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Like dogs straying from home and finding their way into the neighbors’ yards, data analytics is frequently being sighted all over the insurance neighborhood. Instead of sticking to its traditional actuarial confines, analytic activity and results are being sighted in departments as diverse as Marketing and Claims. What’s the cause of this great analytics wanderlust? The proliferation of easy-to-use retail data analytics tools is offering excellent ways for business users to “wrap their heads around” trends and make meaningful changes. However, insurers should ask themselves who is keeping tabs on the quality of the data and how the data being used.
How are they certain dangerous assumptions aren’t made and redundant processes aren’t wasting time? Is there a better way to reconcile the analytics needs and desires of departmental managers, CIOs and actuaries?
As usual the goal is balance. Organizations that can step into modern analytics while knowingly accepting the increased risk will help business users satisfy their data cravings and simultaneously protect data’s reliability, security and analytical effectiveness.
Is the CIO the dog catcher or the dog watcher?
Recently, CIOs have found themselves in a strange position. While they are ramping up data security efforts to protect the organization, they are also faced with a workforce that is begging to use the data with tools that lie outside the IT organization.
Marketing, for example, may have some appealing ideas regarding campaign analytics and penetration rates. Instead of waiting for fully sanctioned implementations of analytics tools and processes, marketers might decide to take matters into their own hands and find analytics solutions that suit their needs. The CIOs who decide to clamp down could be perceived as inhibitors rather than enablers. Marketing may then regard security measures as dampening competitiveness, a message that could spread to other areas of the business.
Data strategy by credit card
The proliferation of user-friendly, cloud-based data and analytics tools is giving business users what they desperately want; a hands on data and analytics experience. Tools such as Salesforce, Spotfire, Tableau and Yellowfin allow users to get a taste of analytics and an experience with infographic style business intelligence that captures relevant information in a format that makes for good reports and presentations. The tools are often approachable by inexperienced analysts and can be obtained simply with a credit card.
There are positive aspects to cloud tool use, but it’s helpful to understand some potential drawbacks. First, it encourages users to seek and find data to load into the system. Business users who are coming up with data workarounds are likely to also skirt around standard compliance and governance practices, opening up the possibility of using the wrong data in their analysis. A marketing manager might search for submission data, but find and load test submission data instead. Decisions would then be made upon invalid assumptions. Without smart data stewardship, even good data may be used in the wrong manner.
Data fidelity is an issue, but so is security and privacy. With all of the security measures the organization is working hard to put in place, data shouldn’t be allowed to flow like water around the firewall.
The democratization of data analytics
There are many upsides to business users wanting to touch data and utilize its value, so empowering users the right way is likely to yield real competitive advantages. A data organization “of the people” and “for the people” may take some of the load off of IT. There are certainly creative people with good ideas throughout the company. If business users are interested in analytics, and educated about the opportunities in a safe environment, they may also become IT’s greatest champions when it comes to budgeting.
Matchmaking data suppliers with data demanders
When the CIO says, “Let’s support business users by finding the right balance between analytics opportunity and data security,” then the organization is likely to buy into next steps, building the right teams to make wise investments in time and tools.
This will involve some matchmaking. Pair the people who are creating data demands up with those who have the skills and experience to supply that data. Place them in teams so that they understand each other. Start with rules of engagement. Business users will agree to only use ‘certified data’ in a sanctioned way. IT will help business users obtain the data they need in the way that they need it. The organization will embrace transparency in data use, decisions and assumptions.
A small pilot project will pave the way for future efforts. Let the business users explain just one type of data they would like to use and what the purpose, analytics and delivery might look like. The whole team can then walk through the process hurdles with a focus on accomplishing one small goal. They can jointly seek the right tools and solutions to create an innovative, easily replicated process, using that success to expand into more areas where modern analytics will have an impact.
The end result will be an operation where enterprise analytics stays comfortably ‘in the yard’ under the watchful care of everyone who has a stake in turning new insights into business growth.