Insurers and financial services organizations are constantly looking at how best to put predictive and prescriptive analytics to use inside of their organizations. In this day and age of so many different flavors and varieties of analytical platforms, it can be challenging for buyers inside of financial services and insurance organizations to fully understand the full value that a software platform delivers. We have found that framing the product into two main categories – Operational Excellence and Analytical Sophistication – can create some clarity around this challenge. In this blog series, I will share our perspectives on what the focus should be when selecting an analytical software solution and where the industry is heading in terms of the latest technologies and trends. Subsequent blogs will delve further into the inner workings of the Operational Excellence and Analytical Sophistication perspectives.
It’s important for organizations to combine Operational Excellence and Analytical Sophistication into a single platform for one simple reason – it makes companies more efficient and effective. The last thing that any organization wants is a product that is analytically adept, but does not allow for consumption of those analytical insights. And on the contrary, if I can easily consume outputs from a software solution, but those outputs or end insights are incorrect analytically, they do me as an organization no good. Thus we find it extremely important to combine and balance the latest technologies with the richest set of features available in the marketplace. And increasingly, ease of use and simple navigation is rising to the top as a key differentiator and selection criteria for many organizations.
For many software development organizations, this is easy to articulate but difficult to execute upon. It requires being acutely aware of what customers and prospects need from a software platform, how they will evolve as organizations, and what alternatives they have in the marketplace. Listening to customers, analysts, and other industry subject matter experts in order to improve software platforms is important as well. Taking these “voice of customer” requirements into account allows for a better end product to be developed, and can be accomplished through frequent customer interviews, discussions, and product advisory councils, among other methods. The feedback from these interactions should translate directly into product roadmaps that satisfy as many requirements as possible from both the Operational Excellence and Analytical Sophistication camps. For instance, to ensure we are in line with the market, we have a Professional Services Consultant turned Product Manager on our product team that understands intimately how our customers consume our software. We also have a user interface (UI) designer that conducts customer interviews and uses that feedback to influence product requirements and design going forward. Both these individuals stand for the voice of the consumer; something that you don’t always see at software organizations.
We will detail out the two topics of Operational Excellence and Analytical Sophistication in coming posts, but let me briefly explain what comprises each category.
Operational Excellence looks at how the solution serves the entire decision making process in the organization, from data input all the way to how each customer executes these decisions and incorporates them into the front line. For complex decisions, this is a process that usually involves multiple steps with users from different disciplines, departments and sometimes geographies and often requires connection to multiple systems and data sources. It also needs to combine things like scale, user concurrency, connectivity, and system reliability into a solution so that it can meet the needs of insurance and financial services organizations decision making. This includes enabling the latest technologies such as Apache and H20 to increase threading, throughput, and scale.
Analytical Sophistication looks at continuing to move solutions up the analytical maturity model scale from basic descriptive and diagnostic analytics to fully developed and advanced predictive and prescriptive analytic techniques. Focusing on predictive analytic techniques in a continuous fashion enables insurers and financial services providers to go beyond basic regression modelling into more advanced techniques using decision trees and neural networks. By adding the ability to integrate machine learning libraries, we are allowing organizations to improve upon their current analytical algorithms and process automation capabilities. Providing the ability to script GUI actions allow the analytically sophisticated user to automate processes and thus spend more time working on important strategic activities like new analytical programs instead of the tactical ones, such as executing jobs over and over.
In the next two posts, I will describe in more detail the components of the Operational Excellence and Analytical Sophistication perspectives. By sharing these insights I hope to empower our consumers with the product knowledge needed to make informed analytical software strategic, design and purchasing decisions.