of advanced mathematics and statistics, the innovation of social media, the creativity of storytelling, the investigation and inquiry of forensics, and the ability to use all of those skills together while still being able to demonstrate the results to non-technical audiences.”

Data Science emerged within the wake of the prevalence of Big Data. It is a term which refers to the process of deriving understanding, significance, and form from the myriads of variety of structured and unstructured Data that Big Data can encompass. Within the field, specifically trained Data Scientists create data sandboxes with which to test new forms and characteristics of data so they can ascertain what value it might have for the enterprise and how.

When organizations are utilizing different forms of data than they previously have (especially if that data is unstructured or semi-structured), Data Scientists are required to deconstruct it prior to the utilization of BI tools to gain insight from it. And, in order to successfully utilize BI and data discovery tools on such data, Data Scientists may need to develop unique algorithms both to test the data and to discern its attributes as they relate to an organization and its interests. Analytics, therefore, can play an integral role in the facilitation of this discipline.

The challenge with Data Science is all of the various skills that it requires, which expands beyond simply understanding data structure, testing and identifying it through the usage of statistics and analytics. It actually requires relating such data to an organization’s objectives and being able to convey their value to IT, the business and upper level management. As such, the requirements for this science are continually varying and are shaped according to the needs of each particular enterprise.

Where first?

As the previous delineation of the distinctions between these three terms indicates, analytics is at the core of both BI and Data Science. Subsequently, there are hybrids of these terms and their technologies. Business Analytics refers to the movement of tailoring analytics and BI specifically for non-technical and business users, which typically focus on descriptive and diagnostic analytics as well as additional Data Discovery components such as search, data mashups, and geospatial technologies.

Analytics plays an integral role in the facilitation of Data Science, both during the initial phase of testing unstructured data and while actually building applications to profit from the knowledge such data yields. Data Science is practically a requirement for Big Data initiatives (particularly those looking to leverage the wealth of unstructured and semi-structured data abounding on the Web and via the Internet of Things), yet organizations can gain simple analytic insight (even on certain forms of Big Data) by utilizing any variety of BI tools.

Finally, it is worth noting that it is also possible to use simple analytics applications, such as any one or two tools that might come in a full-fledged BI suite, to build applications to assist with data-driven business processes – either for Big Data or conventional data. This approach is something of a hybrid of all three technologies, yet also distinct in the fact that it relies more on analytics than on Data Science or merely attaining insight through BI. Application building incorporates analytics to actually create the required action that the knowledge from data provides, and is extremely organization or even business unit specific. There are a number of application building frameworks which can incorporate analytics, such as Concurrent’s Cascading.

Launching Point

Although the specific approach to the application of analytics – either through BI, Data Science, or application building – may vary according to an enterprise’s needs, it is important to note the broad applicability of BI. Its capacities are constantly expanding to include greater access to more forms of data in intuitive, interactive ways that favor non-technical users. Consequently, the business can do more with the data accessed through these tools in less time than it used to, which makes applying discovery-based BI an excellent starting point for the deployment of analytics. According to Gartner:

“By 2015, ‘smart data discovery,’ which includes natural-language query and search, automated, prescriptive advanced analytics and interactive data discovery capabilities, will be the most in-demand BI platform user experience paradigm, enabling mainstream business consumers to get insights (such as clusters, segments, predictions, outliers and anomalies) from data.”

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