Dirty data has been a problem since the first databases were created.
Employees at every level have a lot on their plate, and when it comes to entering data, they tend to rush when adding it to the system. That, coupled with the fact that there are typically very few – sometimes no – standards for how this information should be entered, is a recipe for dirty data. What’s more, with multiple employees entering data this way, it’s no wonder our databases are quickly filled with inconsistent and incomplete data that is difficult to use!
The solution to this problem is to improve your data reliability.
Reliable data is verifiably accurate, highly usable, current, and consistent across the enterprise. Your business depends on it!
Now, you might be thinking, “Oh! You mean data quality.” Not quite.
In our work with business and technology teams, we help them differentiate between data quality and data reliability. Data quality refers to the standards and processes used to produce reliable data, which is a must for companies competing in today’s global economy.
The value of this differentiation cannot be overstated, and providing clarity at this level enables a strong data culture, something every company should strive to create.
Unfortunately, many businesses have no idea if their data is reliable until a large – and usually costly – mistake is made. Or worse, they know it’s not reliable but don’t know how to fix the years of bad data that have accumulated within their systems.
Talk about a potential business blocker!
At DataGence, we believe that reliable data is attainable for every organization, and our mission is to ensure that no business or business leader ever has to think about dirty data again.
Throughout this article, we want to help you better understand how to pinpoint and address data reliability issues, so you know your data is working for you, not against you.
Recognizing Data Reliability Challenges
While an analysis of your data might reveal key indicators of a data reliability issue – your data is incomplete, inaccurate, outdated or inconsistently formatted – identifying – an issue could be as simple as asking your executive team, “Do you trust your data?”
Even if the answer is unclear or avoided altogether, lack of trust can “look” like slow decision-making, avoidance of active investments into new markets, or an asterisk on a financial report, indicating something more behind the numbers.
And even those not on the executive team can help identify dirty data. Have you ever downloaded data from multiple systems into Excel? If so, you know the next step is constantly cleaning, de-duping, and fixing typos within this data. This tedious process can take hours, if not days, and often feels like a never-ending task, which is exactly what a data reliability problem looks like.
Worse, this type of unreliable data can lead to costly mistakes, such as making decisions based on inaccurate information, miscommunicating with clients, or facing compliance issues.
Imagine sending a marketing campaign to duplicate contacts or analyzing sales trends with flawed data. The consequences can be severe.
Other common symptoms that could indicate your data may not be dependable are:
- Mismatched Reports: When similar reports produce different results or when data that should be aligned across platforms isn’t, you’re likely facing data reliability issues.
- Faulty Decision Outcomes: If decisions based on data consistently lead to unexpected or undesired results, your data likely isn’t reliable. This could happen when you order too much inventory because the numbers are wrong, pricing data is off, or predictive analytics don’t accurately forecast business needs.
- Project Delays and Overruns: When data isn’t accurate or complete, teams spend significant time and resources chasing corrections and verifying data instead of moving forward with implementation. This pushes back timelines and leads to scope creep as the project parameters expand to accommodate additional data verification tasks and corrective measures.
- Low Adoption of Business Intelligence Tools: These powerful tools rely on high-quality data to generate insights and drive business decisions. However, the information they offer is only as good as the data used in them. If the data is “dirty” it will produce poorly performing insights.
- Inability to Scale: Poor data integrity is a significant barrier for businesses looking to expand into new markets or applications. Scaling requires reliable data, which cannot be effectively leveraged for machine learning models, predictive analytics, or real-time decision-making systems.
For decades, many businesses encountering these challenges have chosen to simply “kick the can” down the road in terms of cleaning up their data. And, for most of these businesses, they have remained successful, even wildly successful. But in an AI-fueled world, deprioritizing data reliability is no longer an option.
If companies want to take advantage of the latest technologies, their data has to be reliable. Here are a few ways you can diagnose and address these issues.
Diagnose Data Reliability Issues
Diagnosing data reliability issues often leads to “analysis paralysis.”
Companies are overwhelmed and need help determining where to begin, primarily because they need standards or a baseline to measure against. Identifying and addressing data reliability problems can seem insurmountable without a clear starting point.
For instance, assessing the accuracy of your data is a significant challenge if you need to know which sources are reliable enough to use for verification. This uncertainty makes it difficult to trust the data you’re working with.
If you are struggling with where to start, reach out to us at Datagence. We can help you conduct a data reliability assessment to understand how reliable your data is for meeting your business objectives.
Many companies use these assessments to examine the data within their ERP, CRM, supply chain systems. What’s more, the reliability of your data grows even more critical when you want to tackle an AI modernization project because the reliability of your data is crucial to reaching the benefits of your investment.
When we conduct a data reliability assessment, we start by setting clear objectives for the audit: What specific concerns or questions drive this review?
Next, assess the accuracy of your data by comparing it against reliable benchmarks or sources, ensuring what you have is true to reality. Check for completeness; missing data can lead to gaps in analysis and decision-making.
Then, analyze how data flows across your systems to pinpoint any transformation points where data is merged, split, or converted and where errors are prone to occur. Use this information to identify bottlenecks that can delay operations and affect your ability to make quick decisions.
Finally, evaluate how current your data is. You can do this by analyzing the outcomes of data-driven decisions. Are the results consistent and predictable? Or do they often surprise you?
We compile all the above findings into a detailed report highlighting your reliability issues. This creates your roadmap to addressing your data reliability issues. Now, you have a plan of action!
Monitoring Your Improvement
As the Datagence team executes audits and helps clients diagnose and address their data reliability issues, we are often asked, “How will I know the impact of the work and whether my data is becoming more reliable?“
A couple of factors are necessary for you to have accurate results.
- Unification: As the adage goes (thank you, Peter Drucker!), “You can’t manage what you can’t measure.” Since your data is scattered across multiple applications and databases throughout your data estate, it must be unified to be effectively monitored. Bringing the data together shines a spotlight on the source of dirty data.
- Health: Data health is a no-brainer. It’s surprising how many teams skip monitoring overall data quality and integrity. Here are the core data quality measurements:some text
- A Baseline Assessment. This effort provides stakeholders with a starting point. The baseline will contain information such as the number of data sources and the number of dirty data records – duplicates, missing data, inaccurate data, etc. – for each data source.
- Standardization. Data must be compared to an industry-recognized standard such as ISO and/or ANSI. Comparing the current data set against a standardized data set will provide everyone with insights into the corrective actions that need to be made.
- On-going Analysis. The continued monitoring of your data is necessary for understanding data improvement. After the data has been unified and cleansed, it’s essential to keep it that way. Most importantly, you can see how far your data cleanliness has come since the initial baseline assessment and its current state. Ultimately, this will provide the answer to the above question.
Data unification and cleanliness are most effectively accomplished through data observability, a critical aspect of data processing. By enabling robust data observability, operators can identify anomalies and inconsistencies as the data is flowing through the organization and they can often fix those issues without human intervention.
Datagence uses a Data Quality Engine, which automates this intense process and delivers actionable, trusted and verified data. This efficient work allows our clients to focus on their business while we focus on what makes their business run – RELIABLE DATA.
Need help?
The reliability of your data can either make or break your business. The speed and complexity of data management have increased dramatically, and in this dynamic world – with the rise of AI – the quality of your data is more crucial than ever.
Our goal at Datagence is to help you craft and execute a data strategy that delivers data reliability and tangible business results.
