MDM (Master Data Management) has become recognised as a key way for businesses to gain consistent and valuable insight into the data they hold, despite that data being typically dispersed widely across different applications and systems.

While the anticipated benefits of MDM from improved operational efficiency to more far-sighted business intelligence and enhanced compliance with regulatory requirements are clear, there are a number of factors that must be considered in implementing an effective MDM programme in order to ensure tangible success and return on investment.

In this article, I identify four best practices that can help organisations successfully implement an MDM solution that produces quantifiable value from their strategic data assets, in a short timeframe.

  1.  Think Big, But Start Small

Finding exactly the right starting point for an MDM project is critical to getting it off the ground. You need to obtain executive buy-in for your project and you ideally need to start producing value before that buy-in wears off, or your executive sponsors will move on.

You should start by picking a clearly delineated area that is currently causing business pain. You might for example, be struggling to reduce time to market or looking to find ways of driving up customer satisfaction levels. Alternatively, you might want to improve your reporting and metrics; or seek out ways of optimising a sluggish supply chain or identify new revenue opportunities to drive up sales figures that have been lagging.

You hence need to ensure that the scope is reasonable and achievable within weeks, months at most. Typically, projects will start by providing a tactical focus on specific areas where ROI is greatest. You might want to drive efficiencies and consequently profitability by implementing new processes and workflow or to create one customer list for marketing and another completely separate one for billing, for example.

Once the MDM project successfully delivers the anticipated short-term returns, its future roll-out path will become self-evident. You can then also start leveraging the ‘little wins’ you have made into other projects and domains. And you can start shifting your focus from short-term to long-term strategic goals such as revenue optimisation or lowering total cost of ownership.

With this approach, however, you always need to keep in mind ‘the big picture’ and ensure that you are not building a disposable project but a foundational one that will allow you to add to it over time, and meet short terms goals that build towards the long-term objectives.

  1.  Don’t Leave Data Behind

MDM projects also need to account for the wealth of ‘new data’ that is now readily available and has become part of the organisation’s extended data assets – including data coming from inside or outside the firewall from unsuspected data sources, or from sources that were simply not accessible before.

This data must become a full constituent of an MDM infrastructure either by being managed in the MDM hub itself, or linked from the MDM hub as part of a federated approach – a model in which the MDM program is responsible only for a limited set of important and shared data elements with everything else locally managed.

It is critical that all of this new data is in some way part of the MDM hub because all of it is potentially of value to the enterprise. It typically includes social data; public/open data and dark data – essentially data that is stored within the organisation, often hidden in log files, manufacturing equipment and/or various other systems – but because it has been so inaccessible in the past and not historically proactively leveraged for intelligence or used to inform forward looking decision-making.

  1.  Incorporate Big Data Into Your MDM Strategy & Vice Versa

Before long, big data will be driving an essential part of the requirements for MDM projects as incorporating new types of data becomes essential. This trend was initially driven by customer-centric organisations or divisions, because having comprehensive and accurate data sets is widely recognised as particularly key in areas like fast response to customer enquiries and customer satisfaction rates. It is now already expanding to other domains such as manufacturing or logistics.

The use of big data technologies adds new data sources from outside the firewall and incorporates both structured and unstructured to add to conventional MDM sources such as relational databases containing structured data to provide a complete view of the required domain.

Adding ‘big’ to MDM does not necessarily mean that the master data hub will be stored in Hadoop (although some organisations are exploring the use of NoSQL databases), nor does it mean that its size will grow exponentially in a short timeframe.

Rather, it means that some of the big data (or new data) will be managed in the MDM hub itself, linked from the MDM hub in a federated approach, or will simply benefit from the consistency and connectivity services that MDM provides. By ensuring better data consistency and quality across an organisation’s big data sets and by helping connect these various data sets, MDM provides the critical foundation on which big data can build.

Companies are also increasingly trying to understand the intersection between big data and MDM, particularly in analytical MDM applications. There is growing realisation that big data represents a potential treasure trove for organisations particularly in terms of applying analytics to drive more informed decision-making but unless organisations are able to manage their master data properly, that data is likely to be inaccurate and incapable of delivering insight or of supporting good decision-making within the organisation.

  1.  Master Data As A Service

An MDM project is not simply about building, governing and maintaining the master data hub. An important dimension of MDM is how it participates in the larger application architecture. Rather than letting your MDM become part of the ‘accidental architecture’ (a complex and difficult-to-manage enterprise IT infrastructure), design it so that it is a full constituent of IT.

In addition, rather than restricting benefits of MDM to directly participating applications, incorporate the MDM system into the enterprise-wide service-oriented architecture and therefore make the master data available to all the systems across the enterprise that might need it in the most efficient and effective manner possible.

At the same time, have it – at the minimum – publish master data to other applications and systems and make its functions (such as deduplication or enrichment) available as services to other applications. This enables the business to deliver analytical and operational master data from the MDM system to all application systems and users across the business, thereby driving greater productivity and enhanced operational efficiency.

As the tips outlined above consistently illustrate, technology has a key role to play in helping enterprises tap into the benefits, but it does not in itself provide the silver bullet to MDM success. An MDM programme will be successful only if it receives continued support from the entire organisation, and, as I have seen, the best way to do this is to deliver new sources of value, early and often.

Too many businesses are still embarking on multi-year journeys without first putting a structured approach in place or having targets to address, tremendously increasing the risk of failure and at the same time guaranteeing technical obsolescence before project completion. By following the advice provided in this article, they stand a much greater chance of avoiding this outcome and achieving value–added business benefits and ultimately on-going success from their MDM investment.