There’s no doubt: a combination of ubiquitous data and exciting new tools for handling data offers enterprises a range of new options to create value. But becoming a data-driven enterprise is far from easy.
It's not just about having the data of course, it's about making sense of the data. The famous quote from Edward O. Wilson says it all: "We're drowning in information, while starving for wisdom". A much lesser-known fact is that the original quote extends further: "The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely."
The challenge is therefore crystal clear: making sure that you are the best synthesizer to actually produce wisdom for the organisation. This calls for more than just implementing some analytics tooling and solutions as an add-on to existing systems and processes. Currently however, many initiatives are 'one-off' solutions. Many organisations find it hard to industrialise their approach and to integrate algorithms into the key business processes in a more structural manner. This is especially true when it comes to organisations with a diverse IT landscape – often caused by historical growth through mergers and acquisitions. Their challenge is not only to ensure the quality of data but also to harmonise that data.
Now is the time for organisations that want to be leading in tomorrow's world to move to the next level. The way we handle data is changing fast; using tools and developing use-cases in isolated silos is no longer an option. Now that data is so vital – and in fact is the backbone of many enterprises – we should also give tools and infrastructures for analytics a more solid and structured place at the heart of the organisation. Just like the conveyor belt is the backbone of an automobile factory, we need an analytics factory in data-rich enterprises.
To this end, we have developed a solution and approach to help organisations to meet this desired state. We call this solution: 'the Data & Analytics Factory', where all key elements of data analytics, such as a central data lake, data management & governance, advanced analytics and management reporting are combined smartly, to drive integrated insights across the front-, the middle- and the back-office.
This Data & Analytics Factory is the engine (with the key components of people, process and technology) for the synthesis mentioned earlier. In fact it is somewhat of a revolution compared to the other approaches. In the traditional model many companies that want to move away from isolated insights start data harmonisation projects with their focus on aligning data definitions and applying master data management principles. Other companies focus on the realisation of data-driven business insights with a scope that is limited to one business unit.
Both approaches are suboptimal. We need a solid approach to combine data insights across business units and departments. This is where the Data & Analytics Factory takes centre stage. It warrants a more continuous process, one that is use-case-driven and retains the different states of data within the analytics process (i.e. raw, cleaned, harmonised and analytics-ready) so that algorithms can be applied and data analysts can use the data as needed, while it is also available for any other new use-cases/workloads that arise. Moreover, the Data & Analytics Factory is structured and positioned so that it can easily be scaled up and down in response to a changing workload.
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