I was recently helping someone buy a new laptop. Their specifications were pretty light on as they really only needed something to browse the web, type up the odd word document and check emails. The core buying decision was that it had to be small and light. What struck me during our shopping was a particular conversation we had when we were looking at a nice slim ultrabook. “Oh no…where’s the DVD drive? It has to have a DVD drive!” It was then that I asked when was last time they used the DVD drive in their current laptop and they responded with “a while ago, but I might need it to watch a DVD one day.” So we then ruled out small light laptops in favour of a bigger heavier model that could fit a DVD drive. So much for requirements gathering!
So my experience represents a very common and often under-estimated component of any data analytics project. That is change management. It’s not a case of “if you build it, they will come”. You have to change the way humans behave to make the project a success. That of course is easier said than done.
In every data analytics project there are the usual issues to cover off including data quality, data volumes, privacy, security, cost, infrastructure and governance. But I often don’t see or hear change management discussed. How will they manage the human and process changes necessary to make the most of the analytics platform and solution they are building?
A Data Analytics initiative challenges the culture of your organisation in more ways than one.
I recently read a story about a software company in the US with a large customer base. They were implementing an analytics program that would enable both multi-channel customer communications and upselling. To achieve these goals, the company needed to change the way it handled sales data (replace batch processing of sales information with real time processing) and communicates with customers (replace blanket email communications with more personalised, one-on-one communications). Therefore a significant amount of human change management would be required to make this particular analytics initiative work.
Changing people to do the things the way we want them to do it is no easy task. Changing human behaviour won’t be performed overnight but rather is a process done in small measurable steps over a long period of time. One approach to consider is the living model approach. In fact this approach suits the concept of agile analytics. It’s all about building prototypes of analytics and engaging with the users to trial it’s use. The concept of “fail fast” comes into play here because if it doesn’t get take up at this stage then maybe it’s not worth pursuing in the longer term. Switching to a different industry as an example is Nike. What you see on the shelves is the end product of a process that has been tested in small market test groups. Colour, texture, price are all evaluated in small batches to see if people would be willing to make the change to the shoes. The same applies to data analytics projects. Market test what the end users want by setting up small agile analytical environments and having the business test and provide feedback.
Another change management tactic to consider is the one outlined by Brynjolfsson in his paper “The matrix of change” In it he outlines the use of the matric of change model. The model aims to determine three points.
- Which processes need to change
- Which processes can stay the same
- And how the processes interact
The model pits the old practices against the new ones to determine which are opposing and which are reinforcing. The goal is to implement processes that reinforce one another. With enough reinforcing processes, change is feasible.
Figure 1. The matrix of change model by Brynjolfsson
Finally analytics projects should not be treated differently to any other project in the organisation. The same rules apply whether you are moving office locations, replacing the fleet of company cars or deploying an analytics solution that changes the way you interact with customers. With this in mind you should look at Kotter’s 8-step change model.
You can have the greatest technology in the world, the best people to deliver an analytics project and a solid timeline for delivery, but if you don’t win the mental approach with your business user’s to make the change, then your analytics project will be doomed for failure.
Ben Davis is a Senior Architect for Teradata Australia based in Canberra. With 18 years of experience in consulting, sales and technical data management roles, he has worked with some of the largest Australian organisations in developing comprehensive data management strategies. He holds a Degree in Law, A post graduate Masters in Business and Technology and is currently finishing his PhD in Information Technology with a thesis in executing large scale algorithms within cloud environments.
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