Insuranceciooutlook

Lessons Learned in Building and Operationalizing an Enterprise Data & Advanced Analytics Capability

Sukanya Lahiri Soderland (Chief Strategy Officer and SVP, Strategy, Innovation, Analytics and Consulting) and Himanshu Arora, (Chief Data and Analytics Officer) Blue Cross Blue Shield of Massachusetts

Sukanya Lahiri Soderland (Chief Strategy Officer and SVP, Strategy, Innovation, Analytics and Consulting) and Himanshu Arora, (Chief Data and Analytics Officer) Blue Cross Blue Shield of Massachusetts

Insurance is a risky business. Identifying, assessing, and managing risk has long been an analytically driven effort.  Yet with the advent of multi-source data, advanced computing power and new analytic techniques powered by AI, the way the insurance business works is shifting. Insurtech, buoyed by unprecedented levels of venture capital spend, is promising to revolutionize the insurance business model. Imagine the possibilities for designing insurance products curated to the individual powered by the unending storehouses of data captured by the likes of Alexa or Siri! Large incumbents recognize this shift and are creating functions that position data, advanced analytics, and digital transformation to serve as fuel for sustaining competitive advantage.

Yet even when incumbent organizations have committed to creating a cutting-edge data and analytic capability, there are challenges to quickly scale these functions to deliver meaningful business impact. The rationale for why to do it is clear – bolster the income statement by driving impact on the top and bottom-line. And business leaders can quickly rattle off a long wish list of analytic use cases that are worthy of driving actionable insights to implementation. But the speedbumps along the way include the reality of limited budgets, competing priorities, changing workflows in big organizations, matrixed-driven decision making, and byzantine operating models and legacy IT systems.

Blue Cross Blue Shield of Massachusetts (BCBSMA) is on the journey of building scalable cutting-edge data and analytics capabilitiesthat will underpin all our business decision making and enhance outcomes. Our biggest takeaway over the last three years of establishing a new Enterprise Data & Analytics function from the ground up:  the “how”and the “who”matter as much as the “what” (specific use cases) and the “why” (business case for analytics).

Here are some highlights of lessons learned:

What/whyBe clear on the target outcomes at the outset, and measure and track implementation of interventions to ensure impact. Introducing new methods of working can require a “soft sell” to business owners to identify how their thorny problems can be helped withanalytics. For example, our lead Medicare senior executive identified a need to capture greater Medicare market growth as more people become eligible for Medicare. As the market leader, BCBSMA had lots of information on members but we were not using that data to anticipate members’ needs and help simplify  the confusing process of finding the right Medicare plan for them. We developed and implemented a member-facing health plan recommendation tool, backed by algorithms that combine our knowledge of a member’s health with their preferences on cost, benefits and preferred providers. Our goal is to help each member make the best choice for them, enhance member satisfaction, improve our retention across lines of business, and improve health outcomes per unit of cost while driving the highest conversion rate of members from Commercial insurance to Medicare. Through this work, the Medicare leader became a champion for the Enterprise Analytic function which  helped build organizational momentum.

Who: Collaborate with the business owners and enablers to build co-ownership. We have deployed a “hub and spoke” model to establish close connections and cross-pollination of learnings between the data and analytics hub and the business teams. The ‘hub’ functions as the center of excellence in experimenting and adopting the latest methods and technologies. Each of the business spokes is led by an internal business champion and they serve as part of a governance body to drive prioritization of analytic use cases and oversight of implementation to value. Business leaders also pre-commit to implementation to ease “last mile” challenges of getting insights operationalized into workflows.  Enabling functions such as enterprise technology are in-sync from the get-go and talent on teams is a mix of analytics experts as well as business owners who help ensure actionable insights “keep their spot in line” for implementation resources. Finally, we recognize that we can’t do it all ourselves in-house especially given the high demand for top analytics talent. We are deploying creative ways to use vendor partners to address talent needs and retain that skill and knowledge in-house sustainably.

How: Execute for speed, adapt for learnings, scale for the future and manage data as an asset. Our focus has been on executing quick wins to build traction before going for “big swings.” Some of the early focus areas were driven by how quickly a business team could leverage the early insights versus holding back for the most sophisticated analytic outcomes possible. From identifying most common post-visit cost surprises, to developing personas for members in a line of business, some of the early work was focused on building a process, testing the operating model, and quickly helping teams activate the ‘last mile’ to drive operationalization of insights. With that foundation set, we have been tackling more challenging business problems and introducing more advanced data science techniques to pave the way for more complex implementations.

In the interest of producing rapid, analytics-informed actions to drive business outcomes we adopted an Agile Scrum methodology. Agile teams produce outcomes every 2 weeks, working with business teams to test, learn, implement and then re-scope/backlog. Major enablers to the work have been data governance and managing the initial data that feeds the analytics while also carefully collecting and re-piping outcomes data on the back end. Our method has been to move from a one-off “project” orientation to a “product” orientation that favors scalability and reusability.  This is a muscle we are developing, with a focus on driving integration across business areas where analytics developed for one area can be reused and refined for use elsewhere.

"Making technical and complex capabilities such as Data & Analytics relatable and accessible to stakeholders is crucial to inviting collaboration"

We have been purposeful about building last mile implementation considerations upfront to avoid the risk of fancy analytic insights “sitting on the shelf.” To enhance speed to market and value, we have scrutinized our best choices for build/buy/partner and in our case the best path has been leveraging platforms that have in-built accelerators (think AWS, Google Cloud etc.) and building on top of them. A strong partnership with the security and privacy functions has been critical to managing rising cybersecurity risk and in informing vendor protocols. Lastly, we’ve been reminded that people remember stories more than data and facts. Making technical and complex capabilities such as Data & Analytics relatable and accessible to stakeholders is crucial to inviting collaboration. Partnering with internal communications teams has been key to illustrating what data and analytics as a capability can do for members and all other stakeholders at BCBSMA. For example, the stories of the team’s ability to help vulnerable families with proactive outreach during COVID based on a partnership between our analytics function and our care management nurses has helped bring to life the power and potential of advanced analytics to enhance our mission and business impact.

Data and advanced analytics are part of the lifeblood of furthering BCBSMA’s mission by helping predict and prevent acute health events, managing risk, reducing unnecessary friction for consumers, and helping us all lead healthier lives. An effective combination of what/why/who/how of establishing a sustainable and impactful data and analytics capability can go a long way in powering the insurance model of the future and driving transformative impact at scale. 

Weekly Brief

Read Also

Protect or Innovate? Cutting Through the Noise When Evaluating Predictive Models

Protect or Innovate? Cutting Through the Noise When Evaluating Predictive Models

Tom Fletcher, PhD, VP, Data Analytics, North America Life, PartnerRe
Optimizing Innovation Initiatives by Artfully Managing Change

Optimizing Innovation Initiatives by Artfully Managing Change

Lori Pon, Director, Claim Contact Center and Claim Handling Unit at AAA-the Auto Club Group
 Digital Ecosystems and Insurance - A Winning Partnership

Digital Ecosystems and Insurance - A Winning Partnership

Sean Ringsted, Chief Digital Officer, Chubb
Data Governance Systems Undergoing Ongoing Evolution

Data Governance Systems Undergoing Ongoing Evolution

Paul Pries, Director – Data Governance, West Bend Mutual Insurance Company
People as Decision-Makers; Technology  as an Enabler

People as Decision-Makers; Technology as an Enabler

Ralph LaSpina, EVP, Chief Marketing & Underwriting Officer, FCCI Insurance Group