Since the economic downturn of 2008-2009, organizations worldwide have scrambled to reorient and reconfigure their operations to maximize existing processes in the face of slowing sales. To do this, businesses turned to every asset – including production equipment, personnel and facilities – to find ways to maximize revenue, reduce costs and mitigate risks.

For many businesses, another corporate asset – data – has become a target in the search for a more profitable organization. But, the dynamics of data management are more chaotic than ever. Data is now collected and saved from every conceivable source – internet applications, front-office and back-office systems, trading networks, social media – and this complexity requires companies to have a sophisticated, deliberate process for managing this vital information. After all, data holds the key to sales, marketing, customer support, production and other initiatives. Without an accurate view of customers, products, materials, locations and assets, how can a company compete in today’s marketplace?

Because of these factors, the need for data management has never been higher. This paper will explore a new methodology for integrating data management principles into the organization. Through this proven lifecycle for data management, companies have the ability to create more accurate, integrated and controlled data to support every part of the business.

The Business Case for Data Management

In a 2009 Gartner survey, the research firm found that the average organization loses $8.2 million annually from poor-quality data. Further, of the 140 companies surveyed, 22% estimated their annual losses resulting from bad data at $20 million – and 4% put that figure at a staggering $100+ million.

For companies who are facing data management problems, the primary question seems to be, “Where do we start?” As with any corporate initiative, the best starting point is to set measurable business goals. After all, no one has ever managed, moved or standardized data for the sheer thrill of the task. There must be a business reason to conduct any data management initiative. At the highest level, there are three primary reasons why organizations perform any business function: to stay out of trouble, to make money or to spend less money. To translate that into more business-friendly terminology, these three reasons are:

• Governance, Risk and Compliance – Better data creates a more accurate view of the organization to help understand when the company is at risk from failing to meet regulatory requirements, complying with industry standards and other external and internal pressures.
• Business and revenue optimization – Better data makes the company more profitable by improving business processes that support every phase of the business.
• Cost control –Better data supports increased efficiency throughout the organization, driving down the costs of both direct (related to production) and indirect (personnel and other administrative) costs.

The key for data management professionals is to tie the data management efforts to each of these business objectives. The next few sections will discuss how data management technologies and processes map to these broader business objectives.