How Machine Learning Will Impact MDM

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In this special guest feature, Christophe Marcant, Senior VP for Strategy & Communication for Stibo Systems, explains how there is great opportunity to leverage machine learning (ML) to adapt data from a source to a consumer faster. So, rather than focusing on enforcing format and meaning to facilitate exchanges, ML will enable organizations to discover patterns in data, propose associations, correlations, and adaptation. Christophe Marcant is senior vice president for strategy & communication for Stibo Systems, a global provider of multidomain Master Data Management (MDM) solutions. For 10 years as a retailer, Christophe focused on how product information can be leveraged in e-commerce and omni-channel initiatives. Prior to joining Stibo Systems, he led SapientNitro’s PIM practice, where he advised clients worldwide as they considered new product information strategies.

The traditional “system of control“ approach to master data management (MDM) — where the focus is on centralizing and controlling data to better distribute information via a system of control —  is no longer sufficient for an organization to be in command of its data. Thanks to the internet of things (IoT), machine learning (ML) and artificial intelligence (AI), the world is becoming increasingly digital. Just ask Gartner analyst Frank Buytendijk, who predicted in a March 2017 presentation that “In 2016, spending on IoT hardware will exceed $2.5 million … every minute.”

As a result, the challenge of creating a trusted view of all available data and meeting the demands of business users is about to get much more demanding. Today, it is even more critical that companies become data agile so they can adapt to ever-changing demands and be enabled rather than hindered by data. The demand for MDM is moving toward the creation of a “system of engagement” where the emphasis is placed on creating next-generation communication and collaboration capabilities.

The growing concept of IoT and its expansion will become even more pervasive due to the persistent development of Wi-Fi availability. In fact Forrester predicts, “IoT will be distributed across edge and cloud, boosted by AI and containers.” In other words, not only will Wi-Fi availability assist the growth of IoT, but also new machine learning. Both factors will enable nearly any device that can be connected to the internet to be connected to each other as well. Machines and devices will increasingly be driven by data and, therefore, will also become part of the over-all IoT equation.

What ML Means for MDM

Instead of being used as a system of control, the market will soon demand that MDM solutions adapt and react to data demands quicker, whether internally or externally. Defined by some as a kind of artificial intelligence that provides computers with the ability to learn without being explicitly programmed, there is great opportunity to leverage machine learning to adapt data from a source to a consumer faster. Rather than focusing on enforcing format and meaning to facilitate exchanges, ML will enable organizations to discover patterns in data, as well as propose associations, correlations, and adaptation.

For instance, machine learning uses algorithms and pulls from existing data to create a prediction, so the more data a system processes, the more it will learn. It will also allow for any new data to be taken into consideration as needed by relying upon prior interactions, which will render traditional extract-transform-load (ETL) approaches a thing of the past.  So, what does this mean for MDM?  According to Andrew White of Gartner, “Deep learning will not make MDM go away.  We just need to keep our feet on the ground and understand the kinds of problems that deep learning can help with.”

While master data management has helped, enterprises still need to consolidate their views of the customer or product from within their own internal sources. These solutions have not scaled to handle the connected world. No matter how much data there is, or how fast it needs to move, the aim is to deliver timely information that users can convert into insight. MDM solutions strengthen an organization’s Big Data efforts in a number of ways by feeding information to the larger effort and by providing:

  • A connected source for eCRM, personalization, social commerce, and offline activity
  • The ability to combine product and customer master data with dynamic information based on analysis of Web and social media data
  • The opportunity to one day create a single source of behavioral information that can originate from multiple data sources
  • A single data source for all channels

Master data management solutions will continue to be the source of truth and will serve as a logical starting point for Big Data analysis. Companies considering utilizing Big Data applications need to have a full MDM strategy in place as master data management will serve as the backbone of Big Data applications. These applications in turn will supplement Big Data streams, allowing organizations to gain better analysis and more accurate insights from all types of data and sources regardless of how they were obtained.


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