Master Data Management (MDM) has been around since the mid-1980s, but has really come to the fore in the last decade, with many of today’s data governance efforts built on top of existing MDM strategies. This has been driven by the advent of Big Data, an increased focus on Business Analytics and Intelligence, and growing adoption of Machine Learning and Artificial Intelligence.
For the past 25 years or so there have been no major leaps in how providers have built or provisioned their MDM offerings. Traditional MDM solutions still require you to implement strict controls over every aspect of your master data management process—from data acquisition to data storage, and from maintenance and modification to security and access control. These systems were built for the on-premises, siloed institutions of the past where data ownership lay almost exclusively with the IT department.
Modern approaches are more aligned to how most enterprises operate today - in a hybrid, highly distributed and fluid fashion. Data is a valuable business asset, which means that technology and business users are equally responsible for its maintenance and use. This does not mean that everyone in the business needs to be a data engineer or architect. What it does mean is that everyone is, to some extent or another, a data steward and a data citizen. It is the job of technology to enable these roles and ensure that everyone with a stake in an organisation's data benefits from its potential. Which is where Modern MDM comes in.
What is Master Data Management?
At a fundamental level, Master Data Management (MDM) is the process of creating and maintaining a single, consistent view of your organization's critical data. MDM is closely related to data governance, which can be thought of as rules for how data is collected, processed, stored and accessed. It also includes policies on how data should be handled, such as how long it should be retained and what access permissions are granted to different groups of people or individual data owners.
Master data is the set of identifiers that provides context about business data. The most common categories of master data are customers, employees, products, financial structures and locational concepts. Master data is different to reference data, which is data that refers to other objects, but does not contain identifiers that represent different types of master data entities. Whether there is still a need for reference data in the context of what can be achieved with modern MDM is debatable, but that's a discussion for another time.
What's the problem with traditional Master Data Management solutions?
It has been estimated by Gartner that up to 85% of MDM projects fail. That's a big number. Little wonder then that so many organisations have been burnt in the past and aren't exactly falling over themselves to start another MDM initiative.
There's a number of reasons why this number is so high:
- The upfront planning process - data profiling, analysing and modelling is time consuming and expensive. Many traditional MDM projects take over a year to deliver any ROI at all.
- A domain-by-domain approach, such as that used by traditional MDM systems, causes complexity and creates new silos, restricting how the data can be used.
- Traditional MDM demands high manual and technical intervention, which is both costly and time-consuming.
- Because traditional MDM systems are built on relational databases with only direct relationships, connections are manual and add to the maintenance overhead.
- Due to the upfront profiling and modeling requirements, you're always playing catch-up with your data as it changes. This adds to the complexity and need for manual intervention, further delaying projects.
In spite of all of the above, the fact remains that businesses need to be able to use their data to fuel the projects that will move them forward. Whether these are customer, product, supplier or employee focused initiatives, they all rely on data to provide insights to inform them. At the moment, many organisations are using their data in this way, but the data is neither consistent nor reliable. Which means that the results and recommendations aren't trusted either.
The modern approach to Master Data Management
Modern MDM seeks to solve the above issues in a number of ways.
- By managing all of your data - master, meta, reference, structured and unstructured. Suddenly, the potential use cases for your data have multiplied exponentially.
- By eradicating the need to model your data upfront. Modern MDM embraces data in its "raw" form from hundreds, if not thousands, of data sources. The potential cost and time savings are huge.
- By removing repetitive and manual tasks from the outset. Automating manual tasks like data cleaning reduces the burden on the client and frees time and resources to work on value-orientated tasks instead.
- By being truly Cloud-native. Most traditional MDM platforms were not born in the Cloud, they were built for an on-premises, highly structured environment and then tweaked for the Cloud. Modern MDM platforms were built for the Cloud - which means that getting up and running is quicker and easier, you can scale up or down at pace, and you benefit from the Cloud economic model.
- By providing proactive data governance. Establishing trust in data means having full visibility of its lineage and controlling what happens to your sensitive data in a transparent way. Meeting compliance requirements and demonstrating how data is protected won’t slow you down anymore.
You may be wondering what is so different about modern MDM systems that makes all of the above possible. One major difference is that modern MDM systems like CluedIn are built on a NoSQL, schema-less database called Graph. In the world of Graph, the relationships between the data are as important as the data itself.
A really simple way to think of it is similar to the difference between organising your data into neat rows and columns in Excel versus jotting it down on a whiteboard. With the whiteboard you can visualise the relationships between the data and add the connections as they emerge. This is exactly what Graph does - as the data is ingested, it allows the patterns and relationships to surface, and is then able to organise it into a natural data model. LinkedIn, Facebook and Google are all built on Graph, and the same principles of schema-less, scalable modeling now apply to MDM.
What does the future of Master Data Management look like?
In many ways, the future of Master Data Management doesn't look like Master Data Management at all. Where traditional MDM systems were siloed and slow, modern platforms are integrated and quick. Where the old way of approaching MDM dictated set rules and structures, the new way embraces freedom and flexibility. And if we accept that these concepts shouldn't only apply to Master data, but all data, then the concept of Master Data Management becomes almost entirely redundant.
At this point in time, CluedIn is the only MDM platform that uses Graph. This will change as established vendors and new market entrants recognise how powerful Graph can be when applied to the management of business data. And that's a good thing. Right now, forward-thinking businesses that want to use their data to react to market forces, competitive advancements and customer preferences have a very limited choice: traditional MDM or CluedIn. As the market continues in this direction, a new category will emerge and we will no longer talk about traditional or modern approaches to MDM. In fact, there's a very good chance that by that stage, we won't be talking about MDM at all.
