When I was asked to write an article on big insurers and their digital transformations, I had to hit the pause button right from the outset to make the clear, if underappreciated, distinction between digital literacy and data literacy. The terms are often conflated or are used interchangeably, and indeed they are complementary. But they represent very different facets in the journey companies are taking to become digital enterprises. Succinctly put, data is what enables you to succeed as a digital organization.
Here’s how I make the distinction. While "digital" refers to platforms, a web presence, or whether you’ve got digital trading or underwriting platforms, "data" is the fuel that drives it all and enables an enterprise to transcend performance beyond simply being a digital version of its previous, analog incarnation.
Quite often organizations “going digital” invest deeply in creating a new digital web presence, but then forget all about the data—which, in most legacy systems, is locked away and difficult to access. These older systems were built for back-end transactions, not for the customer to interact with up front—and when data is locked away and can’t be reached in a timely way, many companies’ digital transformations fall short of expectation. Often, they even fail. This is the inevitable result of focusing on digital and forgetting about the data, the essential, necessary force driving it. It’s impossible to power a digital transformation without having a data transformation at the same time.
So here I’m going uncouple data literacy from digital literacy. Companies are already well on their way in the digital journey. But I believe they need to spend a lot more time getting up to speed understanding and using their data if they are to see better returns on those expensive digital transformations. And granted, all companies don’t need to invest in this data journey to the same degree—there are many different variants of the data journey.
I’ve observed that the basic building block of any data transformation is a company’s culture. Often data has been ignored in an organization, and before any investment is made, there has to be a cultural, attitudinal shift in how data is valued and treated. Workplace conversations like, We can’t trust our data; it has no value, or We can’t get access to our data indicate that not much is actually known about it. Rather than working from hunches or rumors, it’s necessary to understand objectively where the starting place is. From there, you can ascertain where you’re heading, plan a transformation and build a data strategy.
In the legacy systems of many big insurers, simply figuring out where the data lies and how to extract it is a primary challenge. Carriers have to this point focused their not inconsiderable digital efforts on their back-of-house operations; now they need to move toward leveraging real-time, dynamic data as it relates to the customer-facing front of house, and in particular, to analysis of data that can help them solve market problems.
It’s possible to accomplish a huge amount of this without a massive investment, by simply evolving to a data culture—making people value data, understand it, see what it can be used for, and why some data sets are different from others even though they may look the same, for example. A company could waste tons of money trying to deliver data strategy if they haven’t aligned the hearts and minds of people across the organization to its value; and this shift must start with responsible top leadership who see the importance in leveraging data.
Here are ten steps organizations need to take to ensure they are data literate and can use data science to improve the way they do business:
- Understand the full data chain value. Be clear about what is meant by data literacy. It not just machine learning—it is understanding exactly what data you want to measure to what value.
- Do a Data Maturity Assessment. This measures how advanced is a company's use of data analysis. Determine where you are, define the areas of data that you want to focus on, and where you want it to take you.
- Appoint a data lead. Make someone responsible for raising data literacy. It won’t happen by itself: evangelism and leadership are needed.
- Appoint a senior stakeholder sponsor. Getting buy-in for organization-wide data literacy will require visible top-down leadership.
- Be prepared for a culture change. Data literacy will require a culture change with data at the top of the agenda. This will require new thinking.
- Reach everyone. Engage broadly and deeply with the whole business, from top to bottom and at full width. Data literacy touches everyone and must be organization-wide.
- Promote and engage. Find creative ways to promote data literacy and education; computer-based training (CBT) will not deliver the outcomes required.
- It never stops. Data literacy is not a one-shot exercise; it will require an ongoing program of work and practice.
- Include the Senior Management Team. Senior stakeholders need to be included in the program as they are the decision makers and need, perhaps more than anyone else, to understand the new world of data.
- Run your data maturity assessment again. Always assess how mature your data is—see if you have moved the needle. Initially, there may be negative movement, as improvement in data literacy may have enabled people to be more critically aware.
Once an organization is data literate, it’s time to focus on extracting it, putting it in a form in which it can be analyzed, and determining what use to put it to: What is the right data to use, and what are you trying to determine from it? For data to reveal anything, frequently, it’s a matter of engineering it, much like a sound engineer who softens the “noise”—extraneous matter—and amplifies the signal on other parts that could reveal something of value. Similarly, in legacy systems, it’s not only about figuring out how to extract the data; it’s equally important to determine what is the most important signal. For example, many insurers have a call center that receives hundreds of calls a day. Most of these calls are simple customer service requests, such as changing an address or adding something to a policy. Some, however, are complaints—and that is the data signal we want to amplify. Insurers want to manage those complaints so that customers won’t leave or become ill-disposed to buying another product from the company. In analyzing data on voice and text from the call center, there’s a huge amount of customer service “noise” to get through before you can pick out the signal of the complaints and leverage the value of that data.
Achieving data literacy—accepting it fully, finding the right data to use, then asking the right questions of it—will allow companies to develop the skills to use data science in world-changing ways. It will help us understand everything from how markets will respond to pandemics like Covid, to how investment decisions will impact climate change, to how customers behave and their needs for a secure financial future. But these changes need to start with an adaptive, data literate culture.
