Implementing an Effective Intelligent Master Data Management Strategy

Most enterprises have re-aligned their business models to accelerate digital transformation and revitalize data analytic tools to understand changing market needs and/or customer preferences. But still, there is a long road ahead.

While data collection and management across different sources is now standard practice for enterprises, there are high chances of it being fragmented, siloed, or even uncleansed. Any form of data inaccuracy can hamper the company's decision-making ability across channels, further deteriorating business growth and customer satisfaction. In the current scenario, especially, many businesses have had to overcome losses, often at the cost of their data management processes. From channel partner proliferation to detailed catalog information, the increase in data churn requires robust quality tools to enhance operational excellence in the business.

The imperatives of master data management

Legacy processes are not enough for data handling, governing, processing, and visualizing. Giving data sets the right structure, hierarchy, and versioning by consolidating it to a single source of truth – Master Data Management (MDM) plays a crucial role in enabling smart business processes.

From real-time validation to unified creation across the business' ERP, having a golden record in place simplifies multi-channel data domains and repositories. It helps the organization drive actionable insights from the data to adapt to the ever-changing market needs and explore new opportunities for revenue growth. Further, it enables creating an integrated view of products, customers, suppliers, materials, and other data sets while synchronizing customer information across systems and the organization's supply chain.

In the case of merging records, an MDM solution can also correct any inconsistency or repetition in data, capture where the data came from, and create an audit trail for required changes or corrections. It also provides transparency within a trusted framework offering visibility into how each master data record is being created or modified.

Since an MDM solution streamlines and manages crucial data related to multiple sources, channels and departments, it cannot be implemented without a well-defined strategy.

Let's explore a few crucial steps that need to be addressed in order to ensure your master data management strategy is a success.

Define clear data management objectives

An organization's master data vision must align with its business vision to better identify critical success factors along with clear achievable objectives – functionally, technically, and financially. For starters, the business case must define the 'Whys', 'Hows' and 'Whos' of the MDM exercise to identify and acknowledge business pain points and data issues. Addressing these issues at the earliest stage helps gain the buy-in and approval of all key stakeholders to endorse the business. While being drawn and tested, the conceptual architecture of the MDM strategy needs to be prioritized, keeping in mind policies that need to be drawn within the organization for enterprise-wide consensus.

Focus on a holistic approach in your master data

Master data assets such as customer products, partners, suppliers, etc., do not exist in isolation. Hence, the potential to use quality data measures on all master data assets is unsurprisingly tempting. But, it is imperative that you focus on a smaller data set at a time from your master data assets. An MDM strategy works best when a multi-phase approach is adopted to tackle a minimum number of entities per phase and then scale it for the next phase. If such a granular model is not adopted, future design and model considerations when building the MDM solution for several entities can lead to master data created from isolated and siloed sources. Thereby, recreating the same problem that the MDM solution was meant to resolve.

Choose the most suitable implementation styles as per your existing IT architecture

Organizations need to be thorough with their target architecture, the technology to be used, and the selection of the Systems Integrator (SI) to define a firm architecture style for Registry, Transaction hubs, and Co-existence. All these factors have an impact on cost on both investment and performance. Adopt the most suitable MDM implementation style such as consolidation, registry, coexistence, and registry. The MDM technology in place should support both analytical and operational processes in real-time in order for it to blend with the organization's overall IT architecture and ecosystem. While evaluating the technology and the MDM vendor, it's imperative for the organization to align the same with use cases from all master data assets to control time and budget in custom developments during implementation.

Establish the right data governance rules

Since MDM is not a one-time implementation or cleansing exercise, business owners must own the data along with the business processes from various departments and units. The data governance process implemented must identify, measure, capture, and rectify data quality issues in the source system itself. In order to keep the strategy running, a formal model to manage said data as a strategic resource should comprise detailed business rules, data stewardship, data control, and compliance mechanisms. The governance aspect of data needs to be treated as part of daily responsibilities rather than a one-off initiative for it to be effective and supported by stakeholders or senior management.

Implement with a defined future roadmap

Before diving deep into the MDM implementation process, defining a future roadmap is crucial in showing how later stages will be accomplished, consistent with the strategic objectives of an organization. This ensures that your MDM exercise does not turn into a catastrophic event due to abject failures from structural flaws that corrupt your entire data system. Further, infuse upgrades, conduct regular testing on standard communication interfaces, and set benchmarks to quantify your KPI success, until they are proven to be stable before opening up the gates to the rest of your data stream. By identifying non-successful factors from previous project implementations, amend and improve your MDM strategy implementation in the future.

Keep a phase-wise check on your ROI

The organization must articulate in detail the parameters and metrics needed to measure the progress of the MDM solution in quantifiable terms across its lifecycle. Since MDM stakeholders belong to different departments within the organization, with conflicting or diverse objectives, a phase-wise check on ROI is essential to keep track of contributing factors of progress and maintaining buy-in for your MDM solution. For instance, when you've implemented the customer domain in your strategy, ROI needs to be checked in terms of increase in cross-sell, up-sell, and the benefits accrued from the quality of reporting made available.

Track post-implementation and measurement

A well-planned master data strategy needs pre-implementation analysis and demand post-implementation observation in a logical manner to get more practical business benefits. Employees, higher management, and stakeholders must work together to achieve the common goal with continuous suggestions, feedbacks, and improvements. The chances of an MDM initiative being successful increase significantly if organizations:

• Consider and implement MDM in a phase-wise manner than as a one-time initiative.
• Measure ROI right from the business case formulation through to post-implementation.
• Create an organizational structure that addresses data quality and governance issues at regular intervals.

Encourage continuous improvement

All personnel and departments must be trained and regularly re-trained on how to format, enter, store, and access data. This helps in gain technical knowledge about the workings of an MDM solution and consistently improves the organization's data as a single coherent system. Similarly, running onboarding workshops with stakeholders can help capture business requirements and specific use cases to ensure all deliverables are met. Adding to this, crucial aspects of the MDM solution such as installation, configuration, data models, data management tools, hierarchy management, and alert queues must be regularly reviewed and audited by teams to avoid delays or confusion when new requirements or challenges occur.

The Conclusion

Organizations need to take a holistic approach to craft their MDM strategy by identifying the right data management use cases that align with the reporting, cross-selling, up-selling, decision-making, and compliance needs of the business. Establish the groundwork and then execute your MDM implementation strategy to race ahead of competitors with fewer roadblocks.