Metadata Automation Puts Everyone on the Business Intelligence Team

by Amnon Drori

Business intelligence (BI) is a concept that is now over 60 years old. Software tools meant to help businesses organize and access data began popping up in the late 70s. A couple of decades later, a whole new discipline was carved out to address the importance of using data to drive business decisions—the Business Intelligence Team. Inside the BI Team is where you find the methods, technologies and tools that companies can use to extract and process data. 

Yet, here we are in 2020 and most companies still do not have the means to find data, fix errors and meet compliance requirements quickly and easily. It is a problem that delays the extraction of meaningful insights from traditional places where data is stored. The result is added pressure on the BI Team because businesses need accurate data now, not a few weeks from now.

This pressure has been increasing over the last few years with the exponential increase in the amount of data. It has forced business intelligence to no longer be confined to a particular floor in the building. Business has become so data driven that people from ALL parts of the business need the ability to locate and gain full visibility into what is available and know how it is connected and accurate from the get-go.

But on top of that, out of the blue comes the COVID-19 pandemic forcing many businesses in 2020 to close down in-person operations. Hundreds and perhaps even thousands of employees working from home made true face-to-face interactions impossible. BI teams now needed to collaborate while they were not on the premises anymore, and it became even more difficult because information regarding data processes was no longer being shared at in-person meetings. Companies became acutely aware that data management needed to be simplified and put on the cloud so data users can quickly and efficiently get visibility of their entire BI landscape wherever they are based. Making such a pivot on the fly just proved to highlight another limitation of manual data management and such a realization hit many businesses on top of the head.

Like so many things involving technology, the answer can be found in the cloud. The adoption of tools that automates the basic data management tasks (finding data quickly, fixing errors and meeting compliance requirements) and allows data visibility on the cloud for collaborative BI work must be a key priority for businesses moving forward. Moving to the cloud and utilizing cloud-based software simplifies things for employees who are now mostly accustomed to using apps on a mobile device.

All the realities mentioned here have solidified the need for automated metadata management, which is essentially creating a Google and Waze-like hybrid search and discovery system within an organization. It enables users to almost immediately track the source of any piece of data and, just as quickly, find the root causes of any reporting errors by tracing data back to its origin. Manual business intelligence can no longer serve the current regulations in place that require companies to have tight control and accountability on data. An automated system protects data privacy both internally and externally yet still allows for business decisions to be made in a fast-paced environment. Such capabilities serve the financial, healthcare and insurance industries extremely well.

Again, cloud-based tools for automated metadata management are the most effective way to help users easily find and understand their data. New platforms enable BI teams to locate metadata from any location and provide a format to access and track metadata that is familiar to the average mobile user. Such interface simplicity is crucial because the BI & Analytics teams are no longer the only ones accessing data. Mainstream data users are enabled to access data and make timely, accurate business decisions.

In the immediate future, BI & Analytics teams should focus on these two critical capabilities: updating to automated metadata management and migrating to on premise or cloud-based business intelligence. These two components together will leverage automated metadata management as a strategy that can be baked into all levels of the organization so that the true value of data can be unlocked for businesses. It will create a system that allows employees to easily access data, no matter where they are, and provide a solid business intelligence infrastructure that will accommodate the changing business intelligence environment.

As we continue our move to Big Data, most companies would benefit from extracting meaningful insights from their data. The emergence of an easy-to-use, centralized metadata management solution is essential to move efficiently throughout the data journey and data value chain. The underlying skill is metadata management. And through automation, BI teams can better focus on discovering new patterns, predicting future events, and simulating different scenarios in an easy, efficient, and transparent way. Yet it all gets treated with the foundational and strategic importance of the modern data enterprise. Evidence-based, data-driven insights and digital transformation are imperative for the next-generation organizations. In order to harness this change, glean new insights, and make decisions faster, companies must look at their data operational processes and look for new efficiencies. Metadata management has strategic, efficiency, financial, and regulatory implications that must be addressed to break data value bottlenecks.

Amnon Drori

About the Author

Amnon Drori is CEO of Octopai, a leader in metadata management automation for business intelligence and analytics. For more information visit:

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