For most of the US, taxpayers are taking yet another breather after the completion of this year’s tax season, and with the heightened topic of security your personal information continues to be at risk. However, it’s not just personal information that is at risk – it’s also corporate and federal information. What’s the answer? Data relevance!
In a 2012 CNBC article titled, IRS Pays Out Billions in Fraudulent Refunds, it was reported that there was a potential for $21 billion in fraudulent refunds to be paid out over the next five years. The inspector general said it found one residential address in Lansing, Michigan that was the source of an astonishing 2,137 tax returns, and to which the IRS directed more than $3.3 million in potentially fraudulent refunds. Hmm, it looks like they were sending to an “accurate” postal address but did it make sense for one household to file 2,137 tax returns?
Data relevance is often an ignored aspect of data quality. The common method to achieve data quality is thought to be the implementation of data quality software to get “accurate” data. But what if the information recorded in applications satisfies field requirements but doesn’t make sense? The result is erroneous data in reports and decisions that can be misdirected, such as in the example of fraudulent refunds.
There are five major questions organizations should ask themselves in order to achieve data relevance:
- Does your data support your desired business outcomes today?
- Are you able to determine the work and process necessary to achieve Data Readiness?
- Have you established a system to capture and reflect activity, recommend what to do next, and show you the status of your data quality?
- Have you established a remediation plan to support your desired business outcomes?
- Have you created an ongoing program to continuously monitor and remediate your data issues based on relevancy?
Keep these key directives in mind as you establish a path towards data relevance. Although there can be many ways to reach data relevance, success is dependent on three things: people, process and technology. It is critical to have expertise from people who have acquired skills in and knowledge of data relevance and who understand how to apply it to the task of reaching organizational goals.
In some cases, data relevance issues can be easily fixed by the discovery of incorrect entries in certain fields, and other times it is a very complex and tangled web of erroneous data found downstream from the initial point of entry that causes the problem. Therefore, it is important to create a process that works across both your IT team and business users, so they can solve data quality issues in tandem. Empowering your team with the right process and solution to access the correct data, isolate it and remediate errors on notification will help keep your data relevant and avoid multi-million dollar errors.
For more information on how to ensure your organization’s data is relevant, read our paper, Five Tips to Achieve Data Relevance.
The Road to SAPPHIRE NOW Blog Series features a series of blog posts on data quality, information governance and master data management from BackOffice Associates industry experts. Check back weekly for new content related to the 2014 SAPPHIRE NOW and ASUG Annual Conference and join us at the event from June 3-5 in Orlando, FL at Booth #224.
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