Best Practices in Data Resource Management
Takeaway notes from ”The Learning Curve” webinar. By Aliza Bornstein, copywriter, Melissa Data

Many people today still confuse the words ‘data,’ ‘information,’ and ‘knowledge.’

‘Data’ is the individual facts that are out of context with little meaning, such as “$123.15.” ‘Data in context’ are individual facts that are wrapped with some type of meaning, such as “123.15 is the account balance at noon on January 12, 2001.”

‘Information’ is a set of data in context, relevant to one or more people at some point in time. Information must have a relevant component, and a time component, to the information.

‘Knowledge’ is information retained by individuals and then combined with experience.

As we look at this sequence of definitions, it’s obvious that data is the foundation, and that’s what we have to manage.

Business information demand is an organization’s continuously increasing, constantly changing need for current, accurate, integrated information, often on short notice, to support its business activities.

The impact we see on most business today is that this information demand is not being met. This is happening in both public and private sector businesses. Business decisions are less informed, both citizens and customers are being impacted, and opportunities are being missed.

Looked at in detail, the problems are high quantities of disparate data in most organizations. The data is not thoroughly understood, the data source is not formally managed or readily available, and data quality is low.

Disparate data is data that is essentially not alike, or is distinctly different in kind, quality or character. They are unequal and cannot be readily integrated to adequately meet the business information demand.

There are four basic problems found in disparate data:

1) Unknown Data Existence
Organizations at large are not aware of all data at their disposal, and usually are not inventoried.

2) Unknown Data Meaning
Content and meaning of data are not fully known in the organization, or thoroughly understood.

3) High Data Redundancy
Data is highly redundant and inconsistent across an organization. The average redundancy factor of ten for any organization that’s of any size, that’s been in business for any length of time. That means that each business fact exists, on the average, ten times throughout the data resource.

4) High Data Variability
Data is highly variable in format and content. In large organizations, there’s an average factor of 15 to 20 in the data variability for any one business fact.