You’ve formed your data team and you know which data sources you want to bring together for analysis and reporting. But how do you make it happen? Our roundtable provided some answers.
“Turn your data into insight” is a phrase so often used in tech vendor marketing that it’s become a cliché.
It always sounds so simple, and yet anyone who’s ever faced the task of actually turning raw data into valuable business insight knows it’s anything but.
There are so many moving parts: the different data sources, the technology options open to you, the resources you need to get the work done, stakeholder politics, available budget, etc.
At our recent ‘Put Your Data to Work’ roundtable, we set out to guide businesses through the maze of obstacles; providing practical advice for getting the strategy to the point where it’s making a genuine difference.
Here are four takeaways from the event you can use to make that happen:
#1 Start with the outcome in mind
When your aim is to bring data from different sources together, it’s easy to get hung up on the technology. Should we use our on-premises infrastructure or do it in the cloud (and if so, which cloud)? Do we need a data lake? Which data visualisation tool is the best?
This is never a great place to start, as it detracts your attention from what your business wants to achieve with the data. A more productive place to start is to get all your stakeholders into a room and ask them to write down the business issues they would like to solve.
Once you have a list, you can group the issues into themes, prioritise them, and identify which ones to work on first. We do that using a matrix that ranks the issues according to how easy they would be to solve with data, and the degree of value the business will gain from solving them.
With the matrix drawn up, you’ll have a solid and shareable picture of what the business wants to achieve, and you can easily identify suitable candidates for your first data projects. Ideally, there will also be areas where you can get some quick wins to keep people engaged (see #3).
Another thing to consider early on – and something that often gets lost in the rush to try out new technologies – is how the solution is going to evolve. You’ll have identified priority areas to start off with, but your end-goal is to deliver more and more value to the business over time. If your team isn’t well set up to manage and evolve a data analytics platform, it makes sense to get a partner involved early, who can understand your objectives from the start and accompany you on your data transformation journey.
When you know what you want to achieve and where you want to start – that’s the point to start thinking about the technology you’re going to use to make it happen (see #4).
#2 Get the right people on board
If your data strategy involves bringing data together in new ways, it will inevitably lead to change in your organisation.
You’re likely to be introducing new technologies, which might require new skills (and deprioritise existing ones). You’ll be delivering new reports and new sources of value, which will drive a change in the way people consume and act on data.
You’ll also be providing hard evidence in areas where decisions are currently made based on anecdote, gut feel and HIPPO (highest paid person’s opinion). That will spur changes in the decision-making process – right up to the highest levels in the organisation.
We all know change can be hard to push through, even when it’s positive. So, recruiting sponsors and champions for your project is critical. Having an executive sponsor for the whole strategy is wise (we saw one project crumble because the executive sponsor didn’t turn up to the launch, which instantly made others feel the project wasn’t worth bothering with).
But look out for other champions, too: people at all levels in the organisation who influence what others think. The more of them you can get onside, the more likely your strategy is to succeed.
It’s sensible to look for detractors as well: people who might try to stall or undermine your project, or simply refuse to get on board with it. Knowing who these people are and having a strategy to win them over will be helpful.
#3 Identify some quick wins – and win them quickly
You’ll be much more likely to win support and funding for your data strategy if you can show early on that it’s working.
When you draw up the matrix in point #1, you’ll identify the issues that are easiest to solve with data. Once you’ve chosen one or two, the next step is to put metrics against them. What sort of value does the business want them to deliver, and how will you measure it?
It could be a hard business metric, like increased revenue or decreased customer churn. Or it could be something like a reduction in the time to get data for reporting, or an increase in reporting accuracy.
Whatever you decide, aim to show as quickly as possible that the benefit can be achieved. The Agile methodology can play a big role here, as you can deliver functionality quickly, in short sprints, and get feedback from business stakeholders to see if it’s meeting their needs.
Getting some quick wins won’t just help to keep people onside and excited about the changes ahead. It’ll also help to build a solid business case for further investment.
#4 Choose the right technology: Start simple but with scope to expand
Although technology should never be your first consideration, it is still a critical one. When we build analytics solutions for clients, we always look to build the solution in the simplest way possible, but with scope to extend the platform later if needed.
Taking the simplest route could mean drawing on skills you already have, rather than hiring a raft of new developers and data scientists. It could also mean using familiar technology rather than leaping into something new.
Although your aim is to keep it simple, always keep an eye to the future. Look for a platform that will let you add new functionality, scale up and down, and keep costs optimised as you grow. Realistically that’s likely to be a cloud solution (as a Microsoft Gold Partner we highly recommend the Azure stack), which – if your current analytics solutions are on-premises – will have its own implications for your skills, processes and budget spend.
Project resourcing will be a key consideration, too. Once you’ve decided on your technology platform, you can decide on the right mix of resources and division of labour between your inhouse team and any external contractors or consultants.
For example, if you lack inhouse design and development skills, you may choose to engage an external consultancy at the start, with an objective for them to transfer the required skills to your team. And if your inhouse team will be working mainly on developing new integrations and reports, you may choose to outsource the ongoing support, maintenance and evolution of your platform to a third party, to ensure it’s kept up to date and its performance is optimised.
Adatis can help: ask us about our half-day Solution Envisioning Workshops
Many organisations make the strategic decision to become data-driven, but get stuck when it comes to putting the strategy into practice. If that describes your situation, Adatis can help. We’ll facilitate a half-day Solution Envisioning Workshop for three business stakeholders, to identify the business issues you want to solve, and set you off in the right direction to build a pilot or proof of concept. To find out more, contact us on email@example.com.