I’ve been working with a lot of insurance companies who are struggling to find the right home for data analytics within their organization and I’m struck by the similarity these questions have to the more general evolution of IT organizations over the last two decades. Matt Josefowicz pointed this connection out in a blog post and I’d like to examine the phenomenon in more detail.
When I call this a “struggle” I don’t mean that in a negative way. Often companies try out different approaches with an updated technology or a shift in process or–in this case–an entirely new group because they believe in its value to the business but industry best practices haven’t been worked out yet. This struggle is part of the growth and leadership process. That’s definitely the case now with data analytics and was (and is still!) the case with IT. There won’t be a general agreement about the best practices for a data analytics group within an insurance company for a long time yet (if ever) but we can shortcut some of the learning curve by looking at a similar evolution of the IT organization.
The most common question: where does the role of data analytics fit? Should insurers create a new separate data analytics group or should different data analytics resources report directly to their respective business units? Insurers are trying it both ways right now, and there’s no sure right answer. This is the same centralized v federated (or horizontal v vertical) debate that has gone on for decades in regards to IT resources.
By centralizing data analytics resources together in their own corporate unit, it (1) allows a sharing of skills, tools, and best practices, (2) avoids redundancy and rework, and (3) leads to an easier adoption of a corporate mission for insight and business intelligence. By federating resources out to each operating business unit it (1) allows very tight alignment of business goals with the assigned data analytics expert, (2) helps that expert gain a depth of understanding about the business that they may lack otherwise, (3) better promotes the mission of data analytics to business users.
These are similar–if not the same–as the drivers in IT, and just like IT there are benefits to both approaches. In fact, for IT alignment, while shifting between a horizontal and vertical approach, many organizations have found that the shift itself is valuable, giving employees the opportunity to spread what they’ve learned to others, either in terms of business insight or best practices. So insurers trying different approaches to the organization of data analytics should be rest assured that multiple approaches all have value.
The second similarity between data analytics now and IT organizations of the past is about recruiting talent. These days colleges offer a variety of information technology and computer science degrees, creating a pipeline of potential employees. But that wasn’t always the case, and insurance companies (and companies in other industries) had to staff their IT departments by hiring out of other engineering programs or find people who had a technical aptitude and train them in computer programming.
With data analytics, there’s a similar lack of clarity about who to hire. Some insurers are recruiting PhDs and creating teams of data scientists, others are looking internally for technical staff who have a knack for data insight and exploration and can be cross-trained. But as demand grows, more universities will offer data analysis coursework at an undergraduate or masters level, increasing the availability of trained hires. Of course, just like IT, insurers will be competing against specialized companies to recruit those graduates, and will need to figure out how to attract them to our industry.
If you’ve been struggling with the role of data analytics within your organization or are interested in benchmarking your company’s BI approach against your peers, please feel free to reach out to me. To send me a note or set up a complimentary 1 hour consultation, contact me via email.