In Data Quality

This is the second post of a two-part series. You can find the first here.


Impact of Poor Data Quality:

According to the Data Warehousing Institute, organizations lose around 600 billion of dollars annually due to poor data quality. Surprisingly, a large portion of these losses are due to the impact of poor data on productivity. Compensating for inaccurate data and solving structural problems cost significant amounts of staff time. Reliable data, on the other hand, improves decision-making and reduces company risk when instituting new initiatives based on that data. Good data also protects customers and helps insure their satisfaction with your product or services.

Why There Are No Small Data Quality Problems:
A single piece of corrupt, outdated, or incorrect information can multiply at breath-taking speed, polluting other data that it touches within your system. Analysis and reports based on that data can be flawed and lead to bad company decisions. And the longer that bad data lives and moves in your information systems, the more damage it does.

A Proactive Approach:

In a business climate where flexibility and quick decision-making is essential for companies that wish to survive and thrive, organizations can’t afford to take a reactive approach to data quality. Costs associated with bad data add-up quickly, and like the damage mentioned above, costs only increase the longer you delay implementing a data quality improvement program. Consider the “1-10-100 rule.” It costs $1 to verify a record is being entered correctly, $10 to clean it up once it’s already been entered, and $100 in lost revenue and productivity if it’s left untended in your database.

Key Elements of a Data Quality Strategy.

  1. The ability of your data system to protect itself from outside data:  It’s important to remember when you approach the issue of data quality that much of your data can originate from sources well outside of your carefully protected information systems. Partner companies, commercial data sources, events, or any number of additional lead generation activities can produce mountains of information added to your marketing database. If you don’t protect your system from bad external data, you create an unstable and risk-prone information system.
  1. Controlling quality of data in real-time:  Data constantly flows into your information system. While it’s critical to clean, maintain, and control bad data that’s already in your system, it’s equally important to protect your system from bad data that is constantly flowing into it.  E-mails, manual data entry, the Internet, customer input and information exchanges with partners and customers pour into your information system. Advanced software tools help you catch and scrub incoming corrupt information as it swims up the data stream into your system – but only if you have it in place.

So, how do you you ensure your data remains pristine and campaign-ready? Be sure to comment and let me know…