By: Dmitri S. Novomeiski, Associate Vice President, LTIMindtree

As we know, data is being termed as the proverbial “new oil.” The success of an organization is often governed not only by its solutions, processes, and agility but also by the streamlined flow of information and insights on critical domains across the organization. These include products, customers, employees, assets, competition, budgeting, people planning, and much more. However, businesses are often overwhelmed by the information barrage from structured and unstructured to semi-structured data. They do not always have the right processes and solutions to leverage the data for a competitive edge. It is no surprise then that Master Data Management (MDM) solutions are gaining traction to ensure the availability of consistent, accurate, and timely information and drive growth.

Research by MarketsandMarkets expects the global MDM market size to expand from $11.3 billion in 2020 to $27.9 billion by 2025, growing at a CAGR of 20%. The major driving factors in the MDM market are an increase in the use of data quality tools for data management and the rising need for compliance. Harmonized, business-critical data that can be accessed across the organization to deliver new product development, enhanced customer experiences, and deeper insights into  the market are vital to the digital journey. This is particularly true for large enterprises with a wide range of technologies and applications operating in siloes.

Challenges to Effective MDM
The benefits of data management are widely understood and accepted, and implementing an effective MDM solution ensures data integrity and accuracy. However, organizations often run into challenges during deployment. We have identified those common impediments to master data management in four buckets:

Data quality: The effectiveness of your MDM implementation is directly proportional to the quality of the data in it. Without comprehensive data quality, the insights are usually rendered useless and sometimes even dangerous. It is critical to continuously evaluate your data for completeness, validity, consistency, accuracy, and timeliness. However, most organizations today have limited data clean-up and enrichment processes. This can pose serious challenges and even undo efforts if employees have data that is untrustworthy and incorrect. As customer data management matures, it needs to become autonomous and intelligently remediate conflicts using AI/ML models. A regulated and robust data enrichment process is  essential for ensuring high data quality at all times.

Data governance: Despite the defined standards, MDM implementations can be highly complex, and often organizations lose out due to loosely developed data governance policies that are not actively enforced. To get a clear view of data operations, having a robust data governance policy and standard operating rules are imperative. These policies should be aligned with business rules to effectively mitigate the complexity of master data and ensure that update conflicts are minimized, or even better, eliminated.

Processes: Most organizations today have multiple systems of record that are manually intensive and create challenges with regard to real-time data availability. There is a good chance that data is spread across the business in various applications, spreadsheets, and even physical media such as paper and reports. This challenge is further compounded as multiple business functions run with disparate definitions for the same event or relationship. As the data management process matures, customer master data will emerge as the single source of truth with rigorous and standardized methods and eventually graduate to real-time validation at the system of origin.

Technology: Today, the tech systems used by organizations to manage their data are deregulated and poorly integrated. Homegrown applications were used to develop existing customer or product data customers to support a specific function or line of business vis-à-vis  the enterprise as a whole. But as businesses evolved and with ERP and CRM systems implemented, it became  imperative to modify all existing applications. As MDM matures, data could be made available using SOA architecture and web services across the enterprise.

Best Practices: Customers Look for Lightweight and Easy to Adopt MDM Solutions
It is vital to factor in critical aspects while implementing an MDM solution that can effectively handle massive volumes, scale, and range of data.

The solution should be lightweight and flexible. Traditional MDM solutions are often complex, expensive, and difficult to maintain. Hence, a solution that can be implemented quickly and with minimal disruption will reduce time-to-value and ensure rapid deployment and innovation.

One-time master data consolidation and harmonization. Businesses today want solutions that offer one-time data consolidation to identify duplicate records and merge them and harmonization to push cleansed data back out to the systems. The idea is to build synergies and leverage existing systems bringing down the total cost of ownership.

Defined processes and governance for MDM by stewardship teams. To ensure the continued success of their MDM implementation, enterprises are also looking for solutions with a robust and well-defined governance system that involves minimal human intervention. Pre-defined internal data standards and policies are critical to managing data availability, usability, integrity, and security in enterprise systems and also controlling  data usage.

Future-readiness for incremental data hygiene activities. As businesses grow, the data is bound to evolve and expand. To ensure that your MDM implementation is truly next-gen and minimizes risks, it is vital to implement a proven solution that drives customer-centric transformation in the long term. MDM solutions with  AI algorithms will prove to be more aligned with  the future needs of an organization.

The Bottomline
While implementing MDM may have challenges, it is a must-have for businesses to drive insights-driven decision-making and maintain a competitive edge. Without question, managing client-centric information is fundamental to an organization’s growth and digital transformation journey. It provides accurate customer data and history visibility across the enterprise, enhances the quote-to-cash life cycle, improves cross-sell opportunities, and facilitates a seamless omnichannel experience.

The key lies in how the implementation of MDM can be made easy with minimal disruption and investment. Businesses can demonstrate significant progress on their MDM journey when they go for lightweight solutions.