The Data Intelligence Gap: Part One

There is a huge chasm in many corporations today, one that hurts companies by keeping them from revenue, more profit, and better operating efficiency. The gap, of course, lies in corporate information.

On one side of the gap lies corporate data, which may contain anything from unintelligible garbage to very valuable data. However, it is often very challenging to identify the difference. On the other side of the chasm are business users, ever needing stronger corporate intelligence, longing for ways to stimulate corporate growth and improve efficiency. On this side of the chasm, they know what information is needed to make crucial decisions, but are unsure if the data exists to produce accurate information.

Data needs standardization, organization and intelligence, in order to provide for the business.

Companies often find themselves in this position because rapid corporate growth tends to have a negative impact on data quality. As the company grows and expands new systems are put in place and new data silos form. During rapid growth, corporations rarely consider the impact of data beyond the scope of the current silo. Time marches on and the usefulness of data decays. Employee attrition leads to less and less corporate knowledge about the data, and a wider gap.

So, exactly what is it that the business needs to know that the data can’t provide? Here are some examples:

What the Business Wants to Know	Data needed	What’s inhibiting peak efficiency
Can I lower my inventory costs and purchase prices? Can I get discounts on high volume items purchased?	Reliable inventory data.	Multiple ERP and SCM systems. Duplicate part numbers. Duplicate inventory items. No standardization on parts descriptions and numbers. Global data existing in different code pages and languages.
Are my marketing programs effective? Am I giving customers and prospects every opportunity to love our company?	Customer attrition rates. Results of marketing programs.	Typos. Lack of standardization of name and address. Multiple CRM systems. Many countries and systems.
Are any customers or prospects “bad guys”? Are we complying with all international laws?	Reliable customer data for comparison to “watch” lists.	Lack of standards. Ability to match names that may have slight variations against watch lists. Missing values.
Am I driving the company in the right direction?	Reliable business metrics. Financial trends.	Extra effort and time needed to compile sales and finance data – time to cross-check results.
Is the company we’re buying worth it?	Fast comprehension of the reliability of the information provided by the seller.	Ability to quickly check the accuracy of the data, especially the customer lists, inventory level accuracy, financial metrics, and the existence of “bad guys” in the data.

Again, these are some of the many reasons where data lacks intelligence and can’t provide for the needs of the corporation. It is across this divide that data quality vendors must set up bridges... and it is this chasm that data governance teams must cross.

We’ll cover that in part two of this story. I’ll cover what kind of solutions and processes help the data governance team cross the great data divide and bring data intelligence to their organizations.