Good communication is always a two-way street. Insurers that employ data scientists or partner with data science consulting firms often look at those experts much like one-way suppliers. Data science supplies the analytics. The business consumes the analytics.
But as data science grows within the organization, most insurers find that the relationship is less about one-sided data storytelling and more about the synergies that occur in data science/business conversations. We don’t think that it is overselling data science to say that these conversations and relationships can have a monumental impact on the organization’s business direction. So, forward-thinking insurers will want to take some initiative in supporting both data scientists and business data users as they work to translate their efforts and needs to each other.
In my last two blogs, we walked through why effective data science storytelling matters and we looked at how data scientists can improve data science storytelling in ways that will have a meaningful impact.
In this last blog of the series, we want to look more closely at the organization’s role in providing the personnel, tools and environment that will foster those conversations.
Hiring, supporting and partnering
Organizations should begin by attempting to hire and retain talented data scientists who are also strong in communication. They should be able to talk to their audience at different levels – very elementary for “newbies” and highly theoretical if their customers are other data scientists. This makes hiring a strong communicator crucial! Hiring a data scientist who only has a head for the math or coding will not fulfill the business need for meaningful translation.
Even data scientists who are proven communicators could benefit from access to in-house designers and copywriters for presentation material. Depending upon the size of the insurer, a small data communication support staff could be built to include both a member of in-house marketing, a developer who understands reports and dashboards, and the data scientist(s). Just creating this production support team, however, may not be enough. The team members must work together to gain their own understanding. Designers, for example, will need to work closely with the analyst to get the story right for presentation materials. This kind of scenario works well if an organization is mass-producing models of a similar type. Smooth development and effective data translation will happen with experience. The goal is to keep data scientists doing what they do best, using less time on tasks that are outside of their domain, and giving data’s story its best possibility for impact.
Many insurers aren’t large enough yet to employ or attract data scientists. In this case, partnering with Majesco Data Services is an excellent approach that will meet both the needs of data analytics and the desire for effective business communication. A data science partner provides more than just added support. They supply experience in marketing and risk modeling, experience in the details of analytic communications and a broad understanding of how many areas of the organization can be positively impacted.
Investing in data visualization tools
Organizations will need to support their data scientists not only with advanced statistical tools but also with visualization tools. There are already many data mining tools on the market, but many of these are designed with outputs that serve a theoretical perspective, not necessarily a business perspective. For these, you’ll want to employ tools such as Tableau, Qlikview, and YellowFin, excellent data visualization tools that are key to business intelligence but not central to advanced analytics. These tools are especially effective at showing how models can be utilized to improve the business using overlaid KPIs and statistical metrics. They can slice and dice the analytical populations of interest almost instantaneously.
When it comes to data science storytelling, one tool normally won’t tell the whole story. It will require a variety of tools, depending upon the various ideas that the data scientist is trying to convey. To implement the data and model algorithms into a system the insurer already uses, there may be a number of additional tools required. These normally aren’t major investments.
In the near future, I think data mining/advanced analytics tools will morph to contain more superior data visualization tools than are currently available. Insurers shouldn’t wait, however, to test and use the tools that are available today. Experience today will improve tomorrow’s business outcomes.
Constructing the best environment
Telling data’s story effectively may be best if the organization can foster a team management approach to data science. This kind of strategic team (different than the production team) would manage the traffic of upcoming and current data projects. It could include a data liaison from each department, a project manager assigned by IT to handle project flow and a business executive whose role is to make sure that priority focus remains on areas of high business impact. Some of these ideas, and others are dealt with in John Johansen’s recent blog series, Where’s the Real Home for Analytics?.
In order to quickly reap the rewards of the data team’s knowledge, a feedback vehicle should be in place. A communication loop will allow the business to comment on what is helpful in communication, what is not helpful, which areas are ripe for current focus and which products, services and processes could use or provide data streams in the future. With the digital realm in a consistent state of fresh ideas and upheaval, an energetic data science team will have the opportunity to grow together, get more creative, and brainstorm more effectively on how to connect analytics to business strategies.
Equally important in these relationships is building adequate levels of trust. When the business not only understands the stories data scientists have translated for them, but also trusts the sources and the scientists themselves, a vital shift has occurred. The value loop is complete and the organization should become highly competitive.
Above all, in discussing the needs and hurdles, don’t lose the excitement of what is transpiring. Insurer’s thirst for data science and data’s increased availability is a positive thing. It means that complex decisions are being made with greater clarity and better opportunities for success. As business users see results that are tied to the stories supplied by data science, its value will continue to grow. It will become a fixed pillar of organizational support.