Over the last few years, increasing numbers of organizations have taken the plunge and invested in analytics and data science. They have recognized that it is more and more important for business decisions to be data-driven, not based on ‘gut feeling’ or the whims of those in charge.
However, it is also fair to say that many of these organizations have not seen the desired return on their investment—or rather, they have not seen the rapid return on investment that they were expecting. It has often taken considerably longer than anticipated to become data-driven, and to move from isolated pockets of analytics use to analytics being ‘how we do things around here’. It is worth considering why this might be the case, and perhaps more importantly, what we can do about it.
A complex world
First, let’s unpick what we mean by analytics and data science, because it is a complex area. One of my colleagues recently wrote a very helpful article about the changing scope and nature of data science. This explains that ‘data science’ consists of about 15 different activities, from finding, validating and merging data through building models to operationalizing and deploying them, then model monitoring, governance and retraining. Linus also commented that few data scientists are able to carry out all of these activities—or at least, not well. Most prefer to specialize in one or two areas from the 15 activities, and focus, say, on developing models, or operationalizing them, rather than trying to do everything.
Linus also talked about who within the business was best placed to do each task. He highlighted a ‘business–IT split’ in the work. He explained that some tasks need more IT input, and others need more input from business teams. The examples that he gave were that finding the right data was really a business team function, because they were better placed to know what data were available. However, developing analytical models was more of an IT function.
Why does this matter? Because it shows that it is not enough to simply decide that you want to start using analytics and data science, or even that you wish your decisions to be data-driven. You cannot just pay for software, recruit a data scientist, or even several, and simply leave them to get on with it. Similarly, it doesn’t work to provide your business teams with analytics software and hope that they will be able to use it to answer business questions. It’s just too complicated.
Operationalizing analytics
Instead, you need to consider how you will operationalize the use of analytics and data science throughout the organization. You have to develop a culture of analytics and data use and enable teams to develop the skills needed to use them. Ultimately, only this will enable you to deliver value from your investments in analytics and data science.
In practice, realizing value for money from an analytics investment is a matter of developing three areas across the organization:
- Knowledge, which means knowing when and how to use analytics most effectively, and understanding the capabilities that are available
- Skills, which means the ability to use analytics effectively, including advanced techniques; and
- Abilities, and particularly being able to define analytical problems clearly, communicate the results effectively to decision-makers, and demonstrate analytics value.
Driving change through leadership
Delivering a programme of change, such as digital transformation, or becoming data-driven, needs leadership. Over many years, we have seen that strong analytics leadership is key to accelerating the realization of business value from analytics investment. It enables cultural change and drives the necessary evolution of the business towards data-driven decision-making. It is crucial to making analytics ‘the way we do things round here’.
However, there is more to analytics leadership than simply driving change. Strong analytics leadership also makes an organization more attractive to data scientists. Data science is a shortage area. Data scientists are in high demand, and they can therefore pick and choose their employers. They are more likely to want to stay in an organization that both values them and understands their needs and wants. In other words, talent retention depends on providing an environment where data scientists want to work, and strong analytics leadership is part of this.
Put simply, analytics leadership is the key to accelerating the realization of business value from investments in either analytical software or people. There is no other way.
Join us to explore the evolving scope of analytics leadership
Over the past five years I have had the privilege to work with a number of analytics leadership practitioners as we delivered analytics value training. I will be exploring with them the opportunity to unlock greater value through better analytics leadership. I hope you can join us.
