Customer Experience & Digital Transformation
Brian Timmeny, Chief Technology Officer, Zions Bancorporation
Brian Timmeny, Chief Technology Officer, Zions Bancorporation
Through the use of modern technology and data architecture solutions, we are able to establish a radically improved understanding of our customers. This allows us to expand our services to meet their evolving needs constantly. Modern analytics, machine learning, and artificial intelligence give us an opportunity to consolidate our customer data, improve insights, and turn those insights into actions that can serve the best interest of our customers and clients.
While the theory and value proposition of machine learning and rapid insights are clear, the journey to get there is not as easily mapped. The manner by which investments are allocated to a digital and data transformation is paramount to the success over the long term. If a connected ecosystem is established across a complex network, this is a strong start, but if not backed by the insights of our data engines, little value will be realized until very late in the journey. On the other hand, if time is spent improving our learning models, but little allocation is made to the connection between those insights and the live customer experience; the value would again be lost.
Connecting our data ecosystems. Building an early data fabric that connects our most complex systems and data from across the organization is an important first step in the journey toward a connected customer experience. This fabric will allow for our system, our legacy applications and our next-generation solutions alike to connect in a common manner. This will allow for data to be shared across the enterprise and allow flexibility for each system to join this common data engine integration over time. This avoids the need for a “big bang” approach, one that is rarely successful and often intensifies the risk to the overall journey. The data fabric, in turn, feeds our common data stores, and enterprise data transformation patterns (to a common data model) allow us to consistently take advantage of this information and insights across the enterprise.
Establish early data capabilities. At the time when the data fabric build-out remains early in its evolution, we must begin the work related to evolving our common data stores. At the same time, we must also begin evolving our early machine learning and modeling capabilities. This allows the organization to establish the foundational hypothesis surrounding our customers, understand the customer profile views in this light, and continuously evolve these models for better and improved insights over time. As this data is then connected back to the data fabric and exposed to all customer channels, we can now begin to move toward a common experience that can be served through multiple channels, all serving a common experience in the best interest of our customers.
With both the elements of the data engine underway and the data fabric that now connects all of our ecosystem assets, we must pause and reflect as we move toward our next step in the journey. We must step back and remember that knowing the customer, knowing what is important to them in their short-term and long-term journey with us is paramount to ensuring we can serve them well over time. This journey does not begin with a new digital experience, our data fabric, or our machine learning insights but rather builds upon the relationship already in place with our customers. To this end, our next area of focus now moves toward populating our data stores in the correct manner. This means moving information from our most important assets into a common data warehouse, where this information can be linked with data from across the enterprise. This is the engine that will drive our critical insights.
Establish early insights. As data sources are connected over time, and the data is both gathered and rationalized more consistently, and this establishes the foundation on which commonly housed data can begin to garner important customer insights, and our data models, with the assistance of our data analysts and scientists, can begin to evolve meaningfully. It is here where our first customer profile insights are born. This does not signify that we never knew our customers, only that once we begin to harness the value of our modern data technology suites, we can understand them in a far more profound manner. At this moment, we begin to see our customers through their needs and behaviors, and our first insight-driven profiles are born. These profiles will allow us to create a stronger and more lasting connection with our customers, offer more appropriate products and services, meet our customers where they are and surpass the constantly growing expectations that they have of those of us in their service.
Gather our data into a common data suite. For many large institutions, gathering disparate data from across our critical systems is often far easier said than done. This data sometimes exists in siloed solutions, some that have been in that state for decades. The common data fabric, now evolving into a more mature state in our journey, can now become an important consumer of these data suites, consuming this information from across the enterprise and placing that data into a single common location. Because of the complexity of gathering this data from stores that have been disconnected, many organizations must focus upon incremental connections to the data fabric, beginning with those most critical applications driving the current customer experience. This prioritization is critical to the journey and focuses and resources of the transformation on the most important early assets. Limiting the methods by which this data, though somewhat counterintuitive, is important to ensure simplicity in connecting to the data fabric. It will be important to maintain a focus upon limited methods for sharing data. The most widely adopted will be those of APIs and queues. These are not exhaustive as data sharing options but are two options that give the most robust manner to share data in near real-time (APIs) and hyper-real time (queues) for value offered in our most important services.
We must step back and remember that knowing the customer, knowing what is important to them in their short-term and long-term journey with us is paramount to ensuring we can serve them well over time
Connect our insights back to our customer channels. In understanding our customers and utilizing these profiles as the basis of our interaction, it allows us to now ensure that each customer experience is informed by this profile and our growing insights. This does not signify that we do not leave space to continue to evolve this profile but rather ensures that we focus upon those areas most important to our customers in every interaction. In this final stage, we now build a common experience framework back through the data fabric. This completes the circle, and with the insights we have gathered throughout our continuous journey with our customers, we now inform this experience back to the customer through our digital and human channels. This means that should a customer call or choose an online interaction, we inform and serve their needs in the same manner through customized experiences and conversations, all served through a common data fabric in line with the profile and our understanding of the customers’ needs and expectations.
As we now look across the suite of experiences and outcomes our customers expect, we now must ensure that we commonly serve them through these informed insights. Our newly established data fabric is now connected to each channel and how our customers enter into conversations with us. Should our customers connect with us online, through a mobile experience, at an ATM, or in a more personal manner through our bankers or tellers, the experience through each of these channels should be consistent and informed by common insights and our understanding of the customers’ expectations and needs. In this way, we can consider our customer’s experience as a single discussion over time that may involve multiple channels but always informed by a common profile and common objectives that always serve our customer’s best interest.
