Differences between Business Intelligence and Business-Centric Data Science - dummies

Differences between Business Intelligence and Business-Centric Data Science

By Lillian Pierson

The similarities between business intelligence (BI) and business-centric data science are glaringly obvious; it’s the differences that most people have a hard time discerning. The purpose of both BI and business-centric data science is to convert raw data into actionable insights that managers and leaders can use for support when making business decisions.

BI and business-centric data science differ with respect to approach. Although BI can use forward-looking methods like forecasting, these methods are generated by making simple inferences from historical or current data. In this way, BI extrapolates from the past and present to infer predictions about the future. It looks to present or past data for relevant information to help monitor business operations and to aid managers in short- to medium-term decision making.

In contrast, business-centric data science practitioners seek to make new discoveries by using advanced mathematical or statistical methods to analyze and generate predictions from vast amounts of business data. These predictive insights are generally relevant to the long-term future of the business.

The business-centric data scientist attempts to discover new paradigms and new ways of looking at the data to provide a new perspective on the organization, its operations, and its relations with customers, suppliers, and competitors. Therefore, the business-centric data scientist must know the business and its environment. She must have business knowledge to determine how a discovery is relevant to a line of business or to the organization at large.

Other prime differences between BI and business-centric data science are

  • Data sources: BI uses only structured data from relational databases, whereas business-centric data science may use structured data and unstructured data, like that generated by machines or in social media conversations.

  • Outputs: BI products include reports, data tables, and decision-support dashboards, whereas business-centric data science products either involve dashboard analytics or another type of advanced data visualization, but rarely tabular data reports. Data scientists generally communicate their findings through words or data visualizations, but not tables and reports. That’s because the source datasets from which data scientists work are generally more complex than a typical business manager would be able to understand.

  • Technology: BI runs off of relational databases, data warehouses, OLAP, and ETL technologies, whereas business-centric data science often runs off of data from data-engineered systems that use Hadoop, MapReduce, or Massively Parallel Processing.

  • Expertise: BI relies heavily on IT and business technology expertise, whereas business-centric data science relies on expertise in statistics, math, programming, and business.