Four tips to accelerate the data maturity of any business
Many organizations are in despair when it comes to data and analytics maturity. Here’s four tips that can dramatically accelerate how your organization approaches data and analytics.
1. The most important question “Does everyone have the data they need to do their job?”
The very first question to ask is: does everyone in the organization have the data they need to do their current job?
If you can’t answer this question with a strong yes, then this must be your starting point. Nothing else matters until everyone has the data they need to do their job.
Too many organizations struggle to provide the baseline data people need to perform their job. Maybe they don’t understand what the roles are. Maybe they don’t understand what data needs to be provided. Or maybe it’s just not available yet. In any case, not having this fundamental data sets the individual and organisation up to fail.
There really is a hierarchy of needs. At the bottom of that hierarchy, the most important foundation is the data everyone needs to do their job. This seems silly to say. But it’s amazing how many organizations try to build fancy predictive models whilst there’s still people who don’t have access to basic information they need for their day to day.
Giving everyone in your organization the data they need to do their job is the most important starting point in thinking about data maturity.
2. There’s no technological nirvana, just an organisation that embeds analytics into the day-to-day
An industry analyst’s view is often that a business must have specific tools and people in place to qualify as “mature”. But that’s not true.
Data maturity really has nothing to do with the tools you use. It’s about bringing data to bear consistently in every decision that’s made throughout the organisation.
I see a lot of organizations working towards a nirvana of “technological maturity.” It’s a mythical point three or four or five years into the future where everything will be perfect and cutting edge. But I strongly believe this is the wrong approach.
You can have the best technology in the world, but unless it’s used uniformly across your business to drive decisions, you are still an immature business.
3. Achieving data maturity is more about cultural change than technological change
Data maturity is as much cultural as it is technological.
As I said this in the point above, if the conversation is about “what technology stack we need to achieve data maturity” then the conversation is wrong.
A culture of analytics is an organisation where people embrace data to do their jobs. There’s a willingness to take risks, to use data, to try new products, and to move quickly.
A mature organisation is an organisation that embraces data to make decisions. Your technology stack needs to serve this mindset, not the other way round.
4. Momentum leads to maturity
Data strategy is important, you need to know where you’re going. But at the same time every organisation must be agile in implementation. The tech in this industry just moves too fast.
Imagine: you start with a big strategic roadmap. That alone takes nine months to develop. Then maybe you build a data warehouse, and that takes another nine months. Suddenly you’re two years down the track, the solutions and technology have moved on, and your budget’s blown. All of the momentum within the organisation has evaporated.
Instead, what if you identified all the quick wins? The tactical stuff that keeps things moving – and profitable. You can have a strategic end-point – a “where we want to be in five years” – but let the quick wins take you there.
That’s real agility – being flexible enough to work towards a goal, but pick up all the extra value along the way. You can push forward with speed, without waiting for a strategy to be built around it.
Think strategically, but be versatile. That forward momentum becomes a strategy.
Ultimately, data maturity is driven by cultural change, not technological change.
You can have the best technology in the world, but does your organization embrace data to solve problems? Do you encourage critical investigation? Do you encourage people to ask for the data they need to do a better job?
Once you have that mind-set, the ‘how’ doesn’t matter. It doesn’t matter if you’re using standard tech off the shelf or a bespoke solution. It doesn’t matter if you have a cutting edge data lake.
What’s most important is a culture that promotes data as an asset and an opportunity. If there’s no culture of data maturity, then technological maturity is a very expensive exercise in shelfware.