“There must be something wrong with Excel. I can’t get these numbers to make sense.” For anyone who has had a similar experience of staring at a spreadsheet for far too long, we have news for you: Excel isn’t the problem; your data is.
Growing businesses encounter this issue as they scale their offerings and onboard new technologies. The more information you gather and store, the more complex your data infrastructure becomes. One manual error — like a coding typo or selecting the wrong data type for a new CRM field — can cause entire data sets to be inaccurate or incomplete.
Mistakes will inevitably happen. That’s why it’s important to have data reliability tools and procedures in place that prevent bad data from moving further through your tech stack. This enables your organization to be confident that the data being used for analysis, insights, and decision-making is accurate and up-to-date.
In this guide, you’ll learn why data reliability is important, how it affects business outcomes, and the tools companies can employ to strengthen reliability and ensure consistency.
Why is data reliability important?
“Data-driven decisions” has become quite the buzzword, but don’t let that distract from the fact that making business decisions based on accurate data is vitally important. Over the last decade, businesses have gained unprecedented access to quantitative and qualitative data about their operations, customers, and prospects. With all this information at our fingertips, it’s become best practice for every team, from marketing to customer success, to make data-driven decisions. Why trust your gut or talk about hypotheticals if you can use hard facts and figures to guide your next move?
In some ways, access to this information has helped level the playing field among small, medium, and more mature businesses. The data is out there if you’re able to tap into it and know how to use it. But this also presents a problem.
You may be collecting and storing a sizable amount of information within your data stack, but are you certain that these data sets — individually and when combined for analysis — are always complete, accurate, and up-to-date? If your answer is not a confident “yes,” then your business can’t reliably make data-driven decisions.
Data reliability is the foundation for confident decision-making and a successful data team.
What tools can improve data reliability?
Proactive, automated alerts and transform tests are two crucial data reliability tools companies utilize to proactively catch and debug issues, ensuring their data is accurate and reliable as it flows through the data pipeline.
If organizations don’t monitor their data, they’re setting themselves up to miss important outcomes. This could be flaws in the data, like duplicate fields, or important events, such as revenue goals. Proactive, automated alerts can be raised the moment that specific conditions — which are predefined by the business — are met.
Data reliability should be an ongoing consideration, and data alerts can help maintain diligence. There are two notable points in time when data monitoring should lead to an alert, if necessary: as the data is loaded into a data warehouse and post-transform.
When data flows from its source, such as a marketing automation platform, into a data warehouse, it’s still considered to be raw data. At this point, it’s likely the data will contain flaws like duplicates, missing values, and incorrect formatting. A transform can clean the data, but many of these issues can and should be fixed before getting to that point.
Alerts that identify issues as data enters the warehouse allow you to take corrective action before defective data is used downstream. Corrective action might involve editing the data in the source platform, or letting an engineering team know that data is not being stored or collected correctly in a database, so that more involved troubleshooting can be prioritized. These types of actions help build confidence that the transformed data teams are working with is as complete and accurate as possible.
Additionally, the results of a transform should be monitored to help businesses track specified outcomes. Alerts raised post-transform might highlight potential discrepancies, like unusually low or high returned values, or monitor discrete metrics for irregularities, long-term trends, milestones, etc.
Related to alerts are tests. In the context of maintaining data reliability, this would often look like a transform test. A transform test runs before the actual transform and can be used to flag specific outcomes. After the test runs and identifies an issue, it can either prevent the transform from running or send out an alert.
Transform tests can be an important tool to help prevent data errors from reaching others, proactively identify data collection issues or unexpected positive outcomes, and maintain everyone’s trust in your data.
How to make your data more reliable with Mozart Data
Mozart Data’s modern data platform provides data alerts, transform tests, and many other tools to help you ensure your data is accurate and reliable. Maintaining trustworthy data gives business leaders confidence in their team’s reports and analyses, and enables them to make strategic, data-driven decisions.
Data reliability is only possible if you have a clear view of your entire data pipeline though. This is achieved through what’s known as data observability. Read this post for a deep dive on this topic and how data observability is linked to business growth.
Contact us to schedule a demo to learn more about Mozart’s data reliability tools and how our modern data platform can help your business transfer, store, transform, and organize, large data sets in a scalable way.