In the preceding articles we have covered topics on building the correct foundations so as to implement data analytics in an organisation with a centralised data function. Using the Sit. Crawl. Walk. Run. Paradigm, we explored how best to embark on a data journey and what steps to take to reach analytical maturity. Finally, we investigated ways to ensure organisational trust in your data, all of which lead us to today’s topic; scaling data usage to drive adoption.
Once your organisation has started to mature their data capabilities by leveraging clean consistent data the next challenge awaits. How can you drive scale and adoption? One key lesson I have learnt over the years is to strategically pick your battles. More optimistically, actively search for opportunities in the business where the is an inclination (or need) to leverage data, technology and digital to solve a specific business problem more intelligently. Develop data-driven solutions that solves the business problem through a Minimum Viable Product (MVP) approach, whereby you solve the business problem and show business value. Ultimately, it is all about ROI.
In a typical organisation, one of your key challenges will be identifying which opportunities to take on first. Typically, I like to plot the business challenges on a graph which positions each problem in terms of business impact vs. feasibility. This enables you to essentially pick the ‘low-hanging fruit’, solving the easiest problem first to get a quick win. The biggest mistake here is wanting to solve the biggest challenge first, as it will take the most amount of time. The business wants to see quick wins, tangible results, and a ROI on the efforts.
If you are not familiar with the Fibonacci Scale, which is quite relevant in this scenario, it works as follows; if you are presented with five challenges, prioritise the one which will take the least effort to solve and which has the highest business impact. Once the first problem is solved, problem two should be twice as difficult to solve as problem one (and provide twice as much perceived value), then problem three twice as difficult as problem two and so on. As you continue to provide solutions to problems, you will incrementally be showing value to the business. In a nutshell, aim to solve the easiest problems first and foremost while building trust in your data.
One common challenge, however, is that the organisation may have to undergo a digital (data) culture shift. If your organisation isn’t data-led, you may have difficulty finding where the pockets of opportunity lie, unless of course you are well-entrenched with all the business units in the organisation. It is valuable to identify the stakeholders who would be receptive to you coming in and being solutionist to help solve their problems. It is imperative that you position yourself as a solutionist, rather than a resource. Often, the latter does not provide a platform to showcase the business impact you would like to achieve, nor is it conducive to your plan for building trust in your data.
Once you position yourself as a data champion within an organisation, everybody will come to you with a challenge, and everybody will want to be heard. Pick your battles – ensure that the challenges you solve will show tangible value, whilst ensuring that you and your team are not inundated with requests which could be serviced by the team making the request. Each use case you solve needs a well-defined request, hypothesis and summary of the business impact that it will have should it be solved. Use the ‘fail quick and learn fast method’; take on the problems which will have the highest business impact once solved, learn from your previous efforts, and move onto the next challenge. Scalability and adoption will become natural by-products.