The Intersection of Data and the Cloud: Journey from A to E

Cloud Conversation

The Intersection of Data and the Cloud: Journey from A to E

By TMCnet Special Guest
Nigel Turner, Trillium Software
  |  September 04, 2013

In today’s data driven world high quality data is a prerequisite of success and survival. The exponential amount of data generated by big data and cloud computing, coupled with the advent of the 24/7 online, any time global business, means it is no longer feasible to tackle data quality problems as point applications within tactical business and IT silos such as CRM, and billing. Instead organizations need to develop approaches that ensure high quality data across all parts of the enterprise.

But moving data quality improvement from application to enterprise level – from A2E – is a daunting task requiring cross-organizational collaboration between business and IT to make it happen. Its potential complexity means it is all too easy to lose your way; a roadmap is needed to help reach the desired destination.

It is estimated that by 2015 an average organization will hold 700 times the volume of data it held in 2000. By 2020 this will increase tenfold so that it will store 7,000 times the volume of data it held at the turn of the century. This has led organizations to seek new ways of managing and storing data, which has fueled demand for cloud computing. As such, in the areas of data quality and data governance, there is a growing awareness that continually expanding data demands mean that traditional approaches are no longer sufficient.

Historically, the dominant approach to data quality improvement and data governance tended to focus on point solutions. For example, organizations used data quality to “clean up” customer marketing lists or reconciling financial records for compliance purposes. However, this approach left the root causes of poor data only partially addressed and at times, not at all. Without knowing it, organizations were putting themselves at risk for lost profits and more as the majority of their data was untouched and uncontrolled.

Fortunately, many have realized that permanent and enduring data quality improvement and management requires a new approach, one that requires the entire organization to be involved and leverages a close partnership between business and IT functions. This is radically different as it not only requires business involvement, but requires that it is business-driven and managed, and not the primary preserve of the IT department.

Five Steps from A to E

Here are five steps to enable data quality champions in an organization to successfully sell and implement a true enterprise wide approach to data quality:

Step 1 – Prepare the Ground

Ensure that the benefits of data quality improvements already made in point applications are fully documented. Develop a wider engagement strategy that doesn’t just rely on IT. To make it work, business and IT need to be not only involved, but totally bought in so be sure to identify the key people who can be supportive and help drive the strategy for the long term.

Step 2 – Identify the Opportunity

You will uncover data quality issues and problems in areas you never considered. Don’t shy away from it! Use those challenges to produce a vision of what enterprise data quality could (and will) deliver.

Step 3 – Secure Support

Rally the troops at all levels to gain endorsement and active support of the project. Be sure to develop a high-level business case for funding and resourcing. When things go wrong or momentum slows, refer back to that business case to remind your key sponsors why this initiative is so important to the business.

Step 4 – Get Organized

Create an enterprise wide data quality steering group to oversee activities and a User Forum to ensure stakeholder representation. Consider creating a physical or virtual enterprise wide Center of Excellence that uses a designated to team to provide data quality services in a standard process across the organization.  Implement a common data quality improvement methodology and toolset.

Step 5 – Deliver the Improvements

Produce a data quality improvement roadmap to share the end goal of your journey and highlight your progress along the way. Deliver early data quality projects to prove the approach, toolset and projected benefits.

Harnessing the power of big data and cloud computing requires a major shift for in how organizations approach data quality. Moving from application-centric data quality to one where data quality is tackled as a strategic, enterprise wide initiative can be a transformative initiative. But to be successful, the business must play a leading role. By creating a strategic program delivered through cross-organizational collaboration organizations will quickly realize the benefits of sustainable data quality improvements.

Nigel Turner is vice president, Information Management Strategy, at Trillium Software.


Edited by Alisen Downey
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