Webinar: Proven Approaches to Hive Query Tuning

Webinar: Proven Approaches to Hive Query Tuning

The Continual Quest to Refine the Customer Experience

Most companies will happily talk about how they are undergoing a “Digital Transformation.” Ask executives what it means to their organization and often you will get a well-curated word salad. That is because digital transformation in banking is a nebulous term until it can be measured. The digital part is rather easy. It’s the transformative aspect that is difficult to quantify.

What drives digital transformation is the need to continually refine user experiences across all classes of customers. Whether it be a retail banking customer, a small- or medium-sized commercial entity, a trading counterparty, or a public corporation receiving treasury and finance services, they all expect a highly effective and customized response to every transaction and query that is better than the one before.

For some companies, this is an awesome opportunity to differentiate themselves and grab more market share, because clients can easily move from one provider to another. To help readers along that journey, this post emphasizes the concept of measurement as one of the most practical and actionable aspects of digital transformation. The customer experience can’t be refined if the underlying services are not in a state of continual optimization, and you can’t optimize what can’t be measured.

A Fundamental Rethinking of Automation

We, as a society, have already moved from analog to digital. But from a transformational perspective, for example, running documents through a fax machine is not much different from scanning and emailing them. This is because you are still distributing the same documents, albeit more efficiently.

One would think that Robotic Processing Automation (RPA) would be an excellent way to thrust you into the next realm of digital transformation. But this is not the case if the same types of documents are sent for the same reasons, even if done at a much faster and larger scale. In that case, you can proudly add operational efficiency to your LinkedIn profile along with a list of cost saving and improved resource allocation benefits. That would be well deserved because it is a challenge in itself, but it is not a digital transformation.

So what is digital transformation? In simple terms, it’s the embedding of digital technology into every aspect of business operations that results in changes (transformations) of thinking, models, and behavior.

This is not just about applying technology to business because companies have been doing that for decades. Digital transformation is a fundamental rethinking of how a company utilizes advanced technology such as artificial intelligence, machine learning, and big data to build automated processes that evolve. It is no longer people-driven tasks that are automated but an automated process that is overseen by people in which each decision and action is minutely determined through empirical evidence and analytics of market-driven events.

When properly executed, new business models are initiated that apply insights derived from the real-time interactions with a larger and broader population of customers across a growing number of scenarios.

An Old Business with New Legs

A contemporary example of digital transformation is car insurance claims processing. Traditionally, it has been a paper-intensive operation that followed a very linear process of checks and approvals, one document at a time. However, for a few innovative companies, it has transformed into a low touch fully automated end-to-end process. The “paperwork” is not only more efficient, but how claims are processed has been fundamentally transformed from a cost-containment function to a cross-sell and up-sell opportunity. The claims process is now a convenient self-service-styled experience that can also suggest additional, customized services that are perfectly relevant to that customer’s emotional state at that point of time and location.

The industry transformed, digitally, in order to conform to customer expectations, and that example is endemic of what is happening across all segments of the financial services industry.

Digital Transformation Occurs Over Time

Early generations of auto insurance apps allowed the customer to take a picture of an accident which was then manually viewed by someone with years of experience. That individual’s experience from reviewing thousands of accidents in-person allowed them to assess damage simply from viewing a picture to come up with a cost estimate.

In that first iteration of change, the process was enhanced by technology, not transformed. But that first step of uploading digital images to a database laid the foundation for transformation. Over the span of years, that same company ends up collecting a massive amount of digitized automobile accident data. By storing all of that information in big data clusters, they then had the opportunity to build and train complex AI models.

Running a collection of those models together thus enabled a full end-to-end claims process to be automated, generating and confirming work estimates with the body shop of your choice, wiring money to appropriate accounts, and managing car rentals. What was a long high-touch manual process transformed into a low-touch experience with real-time action and response. That convenience engenders trust from the customer and a better chance to capture more revenue opportunities from them.

But this was not an overnight digital transformation in banking. It was many years in the making and required a deep rethinking of how business is done with respective applications of technology. Nor is it a one-and-done project. As more companies do the same, the process requires continuous optimization and refinement to stay relevant.

Continuous and End-to-End Optimization

While the example above makes it look easy from a customer point of view, fully automated and real-time processes are complex endeavors that leverage the subject matter expertise of a number of intra-company domains. That means you have a number of groups working on the same product, each employing a number of technologies and data workloads at the same time. One bottleneck at any time in a string of processes can ruin the entire experience for thousands of customers at once. Measuring how things are going across highly complex interdependent workloads and optimizing each and every interaction across a variety of environments on a continual real-time basis is critical to digital transformation.

Everything must be considered, from machine learning app performance, network latency, and 3rd party payment APIs, to the management of massive amounts of unstructured image data and a host of other types of workloads. It requires a specialized set of software tools that can peer into these components, generate metrics, and recommend the best route to optimization in real time.

The Ultimate Measure of Success

While there are many variables to client satisfaction, performance and response time are huge components in a client’s perception of user experience. It’s a constant battle to address and manage customer expectations with the types of complex systems as described above. This becomes even more challenging as organizations move toward multi-cloud environments. The toolsets used to manage these ecosystems fall into the Application Performance Monitoring (APM) category, an example of which is Pepperdata.

However, unlike traditional performance monitoring solutions that merely summarize static data and require manual, time-consuming application-by-application tuning, Pepperdata provides correlated visibility into your infrastructure and applications across your big data analytics stack.

Pepperdata is a critical component to a successful and continual digital transformation in banking because it automatically scales system resources while providing a detailed and correlated understanding of each application using hundreds of real-time application and infrastructure metrics in the cloud and the data center. Financial services organizations ranging from FinTechs to global institutions like the Royal Bank of Canada depend on Pepperdata to ensure that they can measure and optimize in a comprehensive and automated fashion. This ensures that they get the business insights they need from their big data environments.

Although the narrative throughout this post could be considered a topic that is too much in the technical weeds, in effect, each and every customer demands that close attention is paid to it and that their experiences are continually refined. Making them happy is the ultimate measure of success.

Get more information on Pepperdata in financial services.