Building Your Intelligent Master Data Management Strategy in 2020
Data is the central concept around which the business world revolves, and decision-makers are not flinching from that anymore. Rather, the pursuit to harvest the data further to bring the best business outcomes is a global one. However, while walking the tightrope between application data and business processes, one needs to steer clear of possible challenges arising from a range of heterogeneous and complex applications that produce variegated data.
Industry analysts believe that the amount of data in the world will grow from 4.4 zettabytes in 2013 to 30 billion by 2020. Fueled by data-heavy digital technologies like the Internet of Things (IoT), sensors, video surveillance, cognitive automation, RPA, and social engagement platforms; the figure is expected to boom even more.
However, there is one challenge. With the proliferation of data types, formats, analytical tools, and hosting channels, the constant creation and distribution of data inside and outside of the enterprise are prone to inaccuracy, inconsistency, non-compliance, expiry, and security breaches. Organizations with complex or heterogeneous information landscapes and multiple application integrations, typically suffer from inconsistent master data, which in turn weakens business-process integrity and outcomes. Any number of business applications may be affected, including customer-facing, supplier-facing, and enterprise-wide applications.
Data inconsistencies and redundancies are not uncommon for those who have been in the business world for even a year now. However, the impact can be felt significantly across the different levels of the application—customer, supplier, enterprise, and value chain. The incoherence of data from source to a process can have a ripple effect on the business processes and, in turn, on the business itself.
Now, what can ensure the best results? What can bridge the gap between data consistency and master data management?
The need of the hour is beyond just formulating a data strategy. There is an urgent need for an intelligent MDM strategy. Now that we have the goal figured out, it is time to look at the ‘how’ of creating an intelligent MDM strategy. If data is one of the foundational aspects of your business, here are a few key aspects that you should keep in mind while formulating an intelligent MDM strategy.
- Clarify whether the MDM is the right response to your existing data challenges
- Assess the readiness of your organization as well as the cultural and political status quo
- Secure the support of involved stakeholders and IT teams
- Set clear and achievable business outcomes
- Keep the cost factor in mind while building your MDM strategy
Strategizing data management means planning the entire value chain of data handling (creation, storage, processing, administration, and distribution) that can happen anytime, anywhere in the organization. A valid master data management strategy requires a complete structure that facilitates collaboration between technology and business. It implies the definition of policies, roles, processes, and responsibilities throughout the company on the definition and management of data. It is transversal across different departments of the organization.
While implementing your MDM strategy, take a stepwise and iterative approach. Start small and think big in aligned with your long-term vision. Then, scale it systematically across the board (by infusing more and more trust in master data). Besides execution, it is always essential to relook, review, and measure your MDM strategy in the context of your organizational priorities and objectives. Based on the analysis, further advance your master data management capabilities so that you can timely unlock more business value in running, growing, and transforming your business strategy. MDM is a never-ending process; and attention and commitment is all it needs.
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