At MAC we have recently been having discussions about the concept of Data Driven Agility; how can organisations start to leverage data more intelligently in an agile-delivery process, select data which is fit-for-purpose and, importantly, help solve business problems?
For anyone who isn’t familiar with agile, here’s a summary: Agile teams are best suited to innovation – that is, the profitable application of creativity to improve products and services, processes, or business models. Bosch, a global supplier of technology and services with over 400 000 employees and operations in more than 60 countries, took this approach. As leaders began to see that traditional top-down management was no longer effective in a fast-moving, globalised world, the company became an early adopter of agile methods. Bosch’s first attempt to implement what it called a “dual organisation” – one in which new businesses were run with agile teams while traditional functions were left out of the action – compromised the goal of a holistic transformation. In 2015 members of the board decided to build a more unified approach to agile teams. Today Bosch operates with a mix of agile teams and traditionally structured units, but it reports that nearly all areas have adopted agile values, are collaborating more effectively, and are adapting more quickly to increasingly dynamic marketplaces.
When you are building data capabilities in an organisation, naturally it is going to be reliant on hiring a set of data analysts, scientists and a data journalist (have a look at the previous article in the series if this is a new concept for you). These people need to become your data champions inside the business before they start to empower other people in the organisation. Thus, a large part of the whole maturity process is educating your organisation and taking them on a journey with you to explore what data is available and what it can be used for.
In an earlier article in this series, we highlighted the ‘Sit. Crawl. Walk. Run.’ Principle with regards to data maturity, and if this is actioned correctly, you will get to a point where your data is relatively mature and there will be an abundance of data at your fingertips. Data agility, however, is about selecting the right data to leverage to actually get you an answer, not simply to dive through everything you have collected every time you are searching for a solution.
Organisational lessons have to be learnt to be successful. Rely on your data champions in the organisation, and do not be scared to ask for more data. It is often trial and error as there is no perfect science for every business as each business is unique, but over time you will come across pockets of data which can be useful. For instance, if people are asking you for the same sales reports week in and week out, but suddenly start asking you for something different, you should be able to know how and what to source should a bespoke request come up.
It is not just about understanding what data is where and how to use it, but also, how you can leverage the data to help solve business problems in an agile way, whilst simultaneously applying agile methodologies inside the organisation. There are likely already specific problems you need to solve, be it a daily task, monthly task or a strategic objective, and you are most probably already doing it in an agile manner, but can you now leverage data to help you drive that agility forward? As much as the data team can deliver solutions following an agile delivery method, success often depends on helping driving data adoption inside the organisation. Hence, non-data agile teams should be educated on the possibilities with regards to data, and how asking the right question can enable the organisation to become more agile through strategically leveraging data as an asset.
An analogy would be walking into a car dealership and asking someone sitting behind the sales desk to give you all the technical specifications of the cylinder heads of a particular model – they may not know what you require, but the mechanic would, therefore data analysts should be leveraged as domain experts within an organisation to help drive the company forward.
In general, data teams may have to be more outspoken and pragmatic on what they have achieved, and what can be done with their outputs. In such a way, it should be a top-down and bottom-up approach at the same time as some people/business units are naturally inclined to ask for help in certain areas, while with others you need to be a bit more forceful about leveraging data to help drive the analytical prerogative.
For an agile organisation, data can also be leveraged to be agile – and agile means different things to different people. Ultimately leverage data in an intelligent way which does not deviate from your data maturity process; position yourself so that data can be leveraged day-to-day in the operational side of the organisation as well as from a strategic perspective.