The Importance of Data Quality: Identifying and Improving Good vs. Bad Data

In an era where data is king, data quality is queen. Your king will fall without good data, leaving the kingdom susceptible to outside influences and intruders. Pretty soon, the cracks will deepen, and pieces will begin to crumble away. But before it gets that serious, there is hope! 

Imagine you work in one of the top 20 ranked Fortune 500 companies. Daily decisions are based solely on the data you have collected on the hundreds of thousands of customers coming through your doors. You have millions upon millions of data points to pull from and the top talent in the industry to do the pulling, but what if your data is a little dirty? What happens if the data collected daily from your sites lacks key points and parameters? What if the data stream is sending the wrong data entirely? What if…? You get the picture. 

This “hypothetical” scenario was a real-life nightmare for a recent company. A crucial piece of information about a user wasn’t coming through correctly, resulting in a multitude of unmatched users whom marketing couldn’t target. While you may be exclaiming, “How on earth did nobody catch this?!” The unfortunate truth is that such oversights are not uncommon. The significant gap between data collection and utilization can lead to severe consequences.  

Where is the data disconnect?

The disconnect lies between data collectors and users, often resulting in a mismatch between the data gathered and the actual business needs. Data collectors (think data engineers and scientists) tend to prioritize the more technical aspects of data collection. On the other hand, data users (think marketers and media teams) require specific, actionable insights for customer segmentation or campaign optimization. This rift in priorities can lead to frustrations and inefficiencies, resulting in valuable data being overlooked or underutilized due to a lack of alignment between the two groups. Bridging this gap requires effective team communication and collaboration to align data collection efforts with business objectives and user needs.

What is Data Quality?

Data quality measures how well any given data set serves its intended purpose within an organization. This is especially important for digital teams managing customer data. But what does it mean when we talk about “intended purpose”? If the data is messy, has incomplete fields, or is delivered in an inconsistent manner—for instance, mixing up entries like “United States” versus “USA”—or is full of duplicates, it can make the data highly unusable.

We want to make sure that when we’re talking about data quality, we’re thinking of data that is accurate, consistent, timely, and of high quality. A high-quality data set provides a strong foundation for efficient processes, allowing you to run workflows smoothly and reduce time spent on manual corrections.

It’s also important to recognize that regulatory compliance frameworks like GDPR and CCPA place demands on us to maintain data quality. Meeting these legal standards is essential to avoid penalties and ensure compliance.

Maintaining data quality over the long term is crucial and involves steps like conducting routine audits and investing in automated tools such as a Customer Data Integration (CDI) platform. These efforts help ensure your data remains reliable, accurate, and compliant.

How to bridge the gap 

Bringing the two sides (and the whole company, for that matter) together to create a cohesive data strategy is crucial to prevent scenarios like the one above. Creating a data strategy must start at the bottom and flow throughout the organization. Several strategies can be used to work towards a successful partnership between all parties involved in data collecting and usage. 

• Culture: Fostering a culture of collaboration and communication is crucial. Regular meetings and workshops involving both data engineers and business stakeholders can help align data collection efforts with specific business objectives. 
• Technology: Investing in tools and technologies that enable easier data access and visualization can empower non-technical users to explore and derive insights from the collected data independently. 
• Data Governance: Defining clear data governance policies and standards can ensure that data collected is relevant, accurate, and compliant with regulatory requirements, enhancing trust and usability among data users. 
• Cross-Functional Collaboration: Encouraging cross-functional teams and interdisciplinary projects can foster a deeper understanding of each other's perspectives and priorities, ultimately leading to more effective and impactful data utilization across the organization. 

By implementing these strategies, companies can bridge the disconnect between data collectors and users, enabling more informed decision-making and driving business success through data-driven insights.

Signs of Poor Data Quality

How can you spot when data quality is poor? There are a number of signs to consider; here are just a few:

The High Cost of Bad Data

Bad data can be at the root cause of many costly issues within your organization. Oftentimes, these costs manifest themselves as symptoms, and we focus on treating those symptoms instead of addressing the core issue. Bad data can impact many different aspects of your business.

One of the most significant impacts is the opportunity cost of revenue. If you’re sending advertisements to the wrong audience, missing leads, not following up with leads effectively, or your analytics process isn’t giving you the insights you need to make real optimizations, you’re losing opportunities. These missed opportunities can add up quickly and affect your bottom line.

Another area of impact is operational efficiency. Wasting time resolving issues and manually fixing large datasets not only drains productivity but also affects morale. Most people in operations and IT roles want to spend their time innovating and being proactive, not reacting to bad datasets. It’s frustrating and inefficient for everyone involved.

Bad data also impacts your interactions with customers. If your communications are misaligned, it can damage your reputation. Customers notice when things are off, and this can lead to higher customer churn. Maintaining accurate, high-quality data is critical to providing a seamless customer experience.

Lastly, bad data affects your organization’s preparedness for future trends and technologies. Tools like artificial intelligence (AI) and machine learning (ML) rely on high-quality datasets to function properly. Bad data can lead to misalignments in how these tools are set up, causing problems like hallucinations or implicit biases that are difficult to fix. High-quality data is the foundation for leveraging AI and ML effectively in the future.

Bad data doesn’t just create isolated problems—it touches every aspect of your business. From lost revenue to inefficiency, customer dissatisfaction, and future innovation challenges, the costs are significant. Addressing data quality now sets the stage for long-term success.

Making it easy

With so many pieces of the data puzzle, getting lost in the pipeline is easy. What’s not easy is clawing your way out of a troublesome situation that could get your company in trouble or having a seriously ineffective marketing team dragged down by inoperable data that is only ineffective because of the data they have to work with. 

Revamping your entire data strategy doesn’t sound like a fun way to spend a quarter, but if it could bring the business a considerable amount of extra cash flow from successful marketing campaigns alone, then it is likely worth it. 

The key considerations here are what data you are collecting, whether your data collection is compliant, where you are sending that data, and how you are using that data. Starting from the ground up with a solid data strategy foundation will set you up for long-term success. 

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