Author: Chris Brown

A common problem I am asked to opine on or solve goes something like this:

“We’ve built a great business and are piling on the customers but we need to get our data together to really understand what they want.”

Or…

“Our company has been going for years and we have piles of data about our customers but none of it matches up – it’s stored all over the place and we can’t work out what our customers want, or even why they are our customers!”

Regardless of whether you are a 100-year old bank or a 100-day old start up, the data problem is common. It is a symptom of:

• Multiple business stakeholders with different information requirements
• Multiple sources of data, both within and external to the company
• Multiple technologies and systems in play

All companies want to be ‘data-driven’ but I’d argue that a lot are ‘data-constrained’ at the moment. For most, the effort of collecting, corralling, and mining information from data is enormous and can be a real drag on the business. The advent of big data, cloud computing, and machine learning has only brought this problem into starker contrast.

When I ask these companies to describe their data platform, I’m met by a range of reactions from blank looks to descriptions of the most amazing cloud warehouse technologies deployed, or about to be deployed. But defining a data platform is not about installing a multi-petabyte data lake or warehouse. Sure, these are a major component of successful data platforms and will play a part for established enterprises but I’ve also seen multi-year, multi-million dollar projects to build warehouses that have ultimately disappointed their sponsors.

So, if a data platform does not necessarily mean a data warehouse, what does it mean?

I’d posit this definition of a data platform:

A defined set of processes, systems, and people for collecting, fixing and analysing data to produce information of value to the business.

The aim of the platform being to produce a canonical and dependable reference data set for the business to achieve its aims. The scope of a platform definition encompasses all aspects from collection, ingestion, processing, and learning to ultimately surfacing the results to the business users.

Using this definition you can see that any size of company can describe a data platform:

- A start-up can have a willing and eager volunteer armed with spreadsheets build a weekly snapshot report for the founder’s business KPIs;

- A FTSE100 enterprise can generate a streaming, 360 degree view of customer behaviour to display in real-time dashboard visualisations.

Obviously the more sophisticated and automated the platform can be the better, and at some point the sheer scale of data will overwhelm the willing volunteer.

The common factor is that stakeholders of any size business need a clear vision of what great looks like when it comes to getting information from their data, an honest assessment of their current capabilities and an appraisal of whether they can get from now to that future target state with their existing team and technology. They need to define their data platform and have half an idea of how they are going to build it.

Chris Brown is a data professional who specialises in building platforms for companies to analyse data for business value. Throughout his career he’s been instrumental to many data driven firms, focusing on platform architecture; data science and insights; and building a digital bank from scratch. Chris joins us to share his insights on the fintech landscape; expect to learn about data engineering and data science, the areas he’s passionate about.