“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.
“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.
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.
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 into this topic and how data observability is linked to business growth.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
A majority of business leaders believe data insights are key to the success of their business in a digital environment. However, many companies struggle to build a data-driven culture, with a key reason being the lack of a sound data democratization strategy.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
Data governance is more than just having a strategy – it is about establishing a culture where quality data is achieved, maintained, valued, and used to drive the business. Modern-day businesses are supported by data and information in many ways and forms. In recent years, data has become the foundation for competition, productivity, growth, and innovation. We are seeing successful organizations shift their focus from producing data to consuming it, and data governance strategies becoming increasingly important to support their crucial business initiatives. Executives and shareholders are starting to realize that data is a strategic asset and data governance is a must if they want to get value from data.
Data matters more than ever – we all know that. But at a time when being a data-driven business is so critical, how much can we trust data and what it tells us? That’s the question behind data reliability, which focuses on having complete and accurate data that people can trust. This article will explore everything you know about data reliability and the important role of data observability along the way, including:
I started my career as a first-generation analyst focusing on writing SQL scripts, learning R, and publishing dashboards. As things progressed, I graduated into Data Science and Data Engineering where my focus shifted to managing the life-cycle of ML models and data pipelines. 2022 is my 16th year in the data industry and I am still learning new ways to be productive and impactful. Today, I am now the head of a data science & data engineering function in one of the unicorns and I would like to share my findings and where I am heading next.