Over the last few months, we’ve had several conversations with clients about data models and master data management (MDM). We’ve been asked about the benefits and challenges of embracing a data-driven enterprise, and what the journey looks like.
Opportunity knocks. Transformation awaits.
The concept of capturing all the data generated within an organisation and using it to understand how the business truly works, and how customers interact – and why – is a sensible one.
When data is visible and accessible for decision making, you can move on from a theoretical view of the workings of the business. You can say goodbye to whiteboards and finally see under the covers. When you’ve got that launching pad, you can not only optimise and address pain points and bottlenecks but leverage AI and machine learning to be truly transformative.
With increased visibility and information, business opportunities and processes are opened up that would not have previously been possible. For example, you can derive considerably more insight into how your customers engage with you and why. Or, if you have an extensive (and likely expensive) asset base, you can forecast the need for preventative maintenance and how much you need to spend – and when.
With a holistic view of your data, you can forget making assumptions and see the business as it’s really operating.
With every opportunity comes a challenge. Or two, or three.
All data is not created equal. Making the right decision from the outset about which data you will benefit from, matters. To clarify this point, let’s look at the difference between data and information.
- Data is an individual unit that contains unprocessed raw material. It has no context and can’t be acted on.
- Information is categorised, contextual data from which you can act on and derive outcomes. So, it’s valuable to the enterprise.
There are further levels of complexity when you realise that within the one business, tracked information may be described in several ways. One department can differ from another in defining, for example, a customer, lead, or prospect, or a sales quote vs a sales order.
A failure to define and agree on what a term means and encompasses often leaves companies unable to generate consistent reporting analytics. Anything built to act on the reports won’t function correctly because the underlying data at the very foundation doesn’t align. And as you can imagine, sorting it out retrospectively can waste weeks of time and chunks of budget.
A further technical challenge is to ensure that every system within the business uses the most up-to-date data. The last thing you want is a customer who has been put on stop credit continuing to purchase on account because the point-of-sale account data only updates nightly.
Rising to the challenge.
As outlined in the previous section, there are several challenges to address before your data starts to deliver gold.
The first step is to create an information architecture – a shared understanding of concepts (sale, customer, employee, etc.) across the entire business. Simply put, you all need to be speaking the same language.
When selecting an information architecture, there are two main approaches:
The first is to adopt the traditional “enterprise” information architecture that takes a top-down view of the entire company and its data flows. It tries to capture all entities within the organisation, including how they act, and how the data moves around. In essence, it produces a snapshot of the full company and everything within. And it’s a great idea. But it’s a massive undertaking and requires a debrief from each staff member about every piece of information that passes before their eyes. This makes it an expensive and time-consuming exercise. Typically, by the time you’ve finished, everything you’ve learned is obsolete.
By comparison, business information architecture is hot, and for a good reason. It takes an ‘agile’ approach to identify core business ‘master’ data (customers, employees, etc.). It’s focussed on delivering a small vertical slice to demonstrate immediate proof of value and enables you to build sustainable, manageable processes. All of which is more appealing to most businesses.
Once the architecture has been created, you need to put it into practice. The key to this is a Master Data Management (MDM) solution. While the MDM concept has been around for decades, its more recent availability as a cloud application has driven down cost and seen it gain traction in the mass market. It’s becoming as indispensable in the medium-enterprise market as an ERP or CRM.
An MDM platform provides your business with a single point from which your systems can obtain up-to-date master data. An MDM takes data from multiple sources (for example, your ERP, sales module, and CRM) and combines it to provide a full view. So, an employee could get real-time base information on a customer, their purchase orders, and their current credit status all in one glance. It also provides tools that allow data to be manually reviewed and approved if required (data stewardship).
While you could channel every bit of data through your MDM, we don’t advise it. Instead, we recommend that you invest time (or ask us) to define what information enters the solution and from where. This includes classifying your entities as a core entity that provides the master data used across the company. And ensuring that the data meets quality standards.
Summary
Like most transformative technology decisions, it’s the approach that makes it successful or otherwise. While traditional information architecture was a sticking point for many businesses (budget blowout, anyone?), adopting a more agile strategy and using the appropriate technology streamlines your path to the wealth of opportunities a data-driven enterprise provides.
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