Master Data Management

Is Your Master Data Fundamental for AI/ML Algorithms?

In today’s connected world, businesses are witnessing data explosion like never before. Enterprises must pay attention to the volume and the variety of data from newer sources—the Internet of Things (IoT) sensors and connected devices. It has become imperative for organizations to become ‘Data Agile’ to adapt to the ever-changing demands of global data management. Nallan Sriraman explains how clean master data is fundamental for AI/ML algorithms in this article at Forbes.

Growing Application of Master Data

According to studies, organizations believe they lose nearly 27% of their revenue due to inaccurate master data. As enterprises embrace artificial intelligence (AI) and machine learning (ML) technologies, they must adopt improved data management technologies to stay relevant in this highly competitive market.

“Master data plays a significant role in linking various operational data in any enterprise,” explains Sriraman. ML requires precise data, and its training sets must have the right connections and links to ensure higher-precision prediction. Any data discrepancy due to missing or wrong information can lead to catastrophes. According to industry experts, to address master data issues ahead of any heavy investments in AI/ML will result in significant data rewiring.

Lack of Awareness

Many large organizations embed data within the core systems—enterprise resource planning systems. However, the executives delegate the data management and maintenance responsibilities to operation teams with a complex process. Such processes can act as a roadblock for keeping pace with the changing business models. In many instances, business decision-makers are unaware of master data’s existence until they face a significant business risk.

Enterprises and business leaders often consider these as an IT issue and bring in new master data management technologies. Instead, companies must revamp their processes and controls and integrate proper operating models.

Before investing in cloud-scale data lakes, an organization must analyze how clean their data is and how efficiently it is sourced, managed, and maintained. In an ideal scenario, the sensible application of AI in data management can bring tangible benefits to enterprises. ­­­­

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