I had the privilege of attending the Gartner Data and Analytics Summit a couple of weeks ago. It’s a great opportunity to see all the new technology that is out there and to hear all the latest ways that people are utilizing data. It seems that everyone wants to get more value from their data, and take advantage of AI and advanced analytics. Over the course of the conference, the two most common statements I heard repeated were, “Data is an Asset,” and “We were evaluating our numbers, and the numbers don’t lie.”
But what if I told you that data is often a liability, and numbers lie all the time? It’s true. Without a solid data strategy in place that utilizes people, process, and technology, your numbers can easily be inaccurate, and your data can quickly become a liability rather than an asset. Let’s take a deeper look into how this happens.
I’ve talked to multiple people across many different industries who all have similar issues. The story typically is: “We had an executive meeting and needed to provide numbers. Multiple LOBs were presenting, and each had different data. We all got our numbers from disparate sources or had a different context around the numbers we provided.” I’ve heard examples of this story across every industry that I have worked with, and they all share the same root cause: a lack of data governance.
In order to remedy this issue, organizations need to establish a data governance program that consists of both business and IT representation, that defines data terms and other artifacts, and that provides clear context around the meaning of those terms and artifacts. Those definitions and artifacts must be easily searchable and accessible to the business users. That way, everyone will have consistent definitions and context for information that is used to put together critical reports. It is also important to document the data lineage. It is critical for users to understand at both a business and technical level where the data comes from. Once definitions, context, and lineage are established, data ownership and stewardship can be assigned to maintain those artifacts and ensure the information remains updated, accessible and accurate.
Another issue that can turn your data into a liability is data quality. Most organizations have data quality tools, but they are traditionally used around the data warehouse. Despite these tools, however, most people say they can’t trust the quality of the data in their organization.
In order to establish trust in your data, think of the processing of data in terms of a supply chain. In manufacturing, controls are put in place and quality standards are measured at various points along the chain. At each point in the process, consistent quality standards are verified and upheld, even for third party suppliers of components. Why should data be any different? Data quality standards should be put in place to measure quality from external sources, along with Service Level Agreements (SLAs) to ensure your partners are providing you with good data. Once data enters the organization, it should be tracked and validated as it moves from point to point. This will assure that your end products (reports, analysis, bills, etc.) also reflect high quality data.
When establishing data governance or data quality initiatives, make sure to avoid some common pitfalls. Establish a data management strategy before going out and investing in tools that you may not need or may not be the right ones for your organization. Remember that in order to establish a successful governance program, you must involve both business and IT. Another key to note is that governance is not a project that has a start date and end date. It is a continuous program that constantly evolves and updates as people, processes, and technologies change across the enterprise. Treat data as a product by establishing a quality and controls framework around it. This will ensure its accuracy, consistency, and reliability. Following these steps will allow your data to be an asset to your organization and not a liability, building confidence in your data and trust in your results.
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