Data lies at the heart of many businesses in the modern era, helping guide their decisions from top to bottom. But if you want to be effective in the realm of data analytics, you need to reduce the number of data errors and conflicts in your system.
What are the best strategies for preventing these mistakes and conflicts?
Centralize Your Data
First, you need to find a platform that can function as a centralized location for all your data โ a single source of truth (SSOT) for your organization. One of the biggest problems organizations face in managing data is trying to pull data from many different sources simultaneously. If you have multiple software platforms that you’re juggling, you might store similar information in each of them, without necessarily having integrations to support synchronous updating.
This can result in a number of problems, the most notable of which is conflicting data sets. If you have two different systems with two different pieces of data for a set of parameters, which one do you believe? Having multiple sources of truth is also confusing for your employees, limiting their productivity and causing more miscommunications and internal workflow problems as well. With a single source of truth, there will be no ambiguity about which data counts or where to find it.
Choose the Right Platforms
Next, make sure you choose the right platforms for gathering, analyzing, and storing data. It’s a good idea to have a single source of truth, but with so many software options available, it’s hard to know what’s the right fit for your business. There are too many considerations to list here, but keep in mind that you should do your due diligence before choosing any data platform โ and you shouldn’t be afraid to switch if yours isn’t pulling its weight.
Hire the Right Specialists
Data analytics specialists can make your job much easier. Consider candidates based not only on their prior education, current credentials, past work experience, and results but also their general attitudes and philosophies concerning data management. Good data specialists will work diligently to prevent data mistakes and conflicts, and they’ll be able to help rectify issues if and when they arise.
Identify and Document Your Data Sources
Choose your data sources carefully; youโll need to properly identify and document them if you want to be successful here. For starters, this forces you to be more discerning, choosing your data sources carefully. You can also preliminarily outline potential weaknesses or limitations of your various data sources. But perhaps most importantly, this process gives you something to review if you encounter data errors in the future.
Follow a Specific Data Quality Philosophy
It’s important for your organization to follow some kind of data quality philosophy or system. You can follow a data quality framework that already exists, like the DAMA-DMBOK or the ISO 8000, or develop something of your own. There are many schools of thought here, but what’s important is that you have some kind of guiding principles to follow.
Practice Data Validation
It should go without saying that you need some kind of data validation practice in place. You can choose a platform that does this on your behalf, or code something from scratch to suit your needs. Rooting out potential discrepancies and errors is critical if you want to keep your data clean and accessible.
Use Automation
Automation can help you with data validation and a host of other responsibilities related to data analytics. The obvious benefit is that it’s going to save you time; automation is much faster than even the best human expert. But it’s also important to recognize that automation is more consistent, leading to fewer mistakes and discrepancies as well.
Rely on Continuous Data Quality Monitoring
Practice data quality monitoring continuously. In today’s fast-paced and data heavy world, it’s simply not enough to practice periodic data review. Instead, you need to be continuously reviewing your new and old data for integrity and quality.
Flag Errors and Discrepancies โ and Investigate
Even with all these strategies and practices in place, it’s possible for you to find errors and discrepancies. That’s totally acceptable. However, you need to commit to flagging those errors and discrepancies, so you can properly investigate them.
Review and Improve Your Workflows
Finally, take the time to review and improve your workflows. There will always be opportunities for further optimization and data error prevention. As long as you’re continuously refining, you will continuously push your organization further.
Data science and analytics are complicated fields, and no business can pursue them perfectly. However, with a bit of proactive attention, the right investments, and better systems and workflows, you can prevent the majority of data quality and accuracy issues in your organization.
