Today, every company is data-driven. Or at least pretends to be. As in the past, business decisions are not made on intuition or anecdotal patterns. Businesses’ most important decisions are now made using concrete data and analytics.
Machine learning and artificial intelligence are becoming more important tools for companies to make crucial decisions. There must be an open discussion about the quality of the data being used by these tools, including its completeness, consistency, and validity as well as timeliness and uniqueness. Machine learning (ML) and AI-based technologies will only provide the companies with insights that are as good as their data. When it comes to data-based decision-making, the old saying “garbage in and garbage out” comes to mind.
Poor data quality can lead to greater complexity in data ecosystems and poor long-term decision-making. Poor data quality is responsible for approximately $12.9 million in annual losses. Data volumes continue to rise, Businesses will face challenges invalidating their data to address data quality and accuracy issues It is crucial to understand the context in which data elements will be used and best practices to help you navigate the initiatives.
1. Data quality is not a one-size-fits-all endeavor
Data initiatives can be applied to any business driver. Also known as What a business wants to accomplish with the data will determine its data quality. Multiple business units, functions, and projects can be affected by the same data in different ways. The list of data elements that need strict governance can vary depending on the data user. Marketing teams will need to have a validated and highly accurate email list, while R&D will be focused on quality feedback data.
If a team is closest to a data element, it will be the best to determine its quality. Only they can recognize data in its context and assess its accuracy based on how it is used.
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2. What do you know Can hurt?
Data is an asset for any enterprise. But actions speak louder than words. It is not possible for everyone in an enterprise to ensure that data accuracy is maintained. Users will not understand the importance of data governance and quality if they don’t recognize it. Or, you don’t place them in the order they should be. They won’t make an effort to anticipate data problems from mediocre entry or to raise their hand when they discover a data problem that needs to be resolved.
To foster accountability and improve data quality, it might be possible to track data quality metrics as a performance objective. Business leaders should also champion the importance and value of their data quality program. Business leaders should discuss the practical consequences of poor data quality with their key team members. Incorrect reports that contain misleading information for stakeholders can lead to penalties or fines. Organizations can improve their data literacy to avoid making ill-informed or careless mistakes that could lead to a loss of the bottom line.
3. Do not attempt to boil the ocean
It’s not practical to solve a long list of data quality issues. It is not an efficient use of resources. There are many data elements in any organization. This number is increasing exponentially. Start by defining the Critical Data Elements of an organization (CDEs), which are data elements that are essential to the main purpose of a business. Each business has its own CDE. CDEs that include Net Revenue are common for all businesses. This is important for reporting to investors, shareholders, and others.
Every company will have different business goals, operating models, and organizational structures. Therefore, CDEs for each company is different. CDEs in retail, for instance, might be related to sales or design. Healthcare companies, on the other hand, will be more concerned with ensuring that regulatory compliance data is accurate. This is not a comprehensive list.
Business leaders may consider asking these questions to help them define their CDEs. Which data are used in these processes? These data elements are used in regulatory reporting. These reports will be audited. These data elements will be used to guide initiatives in other departments of the company.
Also read: Data Analyst: What it is and How It’s Work
4. More visibility = more accountability = better data quality
Companies can increase their value by knowing where and who has access to them, as well as how they are being used. Without proper data governance, a company cannot identify its CDEs. Many companies have difficulty defining their ownership of their data stores. It is important to establish ownership before adding more data sources or stores.
This will ensure that quality and utility are maintained. Organizations should also establish a data governance program that clearly defines data ownership and holds people accountable. You can use a simple spreadsheet to determine who owns the data elements, or you can use a more sophisticated data governance platform.
Organizations should model their business processes in order to improve accountability. They must also model data structures, data pipelines, and how data is transformed. Data architecture is the process of modeling an organization’s physical and logical data assets, and data management resources. This type of visibility is essential to addressing the problem of data quality. Without visibility into the “lifecycle” of data (when it was created, how it was used/transformed, and how it was outputted), it’s impossible for data quality to be guaranteed.
5. Data overload
Even though data and analytics teams have created frameworks to prioritize CDEs and categorize them, there are thousands of data elements they still need to validate or remediate. Each data element can need one or more business rules specific to its intended use. These rules can only be assigned to business users who work with unique data sets. Data quality teams will need close collaboration with subject matter experts in order to identify the rules for each unique data element.
These data elements can be very dense even when prioritized. Because they have to manually write a lot of rules for many data elements, this can lead to overload and burnout in data quality teams. Organizations must have realistic expectations about the workload of their data team members. To reduce manual work, they might consider hiring more data quality staff and/or investing in tools that leverage ML.
Data is not just the new oil, it’s also the new water. Even though organizations can have complex infrastructures, if the water or data running through them isn’t safe to drink, they are useless. People that need this water must have easy access to it, they must know that it’s usable and not tainted, they must know when supply is low and, lastly, the suppliers/gatekeepers must know who is accessing it.
Access to clean drinking water is a benefit to communities in many ways. Likewise, data quality frameworks that are mature and deepening can help to protect data-reliant programs and insights. This will encourage innovation and efficiency in organizations all over the globe.
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