Extracting Value from Dormant Data Using Data Mesh

Guest Post by Greg Tennyson

What is Dormant Data?

Organizations generate massive amounts of data across day-to-day operations, processes, and functions. Various systems have been developed to collect the data, protect its integrity, and ensure its readiness. However, organizations often don’t apply a common structure or taxonomy to analyze this data and increase its value dramatically by using it to make better decisions. This data often lies neglected, unprocessed, and unused, and remains stashed away in forgotten corners of the server. Such data that is collected and stored but not used is often referred to as dormant data.

Data Mesh: The Cure for Dormant Data

As organizations continue to seek innovative ways to extract value from every piece of information, here is our answer to this problem: the data mesh technology breathes life into data, and unlocks its untapped potential for transformative impacts. It achieves this by converting dormant data into actionable insights using predictive analytics and large language models that leverage artificial intelligence.

Data Mesh creates a robust, decentralized structure that promotes efficient data sharing. While many knowledge sites are now talking about Data Mesh, MECBot by FORMCEPT walks the extra mile in making Data Mesh a reality by establishing a single source of truth by combining the holy trinity of Data Domain Model, Enterprise Knowledge Graph, and Smart Metadata.  

This blog describes the value of data meshing as strategically integrating disparate data sources, ensuring unified and coherent datasets. Applying this to dormant data involves creating dynamic connections between seemingly isolated datasets and turning them into valuable assets.

Why is Managing Dormant Data So Complex?

One of the significant challenges in leveraging dormant data is breaking down the system data silos. Data Mesh acts as a catalyst for breaking these barriers, creating an interconnected contextual web of information. This enables seamless communication and collaboration across different departments and systems, fostering a holistic approach to data utilization. There are numerous solutions in the marketplace, but without establishing a global data definition within the enterprise, success is unlikely. 

A few data products, however, take the bull by the horns by keeping data unification, quality, integrity, and security at the heart of everything. For example, MECBot by FORMCEPT creates an interconnected contextual web by meshing data. MECBot is a cutting-edge augmented analytics product that provides unprecedented, just-in-time business intelligence. MECBot stands as a testament to the power of data meshing. This innovative solution seamlessly integrates data, resulting in a significant boost in efficiency and insights.

Benefits of Data Mesh for Dormant Data

Using Data Mesh, organizations can now gain a 360-degree view of their operations and put their dormant data to good use. This comprehensive view empowers decision-makers with actionable insights that may have previously been overlooked. For example, they can delve deeper into aspects that can help them establish a holistic customer/supplier relationship, such as revenues and spending, legal and commercial terms, renewals, and performance. Meshed data can provide a more nuanced understanding of these relationships, resulting in more impactful business outcomes and lessening organizational disruption.

Data Mesh doesn't just provide insights, it also streamlines processes. When previously dormant data is interlinked, it reduces the time and effort spent on manually cross-referencing information. This optimization of operational efficiency can result in quicker decision-making, improved customer service, and enhanced organizational performance.

As technology advances, the synergy between Data Mesh and dormant data holds promise for the future. Machine learning algorithms, powered by interconnected datasets, can uncover patterns and trends that human analysis might miss. This sets the stage for predictive analytics and proactive decision-making based on a deep understanding of historical and real-time data.