Digital transformation often comes with immense risks that business leaders would rather avoid. Though success brings rewards as many as 85% of all Big Data projects still fail. Could it be that we are looking at digital transformation and data all wrong? I’ve found that businesses often struggle to achieve their digital transformation goals because they rush into them without taking the essential, but often overlooked first step: understanding their level of digital maturity.
Most IT leaders will be familiar with the Capability Maturity Model, originally applied to the process of software development within organisations. This model defined five clear stages of maturity that might apply to an organisation’s software processes, from the initial level of ad hoc and unstructured activity to optimised. The Capability Maturity Model fulfilled two extremely valuable functions for many businesses: it allowed them to assess their current level of maturity, and identify the desired level that they needed to work towards. That then gave a much clearer picture of what actions and investments the business might want to undertake.
After witnessing numerous digital transformation projects across the globe, Domo has created a version of the “Digital Capability Maturity Model” which can help businesses assess their current and required levels of digital maturity, and question what qualifies as digital success, before undertaking digital transformation. Like the original, this model includes five different levels of maturity:
- Digitisation. For any digital project to take place, a business must first digitise its existing information – as much of it as possible. Many people equate digitisation with digital transformation. However, digitisation is only the first and most rudimentary level of maturity that an organisation’s digital capabilities may possess.
- Centralisation and connectivity. At this stage, the business has not only placed its data in one place – like a data warehouse or data mart – but also connected different sources of data across the organisation to one another. Everything that has been digitised is now integrated on what you might call a digital transformation platform – the base layer for new products, services, and so on.
- Action-oriented data. The centralised data, translated into dashboards and alerts, now starts to directly influence the actions taken within the business. Some of these actions are undertaken by people in the business, while others are automated responses. In both cases, actions and data begin to create a feedback loop which ideally drives smarter, more effective operations.
- Predictive data. This is where technologies like AI, machine learning, and predictive analytics come into play, going beyond informing present actions to helping the organisation better understand future states. These technologies also start to reveal insights and correlations that leaders didn’t even know to look for – “unknown unknowns” – guiding their decisions and the overall focus of the business.
- App environments. At this final stage, the intelligence and foresight from the organisation’s data is translated into apps that bring them to where they’re needed most – in the hands of employees, field workers, even customers.
Like the original CMM, you can use this model to assess where you are and where you need to be. A common mistake is to try to apply this to the entire organisation, and spend month first digitising, then years trying to centralise the data. At Domo we apply the maturity model to narrowly defined use cases, which enables us to accelerate delivery and generate action-oriented data and predictive data, which lead to insights and business value, in as little as 6-10 weeks.
But it should all start with a clear vision of what the digital journey should solve. With their end-goals in mind, businesses can make the right technological investments, and ensure every bit of data they collect gradually contributes towards a final, beneficial purpose. This virtuous cycle of increasingly purposeful data is what brings businesses right up to the fourth and fifth stages of maturity, where they can begin implementing technologies like AI or Machine Learning to draw out even deeper, problem-solving insights from their data lakes or analytics platforms. Businesses that mature their digital capabilities initiative by initiative, focusing on smaller areas or business rather than changing the entire organisation in one go, tend to experience much more sustainable results in the long run.
This is the beauty of the Capability Maturity Model – it can be applied to as small or as large an area of business as you want, allowing those in charge to better understand both where they currently are and where they want to end up. Not all businesses will want or even need to reach the fourth or fifth stages of maturity across the entire organisation; some may realise they do not need AI or apps after all. Our customers’ experience suggests that by asking themselves these questions about maturity and consciously assessing their capabilities from the start, business leaders will find it far easier to devise digital transformation initiatives with success in both mind and design.
Join me as I take to the stage at our upcoming Reimagine Digital Transformation breakfast series to discuss how business leaders are navigating digital transformation and data using the five stages of the Capability Maturity Model.