Introduction:
The importance of data quality is all too often overlooked within many organizations. But if more were aware of the effects of inaccurate data, this would not be the case. According to a recent Gartner report on the effects of poor data quality, the average organization loses $8.2 million annually, while 22% estimated their annual losses at $20 million and 4% estimated losses were $100 million[1].
This financial loss is compounded by the following potential impacts to your business:
- Poor customer service
- Inaccurate forecasting
- Drop in Sales pipeline
- Erroneous inventory levels
- Loss in Sales/Revenue/Margin
Claudia Imhoff, a recognized thought leader in data analytics, states that, “Much of this loss is due to lost productivity. When we have to compensate for inaccuracies and have to work around figuring out how to deal with poor data quality – that’s a loss in productivity.”
Now what?
Now that we are aware of the potential impacts, let’s examine how to counter and prevent the maladies caused by data quality. Some industry best practices include:
Develop standards
Organizations with a high quality of data typically have standards and guidelines in place. This includes formatting, structure, completeness, consistency, etc. Develop a standard before embarking on a data cleanse campaign. The standard will not only guide the data cleansing team into identifying suspect records, but also make others aware of expectations.
Be proactive
Don’t wait for your data to become dirty -- be proactive and maintain. Creating a report or dashboard can help with visualizing the progress of your efforts. It can help you trap the “dirty” records as they come into your database. This proactive approach will also create real-time coachable opportunities for those that enter the data.
Obtain senior level support
Having senior level support can help remove the common objection. But more importantly, the senior level employees will most likely be impacted the most by inaccurate data. By having these senior level employees involved in the data quality campaign, they will have a first-hand perspective on the importance of a high level of data quality.
Start small
Pick a strategic area of the business where the data cleansing will take a smaller amount of effort, but create the greatest level of return. Once the process is shown as successful, this will hopefully create moment and support for the rest of the organization. Some may become overwhelmed if looking at the task in its entirety -- bite off a small piece to start.
Conclusion
The process of cleaning up your data will be laborious. The payday will be when you and your team arrive at the point of maintaining a handful of records daily instead of thousands of records. Information is power, but only when the information is accurate. At the end of the day, it is imperative to remember -- data is our friend!!
If you have questions about how Webfortis could assist your organization's data quality efforts, please email info@webfortis.com or click 
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[1] http://www.melissadata.com/enews/dataadvisor/articles/062011/1.htm
