In 2021, 75% of companies who made moves to improve the quality of their data exceeded their annual business goals. Data quality is clearly fundamental to the well-being of your business.
If your data is wrong, you can’t understand your customers, establish competitive pricing models, or strategize for growth. If you’re ready to improve your company’s data quality, start with a series of clear, measurable goals. Breaking big changes down into smaller steps makes them easier to implement, and data quality management is no exception.
If you’re not sure where to start, we’ve got you covered with data quality improvement steps for the next 30 days.
Step 1: Identify the problem(s)
As with most problem/solution scenarios, the first step to resolving data quality issues is identifying the “right” problem. There are several possible reasons for low-quality data, including ill-managed duplicates, human error, inconsistent or incomplete records, and inefficient data quality management.
Once you know what’s wrong, you can begin narrowing your focus to finding the source and strategizing solutions.
Step 2: Find the source
Once you’ve identified the right problem(s), you need to figure out what’s causing them. This is where the dimensions of data quality come into play. Once you understand these dimensions, you can identify where your problems lie and how they occur.
Quality data is:
- Complete. What percentage of your company’s data is complete? Do you have guidelines in place for qualifying a record as complete? Qualifications for completion typically vary from one business to the next.
- Unique. What percentage of your business data is unique? Duplicate records can cause problems when they aren’t managed well. Your data should not include unknown or unnecessary redundant records.
- Timely. When was your data recorded? Old records may be outdated or inaccurate. Company data is typically less accurate and relevant over time. When was the last time your records were updated?
- Valid. What percentage of your business data is valid? Your data only has value if you can use it to enhance customer interaction and other business activity. Valid data is usable data.
- Accurate. What percentage of your business data is accurate according to your company’s established rules? Errors contradict the real-world information your data is meant to represent.
- Consistent. What percentage of your company data is consistent? Are record formats and completion requirements established and regularly met? Is your business data consistent between sets?
Review these dimensions of data quality in the context of the problem you’ve identified. A consistency issue can result from a lack of unified understanding regarding what data should be input in what format. Duplicate data may be caused by human error, a bug in your data management system, or both. Whether it’s an issue of policy, mechanics, or simple human error, once you’ve found the source, you can begin to address it.
Step 3: Strategize solutions
You’ve identified the right problem and verified the source. Now, you must determine the resources necessary to solve it.
Do you have the necessary data quality management expertise in house? A small issue with your data collection process (e.g., a repeated field within a record) is simple enough to fix, but a total data quality management overhaul is complex and unique to your organization’s business — and business data — needs. It takes time and technical knowledge, and it’s difficult to conduct business as usual and manage a project of this scope.
Some data quality issues can be addressed with policy changes to establish a defined set of rules for recording crucial business data. Automation is also a possibility for verifying data quality and ensuring consistency. Staying on top of potential issues — before they become major problems — also helps avoid a longer, more arduous, or expensive data quality repair process later.
Step 4: Expert help
Data quality drives your company’s business and future growth, but resolving data quality management issues can be overwhelming when you don’t know where to start. Ignoring problems will only make them bigger and harder to solve down the road. Follow these steps in your next 30 days, and make solving your data quality problems easier with expert assistance.
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