Customer Centricity, knowing your customer, customer intimacy and proactive customer management are strategies that continue to carry the day across all industries. The Financial Services Industry is undergoing a dramatic transformation due to changes in customer service expectations and the digitization of services in general.

The need to digitize the customer experience has meant that traditional companies like ours have the need to integrate disparate distribution channels, capture the entire customer life cycle, increase customer loyalty through differentiated experiences and in general meet customer expectations that have been set by other traditional retail customer focused organizations. A lack of focus on information management as a discipline and a cultural focus on products has traditionally made it difficult to achieve these objectives.

"The data itself once understood and provisioned for one purpose must not be lost, as it could be used for other, as yet unidentified purposes"

Financial Services and specifically Insurance organizations are even more challenged due to the years and years of evolution of their physical data and application environments.

Over the course of decades, these environments moved and morphed through periods of different technologies, business strategies, and acquisitions. The current data environment can be one of the bigger obstacles to executing customer centric strategies and in many cases it is a problem that is not fully understood nor appreciated. What is needed is an Information Management Strategy that defines an organization’s approach to understand and manage its data as an asset. As with all new things, we sometimes lose sight of the really critical items when trying to enable new and emerging technologies and practices, and our journey in maturing the Information Management practice at John Hancock is no exception.

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More than three years ago our company went through a reorganization with the objective of increasing the degree of scale utilization across its various businesses. This reorganization drove a number of changes, not the least of which was the creation of a divisional responsibility for operations across all of our businesses. The intent of this change was to ensure that the Company as a whole was taking advantage of opportunities for leveraging like functions, while ensuring that core/ unique business functions remained closely aligned with the business priorities of the unit.

A somewhat less obvious drive for consistency emerged on the approach to Information Management. The organization as whole had defined goals around customer centricity and quickly realized that in order to achieve those goals a more consistent approach to understanding, managing and utilizing customer data was needed. In looking at how to meet this need and of course discussing with multiple external experts and advisors, it was clear that there was no formalized Information Management Strategy (IMS).

The initial reaction to this finding was somewhat mixed. Nevertheless, the Customer Centricity objective continued to drive the company forward and the adoption of Master Data Management technology to enable customer data aggregation generated enough questions around ownership, governance and architecture that the IMS skeptics were defeated and an Information Management Strategy was developed.

The strategy comprised many components (architecture, governances, quality, business intelligence, operating model), and only pieces of it were fully understood. Each area had resident experts and when information was needed those experts were asked to provide answers. As we looked to aggregate data across business areas, this legacy of informality created difficulty as there was little consistency in approach and even definition. As the initial MDM initiatives began to take hold, architecture, quality and governance were quickly embraced as reasonable and necessary, but more to support the data aggregation initiative, as opposed to any overall broad organizational need.

The initial activity which began to open organizational eyes to the need for formalized information management came from a risk initiative known as Critical Data Identification (CDI). It stemmed from the need to demonstrate, both internally and externally, that the critical data of the organization was being protected and controlled. While this seemed easy enough, when put in motion a key issue emerged in that there was no formal definition or inventory of what the critical data was. Surely it was somewhat intuitive, but there were many “versions” and individual opinions. What was needed was the organizational opinion. That took some work, not only to define, but then to identify, evaluate and document what the critical data was and what were the controls in place to protect it. Once definition was complete, the ongoing management of critical data needed to be operationalized so that the critical data question could be consistently responded to on an on-going basis. This operationalization will take some time to fully mature but that process has begun to be put in place

Probably even more impactful than CDI in driving the awareness and acceptance of Information management was the emergence and momentum being generated within the business areas for data analytic capability. Initially this activity ran its normal, ad-hoc, iterative, question/response course between IS data experts and inquisitive business executives and analysts. However, as it became clear that this analytics thing was kind of a new ballgame, new players and skills were added to the mix.

Very quickly these new players become frustrated. Getting the data was not so easy. Data was in a lot of places and no one was totally clear on what data was where and what it really meant from a business, end-state perspective. Yes, the experts knew how it was used in the running of the business but different data in different places could be called the same thing but be… well… different! Once again there were many individual opinions or understandings, but no organizational understanding of the data and thus locating and understanding data became the first difficult hurdle.

While data understanding was now viewed as critical, that understanding needed to be operationalized so that it could continue to live to support the next set of needs. In addition, the data itself once understood and provisioned for one purpose must not be lost, as it could be used for other, as yet unidentified purposes.

Through the evolution and understanding of this organizational pain came the organizational clarity of the WHY of information management. If we believe that our data has value, that it IS an asset, then in order to maximize the value of that asset, we need to have a formalized approach to understanding that data and the definition of how that asset will be managed.

Without this formalization, we cannot effectivity take advantage of the opportunities that are presented by the increasing capabilities in data analytics. We cannot leverage our data assets until we have a good understanding of our data and can provide it to the data analysts efficiently. In its simplest of terms, this is the goal of all of the activity associated with the execution of our Information Management Strategy.

Our journey into Information Management is like a lot of organizational change efforts. Just getting everyone to agree there is a problem is the most critical step in generating a solution for the organization. It took us awhile to get where we are and we still have quite a ways to go, but we have come a long way from our initial debates. We needed to find the specific issues and experience the pain before we saw the need for an organizational approach to managing our data. We were not able begin to solve the problem until we were collectively sure that we had one!