Businesses can be transformed, sometimes through new leadership, sometimes through new market realities or business relationships. But perhaps the easiest way, for IT at least, to transform the business is by enabling the business to evolve from a data silo'd organization to a data-centric enterprise and by unleashing the power of data in new ways.

Of course, easy is a relative term, and there are substantial challenges to truly transforming a business environment and proactively supporting business needs through the use of data. These include:

• Achieving a Single Source of Truth Across all Enterprise Applications - A single, authoritative view of the customer is essential for a business to become truly customer-centric.
• Achieving an Extended View of Business-Critical Data and Its Key Relationships - Understanding the true value of each customer hinges on possessing an extended view of all critical relationships.
• Capitalizing on New Data Sources - New data is everywhere from social media data to sensor and RFID data to other types of interaction data. And it is Big Data offering big insights and big competitive advantages to those who can make the most of it in use cases such as sentiment analysis, and real-time supply chain analysis.

Why Master Data Management Hinges on Data Quality
In a recent Ovum Research study, entitled Optimizing Enterprise Applications: The Data Connection, providing a single version of the truth was ranked as the foremost challenge facing IT executives today. Gartner, meanwhile, ranks a single view of the customer/party as second only to improving reporting and analysis as a tactical master data management goal. Clearly achieving a single source of truth across applications is a top of mind concern. And the best way to accomplish this is to comprehensively manage master data across the applications.

Data quality is the lynchpin of master data management, and for obvious reasons. You do not put new wine into bad barrels unless you like expensive vinegar, and neither do you put dirty fuel into a race car unless you like losing. The very idea of having a source of "truth" hinges on its contents being high quality, thus the ability to match, cleanse and profile data as part of the master data management process is critical to the success of any master data management initiative.

Three Levels of Visibility to Transform the Business
Say you want a trusted single view of the customer. How do you go about creating this single source of truth across your many applications? A MDM hub will enable you to source the best (i.e., most correct and complete) information from all relevant applications and integrate it into a single "golden" profile. Integrated data quality capabilities within the hub ensure the data's correctness, completeness and consistency. The final step is to distribute the golden profile back to the business applications to be leveraged by users and business processes.

After achieving this single view, you can move to the second level of visibility: obtaining an extended or 360 degree customer view. This will enable you to understand your customer's key relationships such as family and business relationships and how those relationships impact your business - do I have an opportunity to cross-sell or create special offers based on these relationships?

Finally, you can progress to the third level of visibility: the complete customer view, which adds all interactions your organization has had with the customer.

Delivered in real time or near-real time, the transformative power of such views is immense. Customers can be wooed with genuinely meaningful, real time offers. Up-selling and cross-selling activities can be based on complete and up-to-the-minute customer information. The number of companies that are doing this today is not huge, but it is within the reach of most organizations and the number of empowered ones is growing fast.

Harnessing the Power of Big Interaction Data
An equally transformative power lies in new data sources, particularly Big Interaction Data. We are in the midst of an explosion of interaction data from social media, click streams, sensors and machines, call detail records and other sources. Unlike the transactional data that has been driving business processes for the past several decades, interaction data is unstructured and does not lend itself to processing in relational databases. And while transactional data started as a trickle and grew over time to a tsunami, interaction data has achieved tsunami status right out of the gate. It takes processing muscle to sift through and mine it for insights - cost-effective muscle like Hadoop environments and massively parallel processing databases. Similarly, it takes data integration muscle to manage, transform, and parse it for analytic and other processing.

Already, there are organizations in just about every industry sector, including the public sector, out in front of the pack in leveraging Big Interaction Data to attract and retain customers, more deeply and quickly understand their markets, and identify and influence their markets' influencers.

In order to gain business value from Big Interactive Data, organizations need... well, they need the "same old same old," but much, much more of it. In other words, you must have the facility to:

• Get the relevant information you need, regardless of diversity of format, volume, location, or time scale (from years to micro-seconds).
• Trust the data you will use to drive your business decisions and operations.
• Combine traditional and non-traditional data sources in new and innovative ways to drive new insights and power business processes.
• Reduce risk and improve compliance via real-time data.

This means investing in solutions that deliver trusted data on any scale, leveraging adaptive data services to meet the business needs of all projects as well as self-service integration that empowers users to obtain the information they need while IT remains in control.

While this sounds like a big order, the solutions to meet it are available. They have been for years, handling more conventional (by current standards) data management tasks, but ready and proven to scale to handle today's and tomorrow's non-conventional opportunities for maximizing the return on data.