Analytics plays key role in success with master data management
Industries have been realizing the value of analyzing their sales and transaction data to understand what their customers are buying. However, a holistic view of how it ties back to product information has been long overdue.
Analytics capabilities are emerging to become an integrated part of master data management solutions and bringing benefits to organizations in the competitive world of data-intensive companies like Google and Amazon.
Master data is associated with core business entities (such as product, customer, vendor, asset and others) while transactional data is associated with the business transactions for sales, orders, payments etc. Master data contains information about what an entity is – for example, its technical details and description. Master data management (MDM) requires putting processes in place for governance of the data which helps resolve conflicts across systems and to keep the data accurate and clean.
We are generating increasingly large amounts of data as we increase our digital interactions leading to the era of “big data.” However, if the access to big data for a company is not accompanied with proper understanding and analysis, it results in a lot of missed opportunities.
Customers too are becoming more demanding, not just of product information in general, but also of personalized recommendations to help them make their decisions. In an increasingly data-intensive landscape, with Amazon and Google as competitors, it is no surprise that 53% of companies are using big data analytics today, up from 17% in 2015, to help them serve their customers better.
Industries have been realizing the value of using analytics for their transactional data to understand what their customers are buying. The analysis is being done using the transactional data - what customers are saying about their products on social media, product ratings and which products are selling more or are selling fast. However, a holistic view of how it ties back to product information is now needed. Increasing sophistication of machine learning algorithms could help in closing the feedback loop from transaction data analytics to the master data.
The first way this can be done is by using machine learning algorithms to help find patterns and bring insights on the next maturity level of analysis from “What are my customers buying?” to “Why are they choosing a particular product over the other?”
This would start by determining the correlations between a certain type of product information and its sales trends. These patterns would act as metrics which would be used to measure the completeness of product information in the MDM system and in return, forecast the sales right away. Businesses would get early feedback on product information and work with their partners and stakeholders to improve the metrics.
Secondly, analytics on sentiments from social media and e-commerce website reviews could help in improving the product information and making it more search friendly for the end customers. A plethora of content is created by users regarding how they are using the product and why they like or dislike it, which could help in enriching the product information. Machine learning models could predict which kind of content coming from users would make a positive impact on sales.
Finally, with an increasing number of products and channels on which products need to be sold, internal efficiency needs to be examined more thoroughly. Analytics is playing a consequential role in increasing operational efficiencies by bringing automation and more in-depth root cause analysis.
These are just a few ways in which analytics and machine learning can be exploited to reinvent the ways MDM applications operate. Moving forward, with a greater use of technological tools and ideas in the implementation of MDM, we expect to discover more ways to provide end-to-end solutions to enterprises.