Why You Need Data Observability to Improve Data Quality
Experts estimate that the world is generating 2.5 quintillion exabytes of data every day. That information resides in multiple systems, including legacy on-premises systems, cloud applications, and hybrid environments. It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may require consider your data observability strategy.
The most recent Precisely Data Trends Survey found that over two-thirds of organizations experience negative effects due to disparate data. According to the Harvard Business Review, nearly half of newly created data records contain at least one critical error. It’s no wonder, therefore, that 84% of CEOs doubt the integrity of the data on which they base their decisions.
Systems and data sources are more interconnected than ever before. The resulting interdependency often leads to new problems. Complexity leads to risk. A seemingly simple change can have devastating downstream ramifications. A broken data pipeline might bring operational systems to a halt, or it could cause executive dashboards to fail, reporting inaccurate KPIs to top management.
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This report discusses five best practices for using observability tools to monitor, manage, and optimize operational data pipelines. It provides strategic guidance for enterprise data leaders in defining the core metrics of data quality and pipeline health.
What Is Data Observability?
For over 100 years, observability has been a key element of numerous process methodologies, including in the manufacturing industry and in software development.
The application of this concept to data is relatively new. In a nutshell, data observability ensures the reliability of your processes and analytics by alerting you to potentially problematic events as soon as they occur. This enables the user to visualize data processes and quickly identify deviations from typical patterns. The best data observability tools incorporate AI to identify and prioritize potential issues.
Data observability can be really broken down into three key capabilities: discovery, analysis, and action.
- Discovery involves collecting information about the data assets you want to observe, using a variety of techniques and tools.
- Analysis includes identifying any events that could adversely affect data integrity. The best data observability tools use modern AI and machine learning to improve accuracy and effectiveness.
- Action is about proactively resolving data issues to maintain and improve data integrity at scale.
Data observability identifies potential data issues early, enabling users to proactively solve problems at their root. That prevents further issues from occurring and eliminates the need to go back and fix data quality problems after the fact. Old-school methods of managing data quality no longer work. Manually finding and fixing problems is too time-consuming, given the volume of data organizations must deal with today. Data observability helps you manage data quality at scale.
Why Data Observability Is Important
Ultimately, data observability answers the question: “Is my data ready to be used?” That might have different meanings to different users. For operations managers who rely on downstream analytics to drive key decisions, it means having confidence in the information they need to do their jobs effectively and efficiently. For a data scientist building machine learning models for an important AI initiative, data observability helps set the stage for long-term success. For a top executive who wants that big-picture view of how the company is performing, it means knowing you can trust the data.
Imagine that your development team is making some changes to one of your core operational systems. They change the data type for several key columns in a table that holds customer order information. Unbeknownst to them, that information feeds into a self-service portal that allows customers to inquire about order status. Because of the upstream change, the downstream application might no longer work. A data observability tool would identify this change and alert the users to take action.
Next, imagine there is a sudden and unexpected drop-off in orders from your UK division. A data observability tool identifies this anomaly and alerts key users to investigate. The root cause turns out to be a problem with the data pipeline feeding UK orders into the main system. By resolving the problem quickly, the team can ensure the timely processing of those sales orders.
Data Observability vs. Data Quality
It would be easy to conflate data observability with data quality. After all, the two disciplines are very closely related to one another. Nevertheless, there are some important distinctions.
Data quality tends to focus on clearly defined business rules, analyzing individual records and data sets to determine whether or not they conform to the rules. Customer records, for example, should be consistent across the various systems and databases that include customer information. Furthermore, customer addresses must be valid and complete. If the city name is missing, or if the address includes a non-existent postal code, then it does not conform to business rules.
Data observability, in contrast, focuses on anomaly detection. If the volume of data changes suddenly and unexpectedly, for example, it’s important to know that and understand why it’s happening. A sudden spike in certain values could likewise indicate an upstream issue with the data. Longer-term trends in the data often merit attention as well.
To get the most possible value from a data observability solution, look for a product that includes an integrated data catalog. That provides a single searchable inventory of data assets and allows technical users to easily search, explore, and understand their data. It enables key users to visualize the relationships among various data sets and to clearly understand data lineage.
An integrated data catalog also provides collaboration tools such as commenting capabilities. It enables monitoring, auditing, certifying, and tracking data across its entire lifecycle.
Data observability helps organizations understand the overall health of their data, reduce the risks associated with erroneous analytics, and proactively solve problems by addressing their root causes. To learn more read TDWI Checklist Report: Succeeding with Data Observability.