Are You Making Decisions Based on Bad Data?

Vasu Sattenapalli, CEO at RightData, discovers the symptoms of poor data quality and practical strategies to achieve reliable, high-quality data for confident decision-making.

With every company, regardless of size or industry, becoming progressively more data-driven, poor data quality is becoming more of a threat. After all, decisions made based on bad data can have a major destructive impact. So, with more tools than ever before, why is good data quality still so hard to achieve? For some, it may even feel like data quality has grown more unattainable as the amount of data they’re using has increased along with the complexity and number of different forms their data can take. With that in mind, how can you turn the ship around and achieve the kind of trustworthy, high-quality data that results in positive business decisions? 

4 Key Symptoms of Bad Data

To begin correcting your data quality issues, you need to be able to recognize when you’re using bad data. Sometimes, this is straightforward. For many companies, if you’re using bad data, you know it. However, other times, there are subtler signs that allow poor data quality to persist for years while it slowly erodes your decision-making confidence. When evaluating data, I look for four significant symptoms that indicate that data quality is suffering.

1. Errors and inconsistencies: This symptom is clear-cut. When you see errors, you know you have poor data quality. Records with conflicting or contradictory information, errors like incorrect dates or nonsensical values, duplicate records, and unusually high or low values. These can skew analysis and reporting and negatively impact confidence in your data.
2. Trouble integrating multiple sources: When you need help to integrate data from multiple sources, the disjointed result makes it hard to get a clear, comprehensive view of your data as a whole. For instance, if data is complete and critical information is included in the dataset, it is possible to perform meaningful analysis or answer essential questions. Or perhaps there are discrepancies between different data sources, and because your integration system is ineffective, it doesn’t flag that there are errors and mismatches.
3. Lackluster feedback from users and customers: Sometimes, your primary indicator of poor data quality will be direct feedback from others. Users or stakeholders may report discrepancies or issues with the data they are working with, such as incorrect calculations or unexpected results, which can erode trust in decision-making processes. Suppose customers or clients complain about data-related issues or discrepancies in reports or services. In that case, that’s another strong signal of problematic data and a sign that you’ll also need to do some damage control to rebuild that relationship.
4. Frequent data cleansing: The last symptom I look for is frequent data cleansing, and companies are sometimes surprised to see this flagged as an issue. They may think that their data quality is great precisely because they take data cleansing so seriously. In reality, needing to spend a lot of time cleansing data means that the data is error-prone when it comes in. Any decline in the consistency of your data cleansing efforts means that those errors can get through and begin to compromise data quality.

See More: How Decision Intelligence Solutions Mitigate Poor Data Quality

How to Begin Achieving True Data Quality

Now that you’ve recognized some signs of poor data quality, it’s time to figure out how to address it. Generally, I recommend a three-pronged approach to correct current data quality issues and ensure that you’re using high-quality data for the long term. 

1. Give assets weight during quality checks: Every asset we have needs to undergo quality checks, but only some assets should have equal weight. Say that overall, my platform quality score is 95%. When I dive in, I see great scores across multiple functional domains, management, HR, and suppliers, but sales have a quality score of 60%. That’s a huge issue; sales data being inaccurate has a direct impact on the company’s ability to close deals and generate revenue successfully. Now, imagine that you see great quality scores across the board, but HR is the domain that has scored 60%. This is not ideal, but a low data quality score for HR doesn’t have quite the same sweeping repercussions as a low score in sales. 

I often see companies not using weights when conducting data quality checks, which minimizes serious issues and grants outsize importance to minor ones. By classifying different assets into domains and functional domains and assigning weights for each one, companies can put into practice a more well-rounded way to evaluate data quality and arrive at real data quality scores.

2. Adjust your data governance processes: Nearly every company has some sort of data governance process in place, but only some continually adjust these processes as the technology and data they use evolve. New tools and platforms for data collection, storage, and analysis are constantly being developed. As new data sources emerge, we also see data formats change, the volume of data increase, and the speed at which we receive data accelerate. 

It’s ill-advised or even impossible to keep hard-and-fast data governance rules. Regularly updating data governance processes will ensure your company remains aligned with the current data landscape, accommodating new data sources and technologies while maintaining data security and quality.

3. Use systems with better data quality processes built-in: Data-driven decision-making relies on accurate and reliable data. And sometimes, regardless of how much work has been put into it, your current ecosystem of tools isn’t capable of giving you that kind of data. It could be because of a systemic lack of integration, or it could be because of differing data quality standards between tools. These are fundamental issues that must be corrected to begin improving data quality.

Fortunately, there’s no need to start from scratch with your system. I recommend exploring the possibility of using a data product to promote trust by implementing data quality automated software, providing strong data lineage, and establishing data governance policies. These bolt-on tools serve as a single platform for data ingestion, unifying, structuring, cleansing, validating, transforming, and loading data. This results in more comprehensive and accurate datasets with sustained high data quality. 

There is no easy button to guide the organization when its data goes awry. And with the scale of data increasing so significantly, it may feel like true data quality is almost impossible to achieve. However, by being aware of the symptoms of poor-quality data, companies can take action quickly and address deeper issues, putting them back on the path to high-quality data and greater confidence in decision-making.

How can you enhance your data quality? Why is it crucial for your business decisions? Let us know on FacebookOpens a new window , XOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

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