Preventing Data Downtime

Preventing Data Downtime: Data Governance, Observability & Quality Considerations

Jonathan Robson, Data Governance Director at Precisely

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

But what happens when these reports prove to be inaccurate? It’s a challenge that business leaders are increasingly seeking to address. In fact, a recent study from Drexel University’s LeBow College of Business showed that 77 percent of businesses report data-driven decision-making is an important goal of their data programmes.

The truth of the matter is that the accuracy of reporting and business intelligence systems are only as good as the data feeding them. Companies need to ensure a constant flow of trustworthy and timely data to be able to make confident, data-driven decisions. The answer to achieving this lies in leveraging high-integrity data that is consistent, accurate, and contextual. Below I’ll explain how companies can adopt robust data integrity strategies, centered around effective data governance, observability, and quality, to prevent data downtime and fuel strategic decision-making.

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 is the very definition of “data downtime.” It 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 organisation is simply no longer able to function at its best.

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 organisation’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 organisations 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 organisations. 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 recognising the value of data observability, with Gartner asserting that “data observability has now become essential to support, as well as augment, existing and modern data architectures.” Additionally, the demand for high-quality data has never been as critical, with recent findings from Drexel University’s LeBow College of Business showing that 70 percent of organisations with low levels of trust in their data point to data quality as their biggest challenge to making confident decisions.

To establish and maintain trust in their data, organisations must adopt a holistic approach to these challenges. A robust data integrity strategy centered around effective data governance, observability, and quality is an important step on the path to trusted data, and ultimately, to data-driven decision-making that can be truly relied upon.