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THE DATA DOWNLOAD

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Webinar Q&A: ROI on Data Quality

Posted by Sally McCormack on Aug 31, 2018 11:39:11 AM

1. What areas do companies typically focus on when they want to create a pilot program to demonstrate ROI? Are there specific areas that may have more business relevance?

Companies will focus on areas where they see a specific business issue that needs to be addressed. Often, they will focus on one data domain where they are incurring costs that are related to having bad data as that is easiest to quantify. This will differ across organizations and industries as each company has their own business priorities. The most common focus areas are the Customer and Product domains, as having high quality customer and product data will enable better business decision-making, which in turn increases revenue.

2. How long does a typical Data Quality (DQ) implementation take?

A typical DQ implementation will depend on a number of factors including the in-scope data domains, number of data quality rules, the complexity of the rules, the number of resources working on the implementation, and the tools being leveraged, etc. We have seen implementations range from as little as three months to multiple years.

3. How many people does a typical implementation require?

The number of people involved in a typical implementation will depend on what is in scope. For smaller implementations, there may be a couple people on the IT side, but involvement from the business would be required and depend on the number of data stewards for the in-scope data domains.

4. How do we calculate the cost of a data quality solution so we can present a number along with the ROI? 

To determine the cost of a DQ solution, consider the people, process, and technology involved to support the overall solution. The technology costs include the DQ software and the infrastructure to support the tool (e.g. servers, DBs, etc.), the licensing and maintenance costs, and the cost to implement. The people costs include salaries of resources required to support DQ (e.g. analysts, stewards, developers, administrators) as well as any additional resource costs (e.g. professional services, consultants) necessary to initially set up the DQ tool. Process costs can be calculated based on the training required to learn how to use the DQ tool as well as training people in the overall DQ approach in the organization.

5. Who can I work with to define the ‘cost’ of bad data, like returned mail or rework?

The business process owners can typically determine the cost of bad data in the organization. For returned mail related to advertisements, the marketing department will likely have the number of returned mail items and the cost of sending out ads. Each department in an organization should understand costs associated to people, process, and technology of running their business, so it’s important to work with business stakeholders to define what that cost means to them and understand the impact to the business if these costs are not managed.

6. Who is responsible for tracking savings? Where in a DQ program is this addressed?  

Typically, a DQ program will fall under a larger Data Governance (DG) organization. With that, DQ metrics that are defined by the DG office are often reported to stakeholders to show how well the DQ program is doing over time. These metrics will be defined based on business value and will include the potential cost savings of having high quality data. They are maintained within DQ reports, but ultimately are defined by the business owners.  

7. How do you put a cost on reputation loss?

The costs associated with reputation loss can be calculated on the revenue loss of doing less business with potential customers. If the company is publicly traded, this can also be seen through a decrease of shareholder value. They can also be calculated based on increased operating costs of having to improve their brand image. For example, organizations may spend more on marketing or public relations to help improve their reputation.

 

View the webinar replay

 

Topics: Data Quality

Written by Sally McCormack