by
Jagadamba Krovvidi
Associate Vice President and Principal Technology Architect Infosys Center for Emerging Technology Solutions (iCETS)

Data Analytics

Augment your Master Data Management with AI to be ‘Data Rich’

In today’s connected world, business enterprises are witnessing data explosion like never before. It is not just the volume of data that needs immediate attention but also the variety of data from newer sources such as Internet of Things (IoT) sensors and connected devices. Further, evolution of cloud technologies has formed the basis of change in technology budgets from a concentration on hardware & infrastructure purchases to one that leverages technology & services to make best use of the corporate data assets.

These factors make it extremely challenging for enterprises to remain committed to their traditional data management systems which restrict them from harnessing the full power of data under their control. It has become imperative for enterprises to become ‘Data Agile’ so that they can efficiently adapt to the ever changing demands of global data management.

According to a recognized analyst report, organizations believe that over 27% of their revenue is wasted due to inaccurate master data. As enterprises continue to embrace Artificial Intelligence (AI) and Machine Learning (ML) technologies, businesses will continue to adopt improved data management technologies to stay relevant in a highly competitive marketplace.

With rapid digitization of business ecosystems, enterprises are dealing with rapidly growing and changing data pertaining to their products, customers, suppliers, employees and stakeholders. The ability to manage this data and later master it becomes imperative for enterprises to succeed and gain an edge over competition.

Mine ‘Gold’ out of your Data with the right Master Data Management strategy

As all elements of business and commerce become more and more digitized, every organization finds itself surrounded by immensely high volumes of data pertaining to critical stakeholders of their ecosystem. This is where an effective Master Data Management (MDM) strategy will become a game changer!

At Infosys, we have observed that enterprises that do not have a well carved out MDM strategy are facing tremendous challenge to stay relevant as data has become their most critical asset. Inaccurate and inconsistent data could jeopardize businesses. The goal of MDM is to identify, validate and resolve data issues as close to source as possible, while creating a “Gold Copy” master dataset for downstream systems and services to consume. MDM provides many benefits, and when implemented correctly can ensure consistency, completeness and accuracy of core data sets.

What’s in the NextGen Digital Master Data Management

MDM solutions will continue to be the source of truth and will serve as a logical starting point for Big Data analysis. Enterprises that are looking to invest in Big Data technologies need to have an enterprise-wide MDM strategy in place as it will serve as a building block for future proofing their data stores. This will further enable enterprises to gain better insights from all types of data regardless of where they were sourced from. It will also allow enterprises the flexibility to consider new types of data that could augment their decision making ability. The demand for MDM is moving towards decision making and knowledge management.

Effective Data Management can help organizations to achieve the following benefits:

  • Provide Seamless Information Across Multiple Channels
  • Help Understand Customers Better by creating an integrated view of systems
  • Increase Trust in Data thus enabling better business decision making and forecasting
  • Connect Everything & Anything
  • Greater accountability of data throughout various lifecycle stages

At Infosys, we are working on NextGen Digital Data Management Offering that harnesses the power of AI and ML. Our flagship offering that focuses on data quality, is cloud ready and is highly scalable.

Various ML algorithms perform Data Quality analysis activities such as outlier detection using unsupervised learning and MDM features like data enrichment using supervised learning methods. Further, ML algorithms allow it to extract data from existing data sources to create predictions which can be leveraged when new data is made available. ML allows enterprises to discover patterns in data, as well as propose associations, correlations, and adaptation. As the system learns more about data, it eclipses traditional extract-transform-load (ETL) approaches making it a thing of the past. The platform provides multi database support leveraging technologies like Spark, Big Data, R, Hadoop and Deep Learning. It also comes with a Business Rules Modeler that can extract business rules from enterprise applications like SAP and help validate the data quality automatically using the extracted rules.

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.”