David Loshin is known around the world as a thought leader in data quality and governance. If you’re wondering about how data quality affects your business, he’s a great resource. Loshin recognizes that data quality probably isn’t at the top of your CEO’s list of things to do.
The reason is because it’s difficult to communicate the urgency of improving your data quality to senior management. But, he has some excellent ideas on how to describe the financial value of improving your data quality.
Once you understand that issue, it’s easy to identify how data quality is affecting your business and to make a business case for improving data quality that will resonate with your C-level executives.
How Data Quality Affects a Business
Virtually all businesses have started using business information and data analytics to increase their competitive edge. Whether it’s tracking the activity of customer contacts or feeding the result of customer surveys into product development, gathering and analyzing data are critical activities in today’s business environment.
While many are using their business data in creative ways, fewer are concerned about ensuring that their data is of high quality. But, there are two key ways that data quality affects your business. There are direct financial impacts and impacts that lessen your ability to meet organizational objectives. To uncover those impacts, you can identify:
- Critical business processes
- how those processes use data
- where data comes from for those processes
- problems that have occurred in the past where bad data was the cause
- where business processes can be impacted by data and measures to describe the quality of the data being collected
Example: What Determines Your Revenue Growth?
Let’s look at revenue growth as one of the critical business processes that you’d want to consider for data impacts. Several business processes affect your revenue growth but attracting customers and retaining them are two of the key processes.
Let’s assume that your marketing department is responsible for attracting customers. You’ll probably find that to make their marketing efforts effective, they need to personalize the outreach they do. The marketing team needs to segment prospect lists. If the lead information in those prospect lists isn’t accurate, your marketing people will waste time trying to attract leads with outreach that doesn’t interest them.
The same is true for tracking data related to lead behavior to identify those leads that should be turned over to sales. If the data they use has the wrong contact information, for example, the salespeople will waste a lot of time trying to reach the lead. Having accurate and complete contact information makes an impact on your ability to attract prospects and your revenue growth.
Once a lead has become a customer, your ability to generate revenue will increase significantly if you can retain those customers. Your organization will need to be able to calculate customer value to focus on those high potential customers. If the data you use to make those calculations isn’t accurate, you’ll be wasting resources pursuing low value customers.
You’ll probably want to manage loyalty programs. If one employee adds the customer information incorrectly when the customer makes a purchase, you’ve now got duplicate customer records and your customer won’t get their reward on time. Your loyalty program won’t do its job. Even the difference between entering NY and New York in an address can be a problem.
Attrition, or customer churn rates, need to be evaluated. If employees use different reasons for why a customer went to a competitor, it will be impossible for you to track reasons for customer attrition, and you won’t be effective in resolving issues that are causing customers to go to your competitors.
Your data-dependent processes will cover more than sales and marketing. But, looking at the problems that could arise in the example above will give you an idea of how low data quality could have a significant impact on your business.
What Comes Next?
After you understand the issues, the next step is to take a look at the information flow from the point where the data is created through the business process that could be affected. It’s more effective to correct the point where the bad data is introduced rather than trying to correct the data in the downstream process. Low data quality has many causes, including:
- Data entry errors
- Incomplete data
- Ambiguous data
- Inconsistent data
- Duplicate data
- Data transformation errors
While many organizations don’t worry about the quality of their data, they still use it to make critical business decisions. It’s not something that you should ignore since it can have a significant impact on your business.
Many of the issues you’ll come across in looking at data quality can be addressed by developing an effective data governance program. Setting standards for data can go a long way to helping you overcome the problem of low data quality.
You can address problems such as harmonizing multiple data sources, ensuring data quality, and empowering employees by putting the tools required to use data to your advantage in the hands of your employees.
To learn more about how one organization managed their data quality issues with a data governance program, view our case study, “How a Data Governance Program Improved Data Quality.”
And if you have questions about your organization’s data quality and how to improve it, contact the data quality and data governance experts at Datavail for more information.
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