CIOs Finding Business Value in Structured and Unstructured Big Data

CIOs recognize how valuable both structured and unstructured data is to their organizations.  However, the process of recognizing this value differs for each data type.

Structured Data

As its name suggests, Information Technology (IT) knows what type of data is stored and where it is located, due to the schemas that define data types, values, etc. To extract value in the structured data, one needs to have the proper mining techniques and technology in place.  This enables the CIO to make the proper business decision regarding the challenges that face the IT department.

Unstructured Data

This is more difficult, because the data is free-form and harder to locate and utilize. Understanding how it should be classified and where it is stored is the first step a CIO must take to extract value from unstructured data.  This is very important in that the data helps various lines of business create revenue where lines of business are the profit centers. CIOs should realize that if the lines of business are not given access to the proper data when they need it and how they need it, the data becomes useless.  CIOs that treat their unstructured data as an asset rather than a liability are able to add much more value to the data content.

There are several skills and technologies to develop that can change big data from a cost to a revenue center. Revenue centers traditionally generate profit through sales and marketing-related activities, while cost centers typically eat into profit by generating expenses against a budgeted target. The volume of data or information created today, often referred to as big data, and its importance to the organization, has many CIOs looking for technology and processes to leverage the opportunity that data presents and change big data from a cost to a revenue center.

Technologies that can be used for turning big data into a revenue center include ones that locate and mine data from numerous customer touch points. These technologies can gather data from disparate locations, bring it together, and decipher meaning from it which can then be used by an organization’s marketing and sales teams. The data will be automatically categorized and clustered and can be used for targeting customers around their patterns and behaviors to leverage sales opportunities. These technologies also cross over to post-sale customer touch points and can show if the customers are having a positive experience that can later turn into opportunities for reference and word-of-mouth sales.  The method for accomplishing this isn’t easy, as there will be a great deal of work, but once that task is complete, companies can leverage that customer information and benefit from it.

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