BI works with historical data to perform de-facto analysis as well as create patterns of data analytics and forecasting. While the narrow definition of BI only serves reporting and data storage exercises, data analytic tools are capable of providing insights into the trends generated by BI. The larger definition of BI would effectually involve these basic or maybe even more advanced predictive capabilities. Then what exactly is data analytics? Samuel Victor, the Vice President for BI at TAKE Solutions retorts, “While BI is knowledge, data analytics is wisdom."
Evolving BI scenario
The BI market is continuously evolving; vendors are enhancing product features to meet the consumer demands. When differentiating data analytics from data mining capabilities in BI tools, the advanced capabilities are being spun off as new data analytic tools in themselves. This response to the need of business users is playing a crucial role in demarcating data analytics as a separate exercise.
Additionally, through mergers and acquisitions (M&A), vendors are acquiring companies to add advanced functionalities of data analytic tools. Nevertheless, these solutions are to be held under the larger bracket of BI, even though the presentation suggests a new arena of information management. Data analytics is in reality a subset of BI.
Distinguishing data analytic tools and BI
Data analytic tools and BI solutions cannot be used as synonyms. Victor says, “People in the industry are usually under the misconception that data analytic tools decipher statistical patterns to data, while BI is simple reporting, which can be performed on any kind of data.” The distinguishing factor is perhaps the level of sophistication offered in predictive analysis.
Data analytic tools are based on a firm mathematical model; it uses algorithms to deal and manipulate the data. Being focused on deciphering statistical patterns to data, data analytics is more quantitative than BI reporting. Data analytics relies heavily on data preparation and data usage, which are necessary for data analytic tools to uncover trends as well as develop further logical strategies.
This leads to another point of concurrence that both data analytics and BI face challenges around the organization and storage of data. Data needs for BI span data marts and warehouses. Data analytic tools may consume these, but cannot essentially use them. Data analytics would be concerned about the preparation of data for extraction of patterns. The data needs for analytics depend on the analysis being done.
The first step for BI is data warehousing, a prerequisite for data analytics as well. From the foundation of the warehouse, BI is applied, and data patterns are created to aid in analytics. Data analytics may not be possible on all the available data. Definition of the data then becomes a crucial part of analytics, which is removed from BI. Accuracy is the forte of data analytic tools.
Ideal usage scenarios
During the early part of an organization’s data management journey, analytics may not be used. It might first settle down with BI. In this context, Dhananjay Singh, the BI Delivery Head at UnitedHealth Group feels that the question of whether BI is better than data analytics does not arise.
An organization cannot therefore, grow and evolve with the BI solution they have in place, but might need to incorporate a new data analytic tool as their information needs mature. All the more so since vendors are now packaging their BI solutions in a way that advanced analytical capabilities are offered as separate data analytic products with new capabilities. “The latest trend in data analytic tools is to incorporate in-memory analytics as a key selling point to their products,” observes Evelson.