Most businesses have the same problem when it comes to data…
They are data-rich – with megabytes, gigabytes or terrabytes of data being stored, transferred and processed in their organisation everyday. Yet they are insight-poor – with a lack of data-driven decision-making and no actionable insight.
The reason for being insight-poor often comes down to four data experiences:
- No matter what size business you’re in, you will have multiple, disparate disconnected data sources – think about your customer data, financial data, marketing data, social media data, website data, etc – it’s easy for this data to remain on islands forever
- The hunt for a single source of truth seems so far-reaching that it can feel unachievable, never-ending and therefore sits low down on the priority list – how many times have you talked about a single view of customer/employee/asset?
- Many people hate data – they get spreadsheet-blindness, dashboard-dizziness and run away from code, so it’s often avoided and ignored until it becomes a major problem
- It’s really tricky to translate data into actionable insight, as it requires both a really good understanding of data and a really good understanding of the way the business works – and it’s hard to find people who are good with both data and the commercials
So the result is that organisations are often data-rich, but insight-poor, and this results in…
- conflicting numbers in reports and dashboards causing a lack of trust in the data and, therefore, a reluctance to rely on it for decision-making
- data being collected for no real reason, so the costs for data storage end up higher than the commercial value derived from that data
- a culture that makes decisions based on whoever shouts the loudest or whatever the current business priority is and/or a fear of data and a status quo that means that it’s normal to make decisions blindly
- a lack of measurement of what works or not, so mistakes are repeated and successes are hard to see and impossible to report on
- a growing risk that there is a lack of data compliance, controls, security and governance in place to manage the growing data risks
So how can you transform your business into an insight-rich organisation making data-driven decisions?
There are 7 steps you can take…
STEP 1 – start small.
- Identify a part of your organisation where you can make a difference quickly, as a proof-of-value to the rest of your organisation. This could be one department, one product range, one location or another discrete part of your business. It could be the part of the organisation that’s the simplest, so it’s a quick test case. Or it could be the part of the organisation with the biggest problem, so it provides a solution to a real business need first.
- Wherever you start, this test area enables you to make a difference quickly so it’s easier to get business buy-in before asking them to commit to a larger, longer data project.
STEP 2 – prototype-mindset.
- Think about the first project as a prototype…a project with the simple objective of proving or disproving the hypothesis that your data can be used to add some kind of value to your organisation. This mindset will enable you to experiment quickly, in days/weeks rather than months/years, without fear for what the end solution will be.
- Even better, think about your prototype as being 100% disposable – it’s purely a test project so there’s no need to worry about all the things you have to worry about when you build the full end-to-end operational solution. Again, this will enable you to deliver your first project in days/weeks, for a low cost, rather than months/years.
STEP 3 – define requirements.
- Before you touch the data, focus on the business goals…what do you need to achieve with this first project? Perhaps it’s to have one dashboard showing 2019’s business performance on one page, so the management team has good visibility in one place. Perhaps it’s to have a sales forecasting tool for the next 3 months, so you can plan resources more efficiently. Perhaps it’s to automate a currently time-consuming manual process, to improve efficiency and reduce costs.
- Focus on the business purpose (what will it be used to achieve?), the business user (how will they use it?) and the business levers that can be pulled as a result of seeing the data insight (what actions can they take?).
STEP 4 – extract data.
- Identify, target & extract only the data you need – this will usually be a fraction of the data you have in your organisation. It’s crucial to only work with the data you need otherwise you risk drowning in data and creating unnecessary data complexity.
- The data will probably need to be cleansed, processed and combined in multiple ways – there may be data gaps, duplications, calculations required, exception rules, model algorithms and more. You may be better off using off-the-shelf data tools to do this work for you, depending on the size and complexity of your data challenge versus the costs.
STEP 5 – visualise data.
- Now you’ve done all the complex back-end data work, the trick to data visualisation is to make everything look super simple. Dashboards & reports should be intuitive and NEVER need a user guide or instruction manual!
- Design with your user in mind – who are they? What do they know? What insight do they need? What actions will they take with that insight? Create a solution that works specifically for that user and make it easy for them to do their job.
STEP 6 – train users.
- Build it and they will come? The world of business is littered with the remnants of unused reports and dashboards – you know the ones that appeared for a few months and you never saw them again. The creation of a data tool is the start, not the end. The technology is the easy part – it’s the cultural change, to become data-driven, that’s the hard part.
- Train your users – show them how it works. Explain how you’ve designed it to meet their user requirements. Demonstrate how they can derive actionable insight. Show them how they can make business decisions using that insight. Then stop…listen to their feedback…update your visualisation…and iterate until it works for your users and they really, truly understand how it can improve the way they work.
STEP 7 – repeat steps 1-6.
Don’t know where to start?
We can help you. Drop us a line at email@example.com and we’ll support you through all the seven steps one-by-one. We make data simple – we do the hard work, so you don’t have to.