Preventing Data Downtime with Effective Data Governance, Observability & Quality Strategies

Today’s decision-makers rely on different types of information to help them understand the complex landscape in which they operate. Most organizations use a mix of sales reports, marketing analytics, and operational reports to help their managers with day-to-day decisions and strategic planning.

Such reports often turn out to be inaccurate. Imagine this scenario: the VP of Sales is poring over a sales pipeline report when he suddenly realizes that something is a bit off. One or two numbers fall outside of the normal range, so he starts to raise questions. That leads to the discovery of an error or two, which raises further doubts, prompts some more questions, and ultimately leads to a loss of confidence in the sales reports in general.

The team scrambles to figure out what happened. Why did the report fail to deliver clear, accurate information?

Ultimately, the accuracy of reporting and business intelligence systems depends on the processes that feed data to those analytics tools.

 Data Observability and Data Quality 

Data travels from various source systems through a host of ETL and data quality processes before they ultimately make it into those dashboards and reports. Those systems often evolve over time and grow to be complex. With data coming from multiple places and undergoing sophisticated transformations, it can be difficult to trace a problem back to its root cause in a timely manner. That results in further frustration among the end users who rely upon that data to make critical business decisions.

This describes a phenomenon that we call “data downtime.” Data downtime occurs when users in your company no longer have direct access to the accurate, timely data they need to make effective business decisions. When a report stops working properly, the organization is simply no longer able to function at its best.

A Proactive Approach to Data Integrity

What can data leaders do to address the problem of data downtime? How can they minimize the negative impact by taking proactive measures to prevent issues from emerging in the first place?

For companies that rely on complex data integration processes, the answer begins with a clear understanding of the pipelines that prepare the data and deliver it to various analytics platforms. Data leaders must monitor and manage the ongoing health of those pipelines and develop formal, scalable mechanisms for proactively managing data quality.

Organizations must also develop the capacity to identify problems quickly when they emerge. Regarding the example provided above, let’s imagine how a typical company might respond to the problem with its sales pipeline report. The Sales Manager fires off an email to her main contact in the IT department, which leads to a flurry of back-and-forth communications among team members.

The first step, they all agree, is to figure out where the data is coming from and who owns it. They grapple with questions about what’s happening to the data as it makes its way from one or more source systems to the analytics platform driving the sales reports. What do they need to restore trust in the data and the sales pipeline report?

Which tables and fields and which rows of data are causing inaccuracies? Root cause analysis usually takes time, and that creates frustration among business users who expect to have accurate information so they can make timely decisions.

Today’s top-performing organizations rely on data stewards, data engineers, and data analysts to operate in tandem to create and maintain trust.

• Data stewards are responsible for assuring data quality by defining organizational standards and maintaining companywide consistency.
• Data engineers are responsible for ensuring that data is accessible to all stakeholders, creating and maintaining the data pipelines that transform the data and get it where it needs to go.
• Data analysts are ultimately consumers of the data, but they play an important role in the lifecycle of the data, helping to ensure that it is accurate and of high quality and that it’s available and suitable to the purposes for which it is being used.

Preventing Data Downtime

These three roles must work together to prevent data downtime. In a truly data-driven organization, there can be no islands.

The prevention of data downtime must ultimately be an ongoing process. You can break that process down into three distinct areas of activity:

Prepare: Data governance begins with understanding your data landscape, identifying the information that is most critical to your business, and assigning clear data ownership. Effective data governance requires a structured framework and well-defined processes.

Identify and observe: Data observability is about defining clear business rules to validate data assets and developing the capacity to zero in on the root cause of the problem very quickly.

Remediate: To develop and maintain data integrity at scale, you must put processes in place that enable you to quickly and proactively fix problems when they occur. Effective remediation involves understanding anomalies and creating scalable business processes to address issues quickly.

Business data changes on a regular basis; the nature of your organization’s reports will evolve over time. You must continuously evaluate your data landscape and adjust your data governance, data observability, and remediation processes to keep pace with those changes.

Many organizations struggle to deliver clear, measurable benefits from their data governance programs. To be successful, data leaders must align people, processes, and technology in ways that ultimately serve the strategic business objectives of their organizations. It’s essential to clearly identify a data governance strategy, enlist executive sponsorship, and foster collaboration between business users and the IT department.

Top-performing companies are also recognizing the value of data observability. Gartner asserts that “data observability has now become essential to support as well as augment existing and modern data architectures.”

Analysts at 451 Research emphasize the critical need for data quality: “Data quality and consistency [were] cited as the top barrier[s] organizations face (34%) in attempting to be more data-driven.”

To establish and maintain trust in their data, organizations must adopt a holistic approach to these challenges. Precisely’s Data Integrity Suite offers a fully integrated, modular set of tools for data quality and governance, integration, data enrichment, and location intelligence. To learn more, check out Precisely’s free on-demand sessions from our Trust ’23 Data Integrity Summit.