A restaurant cannot survive without customers. It does not matter how cutting-edge or delicious its food, or how many Michelin stars it has earned. The equation is simple: If it has no diners, there will be no income, and the restaurant will go out of business. This insight could be applied to any business, but it also applies to ways of doing business, and particularly analytics.
Many businesses think that it will be enough to develop or buy in analytics ability, and employ good data scientists to work on analysing data and producing insights. But this is no good if the insights are not used. They simply have no value to the business, a bit like the food in the kitchen of an empty restaurant. And also like the food in the restaurant kitchen, insights have a defined use-by date: You can’t use them in six months’ time, because they will be irrelevant by then.
The importance of analytics use management
Businesses therefore need to consider the “consumption” aspects of analytics, as well as the “supply side.” Managers need to take steps to ensure that insights generated through analytics will be usable and acceptable across the organisation. This already matters, but it is likely to matter even more as artificial intelligence starts to fully penetrate analytics applications. Embedding the use of analytics is not simply a matter of putting the right software in place; it is also a major change project, and needs to be considered in that light.
So what can businesses and managers do to ensure that use of analytics – and the insights it generates – become part of how they do business?
1. Leadership matters. Consistently, I have seen that one of the areas with the biggest gaps between the top analytical organisations and the rest was data and analytics governance. The best organisations have a dedicated executive to lead projects on data and analytics, and ensure that they are managed well and effectively. More importantly, this executive can ensure that there is a whole-organisation overview of developments, and that the organisation is aligned around analytics.
2. The C-suite also has to walk the walk. They must all lead the way in using analytics to support business decisions. If they continue to make decisions based on gut feeling and previous experience, then the business will not see the value of analytics. If, however, executives demand analysis and data to support their decisions, and are seen to use the insights generated, then others will also see what is possible. Showing a clear vision of a data-driven future is essential. Executives also play a key role in ensuring that analytical efforts are well-resourced.
3. Using change management terminology, “quick wins,” especially on a smaller scale, can also work. Individual departments or individual business users who are quick to pick up and run with analytics may be held up as exemplars to others. They can act as “centres of excellence” within the organisation to spread the word about how to use analytics effectively, and also to promote its benefits. Their proven successes should spur other departments on to try out a more analytical approach.
4. At the level of individual departments and users, it is important to break down silos, especially between data scientists and business units. Developing trust between the two groups is vital. This means that data scientists will respond helpfully to requests for analytics, and will understand more about the business. It also means that business users will be able to query and explore the answers they receive.
5. Some businesses have taken the breaking down of silos further, and blurred the lines between the two groups by equipping business users as “citizen data scientists”: business users who can – with the support of data scientists – do quite a lot of their own analytical work. And not only do they do their own analytics, but they may also be more involved in collecting, cleaning and managing data. After all, they have a much bigger incentive to make sure it is accurate if they themselves are using it. A few organisations even take individual incentives further, and provide financial rewards for useful insights and recommendations.
Business users can – with the support of #DataScientist – do quite a lot of their own analytical work. #CitizenDataScientist #Analytics Click To TweetTime well spent
These actions take time to show effects. They are, however, well worth the effort and expense because they enable organisations to fully embed analytics within the business, consumption or demand alongside supply. Neither will work without the other.
