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Companies face many challenges with ensuring data quality

For companies looking to leverage the use of data into well-informed business decisions, the challenge of ensuring data quality is tremendous. Many firms are compiling banks of information on every consumer they can find, including financial data as well as basic contact information such as street addresses, phone numbers and email addresses. It may seem like a simple matter to install address verification software and zap all inaccurate data, but ensuring quality is a complicated process, and it's one that poses several challenges for big and small companies.

Andras Fancsik, specialist leader of advisory services at Deloitte, recently wrote a guest article for Information Management that analyzed the trouble with data quality that companies face on a daily basis. Analyzing data for quality is not as simple as merely firing up a program and sifting through spreadsheets - the process must have support, both financially and log?istically, from higher-up executives at companies.

"Data can be examined and analyzed, which can provide the business greater insights, lead to better, faster decisions and uncover potentially hidden data patterns and relationships," Fancsik wrote. "However, data is only useful if it is kept organized and clean. Therein lies one of the many challenges associated with collecting and leveraging data. Improving and preserving the quality of data can be a daunting challenge. For most organizations, it is an investment of a magnitude that requires buy-in and approval at the executive level."

In order for companies to successfully make data quality a priority in their operations, there are many preliminary steps they must take. Here are a few.

Include data quality in the planning process
Whenever a company undertakes a data initiative, they should make it a priority beforehand to check its quality. There is always a temptation to leave data quality as an afterthought - most companies focus more on collecting data and analyzing it, forgetting to budget any time or money for checking its accuracy. This step is vitally important, and it must be planned for in advance.

Lyn Robison, a research vice president at Gartner, said that data analysts must work to convince their bosses of the importance of data quality.

"Spelling out to corporate executives how data quality is central to the business strategy by showing concrete anecdotal and empirical evidence is a good idea," Robison told TechTarget. "You would think that they would already connect these dots, but they have a lot of things on their minds."

Establish an agreement on importance of data quality
Companies should begin a business project with a clear idea for how important data quality is to them, and that platform should be agreed upon by all workers involved at all levels. If an executive has one concept of his or her business' vision on quality, and an analyst plugging away at a spreadsheet has another, those conflicting viewpoints can torpedo a project. A business should have an agreed-upon definition of "high quality" data, including exactly how accurate they need it to be and how much time and money it's worth to reach that level of accuracy.

Remain vigilant on data quality in the future
A common mistake is to think that just because a company has completed devising a plan for establishing data quality, that means their work is done. Not so - data analysts must always be focused on the process of ensuring quality, as new data is always pouring in and new errors are always arising. Data quality is not a one-time thing - it's a constant struggle to keep all information as accurate as possible.