Our new girl in Analytics, Alex, talks about embedding an advanced analytics culture in business...
The quickest, and potentially most successful, way to create an internal business culture that thrives on advanced data analytics technology and fact-based decision making is to start at the top of an organisation.
To help an analytics initiative succeed, senior executives need to drive an internal emphasis on optimising business performance through quantitative measurements. They should also fund and prioritise analytics projects. But new analytics software and high-level executive support, while a good start, aren’t enough to foster and maintain an analytics business culture. Companies also need to make sure that their employees have the ability to make the right decisions based on information gathered from analytics technology.
“Part of the problem of the financial crisis was that the systems correctly identified risks, but the humans overrode those signals because they were incentivised to do so.”
Analytics programmes tend to work best when employees are truly willing to let their actions be influenced by the technology. Education and training are two of the keys to creating a long-lasting data analytics business culture; training people who are knowledgeable on the different ways of analysing data. Employees should also be educated about the meaning of data as it pertains to their company’s specific key performance indicators and performance metrics.
Another potential way to help foster an analytics business culture within an organisation is to set up a dedicated data analytics group. An analytics group with its own director could develop an analytics strategy and project plan, promote the use of analytics within the company, train data analysts on analytics tools and concepts, and work with the IT, BI and data warehousing teams on deployment projects.
As more and more companies deploy predictive analytics tools and other data analytics software and begin filling the ranks of their analytics team, the pool of available talent is shrinking and hiring costs are growing, according to industry analysts and executives at companies that are in the market for analytics skills.
Recipe for success: A mix of advanced analytics skills and talents
Some of the more successful analytics teams are staffed with MBA-educated marketing experts who work hand-in-hand with statistical modellers. Business knowledge tends to rub off on the statistical math gurus, while the MBAs gradually pick up technical skills, helping to make the teams more cohesive and effective.
Royal Bank of Canada has taken that approach in its pursuit to develop an advanced analytics practice. The bank set up a data analytics team that supports the marketing department helping it to better target marketing campaigns and find new revenue opportunities.
“We’ve got some PhD’s and so graduate degrees in statistics, and then some business analysts that have learned along the way and know the technology. It’s important to have a mix.”
Cathy Burrows, director of marketing services at RBC
At Chartis Inc., as another example, the analytics team includes not just a mix of workers from different backgrounds but also people with distinct skills. Three of the seven staff members are specialists in data modelling and statistical analysis, two are tasked with testing and maintaining the analytical data models, and the other two concentrate on communicating the analytics results to end users via reports and other techniques.
Centralised or Decentralised analytics team?
If finding and hiring talented workers with analytics skills is the first step in establishing an advanced data analytics team, determining how to structure it in relation to your IT and Business Intelligence (BI) groups, and how much autonomy to give your analytics professionals, would be the next step.
Some companies, especially those with highly centralised corporate structures, may be lured to place an analytics team under the purview of the IT department or a standalone BI unit. According to analytics experts, however, the most successful analytics initiatives take a more decentralised approach.
Data analytics teams are usually organised by business function or placed directly within a business unit; an analytics team that focuses on customer behaviour and other marketing-related analysis might be part of the marketing department, whereas risk-focused data analysts are typically best suited to the finance department. This is because developing, testing and maintaining complex analytical data models involves significant domain-specific business knowledge, a requirement that doesn’t lend itself to a centrally controlled analytics programme.
That’s not to say that advanced data analytics teams should be completely cut off from their BI and IT departments. Analytics’ teams are organised by the departments they support, but they should work with each other and the BI staff to share best practices when applicable. Eg. If the risk analysts create a model that could also be useful for the marketing analysts, they might pass it on to marketing management team – they have to be able to communicate.
Another obstruction to a centrally controlled data analytics structure is the variety of tools that are available, and a desire on the part of data analysts to choose the ones they favour and use themselves. While organisations with an advanced analytics program tend to have a default software provider, eg. Experian, many analysts have their own preferred tools and like to experiment with pioneering technologies. Data analysts should be allowed, within reason, to use the tools that are best suited for the job at hand.