Analytics maturity need users who are enthusiastic adopters
It has almost reached the point where there are certain issues about data science that are taken as read. First, organisations need data and analytics. Data-driven decisions are the only way to navigate a rapidly-changing world. Organisations that harness data and analytics first and most effectively will be in a stronger position. Second, there is a shortage of data scientists. Third, even if you can get the expertise you need, it is still extremely difficult to change an organisation and make it data-driven.
There are no easy answers to these issues. Training up new data scientists is happening, but slowly. Data scientists can also only do so much. Training business users is also important, including through various training programmes, such as those offered by SAS.
Developing the answers
Christer Bodell is now Director of Automotive and Manufacturing at Capgemini Invent, and formerly worked at SAS in the Nordic Region. He was involved in developing the original analytics value training programme. Talking to him, it seems that some issues remain constant.
“I saw that when we showcased analytics solutions, there was a lack of capability to receive them and turn data into action. It was frustrating to show people what was possible, and then have them say, but what am I going to do with this? The analytics value programme was a way to reach out and get beyond this.”
Christer highlights the importance of getting the business to take control of analytics, and certainly to “take a seat at the table”. His concern is that data scientists and IT are often seen as being in control of analytics. In a way this makes sense: it’s technical, involves computers and the language of analytics and analysts can sometimes seem impenetrable even to other analysts. However, only the business can identify and decide which questions most need answers. We must find a way to translate between the three.
Analytics solutions as products
One suggestion is that every analytics solution needs to be treated as a product inside the company. In other words, before it is developed, it is important to assess the market demand for it. What problem are we really trying to solve here? What is the value if we succeed? Data scientists need to design the scope, prototype, get approval, produce, and then launch each solution. Without this, solutions may sink without trace simply because potential users do not know about them or have little idea what to do with them. Christer comments that he has constantly seen three very clear challenges to implementing analytics in practice.
“Number one is you have to sell it, and that means you have to get acceptance for it. Two, you have to build it. And three, you have to get it working in the environment. The first two of these has got a lot easier because everybody believes that they want AI. It is also a little easier to build. However, the last piece—to get it properly used—remains as hard as ever.”
Minding the gap
There is clearly a gap between desire and ability to implement. This gap is usually a matter of many small details and difficulties, rather than one big issue. Christer notes that there is also a gulf in understanding that must be overcome.
“It gets complex when you go from showing what has happened in the past, which everybody can understand, to showing something predictive, like a forecast or a predictive model. There is a discomfort in that you can’t absolutely rely on the numbers, in dealing with probability. People need to become more comfortable with how to deal with this type of uncertainty.”
This chimes with my previous experience of providing training for analysts in creating indexes. There was no single number or even right answer. Instead it was about finding weightings that could be agreed. I always felt that getting people to talk about weightings and debate them was a win, because it meant that they were engaging with the issues. This remains a long way from the ideal of data democratisation, but it is a start.
Christer remains positive about the potential to get analytics accepted more widely. He notes that there is always a challenge to get people to look far enough ahead to see what problems can be solved with new technology. It is a bit like Henry Ford saying that if he had asked people what they wanted, they would have said faster horses. The pace of technology advancement is sometimes faster than we can assimilate as humans—but that doesn’t mean we should stop trying to plug the assimilation gap with every means at our disposal.
Moving forward, have a look at Analytics Leadership Program:
