Do you have a blueprint for your integrated big data and data warehouse landscape?

By Rahul Agarwal 

Many commentators have called big data ‘the new oil for business’. Big data analytics is fast becoming the basis of competitive differentiator and growth for companies in every sector in the global economy. But what about the data warehouse that has been around for years?  Is it on its way out or becoming irrelevant?

Absolutely not!

As argued by Andrew Foo in this article, the insights derived from data warehouses have been fundamental to the sustainability of the organization and this will not change in the near future. In fact, many firms are merging big data with the traditional enterprise data strategy  in order to create a holistic picture of their market, customers & business operations. In such a setup, Hadoop provides a powerful, cost-effective repository for both structured and unstructured data and complements the traditional data warehouse.

How can you simplify the deployment of various components and ensure data veracity in this integrated environment?

Interoperability of these big data and traditional data components is extremely important in such a scenario. This is because the value of analytics can be undermined if confidence in the veracity of information is lost. Therefore, enterprises need to put in place a framework to oversee and enforce consistent data definitions, business terms and associated relationships across these components.

Having both of these components derived from, or described by, a common model can help to administer that consistency and integration. A common model can also help accelerate the deployment of the components of such an integrated environment and simplify the linkage between big data topics such as social media, sensor data and unstructured data with the traditional sources of data.

IBM Industry Models

Over the years, IBM Industry Models have been used to enhance business agility by helping clients build and rationalize data warehouses faster, providing consistent data architecture for modeling new or changed requirements, and compressing the required time compared to customer-built projects. They have also being used to help reduce risk and enable successful delivery of high-quality data for applications across the organization. As the typical data warehouse landscape expands to include big data technologies, the role of IBM Industry Models has evolved. To know more about how IBM Industry Models can help to design and accelerate the deployment and governance of this integrated big data and data warehouse landscape join us for session # IWA-5303 during IBM Insight 2014.

About Rahul Agarwal

Rahul Agarwal is a member of the worldwide product marketing team at IBM that focuses on data warehouse and database technology. Rahul has held a variety of business management, product marketing, and other roles in other companies including HCL Technologies and HP before joining IBM.  Rahul studied at the Indian Institute of Management, Kozhikode and holds a bachelor of engineering (electronics) degree from the University of Pune, India. Rahul’s Twitter handle :  @rahulag80


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