Why you need an analytical hub

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Why you need an analytical hub

blog_crystallball_predictivemodelingEnterprises are flooded with a deluge of data about their customers, prospects, business processes, suppliers, partners and competitors. It comes from traditional internal systems, cloud applications, social networking and mobile communications.

With the flood of new data comes the opportunity for business people to perform new types of analysis to gain greater insight into their business and customers. Enterprises have been expanding their traditional BI footprint to provide much more comprehensive and timely reporting for their business. These investments are valuable, but they are limited to analyzing how a business has performed historically.

Looking beyond historical data, there’s a significant business opportunity in analyzing what the future may hold, e.g., predictive modeling, or examining customer behavior from sources outside the enterprise, e.g., social media.

This shift to forward-looking analytics dictates changes both for the business and IT. Traditionally, IT received detailed data requirements, used ETL tools to extract data and load it into a data warehouse (DW), and then provided business people with read-only access to that data.

It’s a long process – too long for people working with advanced analytics (we call them data scientists). They typically do not know all the data they need until they start modeling, and need great flexibility for processing data.

IT needs to change to a supporting role with an analytical hub, and relinquish control to the data scientists. IT needs to understand that data scientists are much more data savvy than traditional BI users, and can be trusted with data.

Read more about best practices for the analytical hub in this white paper.

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