Contradicting sales reports, inaccurate and outdated customer information, incomplete transaction records and redundant data are all telltale signs that you have dirty data on your hands.
Poor data quality, or dirty data, can affect performance and productivity, especially if business decisions are being based on it. Industry experts often say that data is a company’s biggest asset. Therefore, there is significant business value associated with keeping corporate data clean.
Up to 75 per cent of information workers admitted they have made wrong business decisions because of inaccurate, incorrect or incomplete corporate information, according to a recent survey by Harris Interactive. It polled employees from the U.S., Great Britain, France and Germany.
“Information workers”, as defined by the surveyors, are employees who use data from various applications, such as spreadsheets, business intelligence reports and executive dashboards, to make decisions.
Often, customers’ perceptions can be key indicators of the level of a firm’s data quality.
“In a customer-facing type of business, there tends to be a general feeling among customers that the enterprise (they’re dealing with) doesn’t know them or understand them,” explained Royce Bell, CEO of Accenture Information Management Services in London.
Consumer-focused enterprises today are always trying to find ways to intelligently predict the behaviours of specific group of customers based on their past activities, said Bell. This can prove difficult when working with unclean data and may lead to misleading information about the customers.
Poor data quality also has a significant impact on employee productivity, according to the survey. Information workers in the U.S. spend an average of 12 hours per week, or 30 per cent of their workweek, verifying the accuracy of the data needed to make business decisions.
Another sign of poor data quality is when employees are constantly raising questions and concerns over the trustworthiness of their data, said Kristin McMahon, audit marketing manager for enterprise information management at business intelligence software developer Business Objects in Vancouver.
“There’s nothing worse than going to a meeting with your vice-president of sales where you’re showing you have $45 million for the quarter and he has a report showing $65 million for the quarter. Whose report is right?” McMahon said.
Examples of dirty data can range from bad addresses, non-standard firm names, multi-format person names, incorrect account value, missing data and duplicate records. For instance, the different format for entering dates – with dashes, slashes or periods – may result in confusing information when merging data from various data sources, explained the Business Objects executive.
In resolving data quality issues, organizations should be careful not to address all their problems in one shot, cautioned Accenture’s Bell. “If [companies] are clever about it, [they] will sit back and think a minute before diving in and saying, ‘Clean everything.’”