Good Data, Bad Data: Why Data Quality Matters, and How to Make It Better

Ensuring Accurate Insights: Strategies to Distinguish and Enhance Data Quality

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

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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.

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.

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 datathat 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.