It can be said that many of today’s businesses are “data rich” in that they have more raw data than they know what to do with. This statement, however, is usually followed by noting that the data rich businesses are also information poor, meaning they are not getting useful information from their data. This has many negative consequences for businesses both large and small in today’s competitive landscape.

Why are Businesses Data Rich and Information Poor?

Since the proliferation of the internet, computers, and EDI, businesses are amassing a wealth of data at lightening speeds. To explain this business phenomenon, we must examine several factors. These factors include

The cost of both data collection and data storage has exponentially fallen, and continues to fall. Since the incremental cost of gathering each new piece of data on a given process or transaction is either zero or very close to zero, firms naturally endeavor to collect as much data as possible in hopes of being able to make better decisions.

Since businesses have largely digitized most of their business processes, implemented EDI into their processes, and have completely automated many processes, the ability to capture and collect various data at every stage of a business process has become very cost effective. Many businesses collect hundreds of data for a single transaction.

Legislation also requires firms to collect and store various data about transactions and processes. This alone has led to massive amounts of data collection. Although the collection of data may be mandatory, the competitive business landscape is forcing companies to make better use of the data in order to compete in today’s marketplace.

Because of this increased reliance on data, there may be a misconception that firms make better decisions with more data. The fact is, however, that this data must be processed correctly in to information that is useful, and must be combined with human knowledge and expertise to be used for decision making.

While companies possess vast amounts of data, they often lack critical organizational alignment with their information systems. This means that many decision makers may not have access to the data they need, or may not receive it in a format that is optimized for their specific use.

For example, a firm may collect data about its labor costs over time by recording weekly payroll numbers. A report could then be generated to show totally payroll costs over time via a graph. This data may not be useful “information” if the decision maker is trying to see if costs are increasing in relation to revenue. To glean this information, the decision maker would also need sales data over the same time period, preferably on the same report in the same format. Profitability data over time may also be useful here. If this is a regular task for the decision maker, his or her input during a data warehouse or DBMS implementation would be critical. Since data must be processed to become information, and since the processing will depend on the information user’s need, there must be collaboration between IT and business leaders. Quite often there are time constraints that prevent this collaboration, and so firms remain rich with data, but rarely process it or convert it successfully to optimized and useful information.