Everyone has data on their agenda, from Board and Exec to IT department, Marketing and Finance. Never before have their been so many shiny terms to describe it all, Data Science, Big Data, Machine Learning and AI. A lot of what's branded new has been around for decades, but what has changed is the pay-as-you-go access to the technology, speed, scale and accessability of it all.
If you’ve been in IT for a while or a Business Information/Data team, you’ll have more than likely experienced at least one project looking to build a data warehouse to rule them all or a consolidation exercise or something similar. If you’ve been very fortunate to have got through the project and it was a success and still is, then you're one of the lucky ones.
Chances are though, the project has completed, the warehouse is live and now your experiencing problems. These problems could be one or more of; there’s multiple copies of the warehouse, some of its out of date, a project want to connect to it in real-time and treat it like the master data or something else. In addition to those problems, it's possible that the extract, transform and load (ETL) process has become painful to maintain.
Even when you have your data warehouse, there’s a constant stream of project needing to integrate one system to another, extract data, move it somewhere and so on.
The initiative may not have been a data warehouse but the organisation has heard the term Big Data and it's become the must do for everyone – the challenge is, what even is Big Data? Big Data seems to be a catch-all term for a lot of data and related activities, but to think of it in that way is to miss an opportunity.
In our experience the pain felt at exec level is that they know the data exists in the IT Systems but it takes ages to get to it or the time taken to develop the integration is frustratingly slow, either way the speed to market is slow and costly and corners get cut or opportunities get missed.
Things get worse if you have Shadow IT because departments may be off performing their own integrations, extracting copies of data and storing it elsewhere or even creating a new data warehouse with their favourite vendor.
With the scene setting out of the way, lets discuss what we should do.
When we talk about creating a Data Strategy, we think about architecture, integration, consumption, managing, storing, validating and analysing data and many other topics but to have success, we need more than that.
When we get asked about Data and creating a strategy, we talk about creating what we’d call an Enterprise Data Culture. A Culture encompasses many great things, but for the purpose of this topic, we’d say a culture is how employees and management interact, how people behave, how departments and functions work together (or not), how decisions get made and perhaps even the risk appetite.
Good businesses will have a business strategy, set goals, created KPI’s and communicate that from executive level down and where this works effectively, businesses tend to do better.
For our enterprise data strategy, we must bring together our business culture and strategy into a plan and document focused on data that together creates our Enterprise Data Culture. Some of the goals we’d expect our Data Culture to enable would be:
In our Enterprise Data Culture, it would be clear how data is onboarded, stored, modelled, analysed, published and accessed. The Exec and Senior Management would make Enterprise Data part of their strategy, making budget and investment available in the right way, mandate that all data projects follow the same approach and create the environment for teams to build resources outside of IT that can use the Data technology stack that the IT team have carefully designed, specified, built and maintain.
Budgets and business cases would not be approved in isolation, but against the wider strategy and how it moves the plan forward. Too many times a business looks at the case for a project without considering the greater good it can deliver, so each aspect is cut back and the greatest loser is often the technical solution. Nobody will ever build re-usable components, do things for the greater good and move the strategy forward if you hold them and their project entirely accountable and require their business case to cover it.
CTO's need to create a data vision and educate, Enterprise Architects should build programmes of work to deliver, Solution Designers need a strategy and reference architecture that is applied to each project to make sure everyone is pulling in the same direction.
The IT goal is to create the unified data technology stack, take care of the technical element and empower the business person (who is the data domain expert) to focus on using and exploiting the data as best they see fit.
Implementing an Enterprise Data Culture and preventing departments making up their own solutions can lead to; saving money on repetitive and similar integrations, the cost of storing the same data over and over, waiting months to do the same work again. GDPR gets that little bit easier as we'll reduce the sprawl, have a greater handle on data, where it came from, where it goes, who accesses it and so on.
An organisation that's informed by data, given the right insights and trust the information has a better chance of making a difference or standing out, but they can't do that if the data is there but not really available.
If this topic has resonated with you but you need support making it happen, get in touch with Metatec and we’ll work with you to get there.