Scrub Data to Make Clean Business Choices
Clean customer information is essential for any successful company. Yet the quality of collected data is typically an overlooked aspect of business information infrastructures.
When data is “dirty” — meaning it’s wrong, duplicated, missing or misleading — you waste money by sending direct mail to non-existent addresses, missing sales opportunities and providing poor customer service by not having the proper information at hand.
Keeping your data “clean” is no easy proposition. Customers change address, area codes and e-mail accounts regularly. A substantial percentage of Americans change addresses each year. Consumers get married, divorced and die, and as they age, their tastes, habits and needs change.
Many firms recognize this and install software to maintain the quality of their data. The adoption rate for this software is projected to run at a 20% to 30% annual rate over the next several years, according to research by Meta Group.
But plenty of companies still don’t update their business data or adequately “scrub” it to remove bad information.
Here are two categories of information you should keep up to date and accurate to ensure that you aren’t wasting time, money and effort:
1. Customer and Partner Data. Recent information about customers, partners, suppliers, supply and distribution chain members, your company’s subsidiaries, consumers and prospects.
2. Business Data. Numbers, makes, models, transactions, tax identification numbers and other types of data that are the lifeblood of business intelligence and Enterprise Resource Planning systems. Understanding what’s in stock, when to order inventory, and how much has been sold is critical to the success of these initiatives.
Rules of Thumb for Data Scrubbing
Establish processes to prevent errors, then clean current data.
Focus on the most important data, such as the needs of your best customers.
Start with critical data suppliers, insisting they provide accurate and current data.
Don’t be seduced by cleanup tools — they’re no substitute for preventing errors in the first place.
Pick software tools to solve specific problems, rather than all-purpose tools for general problems.
Common Sources of Corrupt Data
The Internet. Online customers sometimes intentionally enter incorrect data to protect their privacy.
Call centers. Operators enter abbreviated or erroneous data to save time.
Third parties. Information you buy may contain inconsistencies, inaccuracies and other errors.
Mergers and Acquisitions. Your company’s data collection rules may not conform to those of another firm.
In-House Errors. Errors creep in when several employees enter data and don’t follow the same procedures.
To ensure data is as clean as possible, keep your quality control methods as close to the actual source as possible. More importantly, remember that maintaining data clean is a continuous process, not a one-time deal.
Even the best data analysis tools do little good if the data is inaccurate. Analysis of bad information leads to bad decisions. For example, dated sales information may indicate high demand for a product. That could lead to increased production, while up-to-date data might show waning interest in that product.
The Trade Off: Choose accuracy over speed when you develop a data monitoring system.
This article appeared in Walz Group’s October 5 , 2022 issue of The Bottom Line e-newsletter, produced by Checkpoint Marketing.
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