Data analytics has the potential to redefine how organizations are structured, breaking down long-standing barriers between departments like Information Technology (IT) and traditional insurance functions. But creating the kind of organization-wide change made possible through big data is easier said than done.
A critical component of bringing about successful and lasting change is for frontline employees in departments such as claims and underwriting to develop a data-driven mindset and for data scientists to better understand the inner workings of the insurance business.
This data-driven approach to operations must flow seamlessly from data scientists to every level of the organization, from the C-suite down. That seamless approach starts with building the right team to oversee the development and adoption of data analytics and predictive modeling processes throughout the company.
It’s no easy feat. In fact, 37 percent of companies say finding talent to run big data and analytics on an ongoing basis is a main challenge to implementing big data techniques for them, according to a survey from Accenture. Another 35 percent said integrating big data into existing systems is a primary struggle. The team tasked with tackling these challenges must overcome a lot of organizational resistance and be exceedingly effective at collaboration.
Different organizations build these teams in distinct ways. Early data science efforts occurred in silos at most companies, with a few core data analysts sifting through information and getting the preliminary analytical tools ready for more widespread use. Other organizations place data scientists within individual departments. Both of these structures are efficient in certain aspects, especially at the start of a company’s data science development.
But as analytical efforts mature at an organization, it becomes increasingly important to build cross-functional teams in which data science is the primary focus and tool. These teams, or steering committees, can help guide an organization’s overall data analytics efforts to prioritize the most significant projects and maximize the value of those efforts.
Assembling your team
Individual team members should have a firm grasp of how data will be used to achieve organizational goals. They must also understand fundamental insurance concepts as well as specific considerations for your company’s culture and priorities. When putting together your data team, make sure the following roles and areas are represented:
• Data scientist: Obviously, you’ll want data analytics experts on your team. Keep in mind that within data science is a wide variety of skill sets and expertise. Data architects, data wranglers, statisticians, business analysts and data visualization specialists have a broad range of core competencies and skills gaps. Ensure that your data reps cover areas such as data collection, analysis and modeling as well as efficient data storage and security and privacy issues.
• Actuary: As big data redefines how your organization analyzes and addresses risk, it’s important to have an actuary involved to help facilitate these transitions. Actuaries use their knowledge of insurance data to work with data scientists to update the models and assessment tools.
• Department leaders: Leaders within individual departments should be kept informed and offer input on areas where data has the greatest potential to elevate efficiency and service. Claims, underwriting, marketing and customer service should all be represented and should increase their contributions when the team is focused on their respective department.
• Operations: These data-driven changes will result in significant operational changes. Having someone to manage that process and make the transitions as smooth as possible is a must.
• Information Technology: Virtually all of these new systems and processes will require some IT resources to develop, implement and maintain.
• Designer: A growing number of organizations are adding a team member who is responsible for making the analytics and new processes more understandable and usable.
In addition to these integral data-team members, others should be kept aware of key developments, including agents, brokers, and your company’s legal and compliance groups.
Most importantly, the data team needs to be empowered to lead the organizational change required to make these new data-driven processes work. That means having an adequate budget and discretion to prioritize projects, as well as holding the team accountable for those tough decisions in clear financial terms.
Cross-departmental teams can struggle to collaborate effectively if individuals or departments are focused more on jockeying for position or resisting change than on performing good work. It’s crucial to set a clear hierarchy and expectations from the outset. Some team members should be focused on asking the right questions and identifying the right problems, while others should concentrate on developing solutions. McKinsey suggests seeking team members who are naturally curious, detail oriented, open to diverse opinions, willing to work together and focused on practical outcomes.
Unless the data team works well together and gets the rest of the company on board with new ways of working, all of these new opportunities will fail to have any real impact. This situation arises most often when actuaries and department heads believe data scientists are threatening their responsibilities or job security. Getting senior leadership involved with the team to keep work centered on collaboration can often go a long way toward heading off many of these concerns.
By Michael Elliott, from: http://www.information-management.com/blogs/big-data-analytics/assembling-a-top-notch-analytics-team-10030443-1.html?utm_campaign=analytics-dec%208%202016&utm_medium=email&utm_source=newsletter&ET=informationmgmt:e8320571:2047253a:&st=email&eid=8e5f5423e859a64488540fc441962c81