Customer Experience Management (CEM)

Beyond CRM: The promise of Cognition Management Systems

The following is a contributed article by Scott David and Kamille Nixon. David leads user experience strategy and design at the World Economic Forum. Nixon is a member of the Neo4j graph database community and a thought leader on the positive impact of good technology design on business goals.


As cognitive computing gathers ground, heads of technology will increasingly need to build ‘Cognition Management Systems’ rather than Customer Relationship Management systems. We are moving into a deeply exciting age of ‘Intelligent Applications’. So how to think about the new infrastructures that power them? What technologies are available? And how to resource for them?


What is an Intelligent Application?

There are a few key changes in the air that make the present different to previously discussed smart systems, rapid insight into customers’ behavior, or the Semantic Web. The main difference is that we are starting to model thinking and making services that perform grunt-work thinking for us - and that impacts how we imagine and build the products of the future. In turn, there are six key opportunities playing out.

  1. The rise of Graph database technologies that model and query knowledge in a way that’s closer to how we think
  2. Breakthroughs in engineering and the delivery of Artificial Intelligence, with Cognition-as-a-Service available as consumer products
  3. Performance-focused next-generation infrastructures for Business, Graph, and real-time Transactional Analytics
  4. Insight-to-action feedback, with Human-in-the-Loop, to enable judgment as a process step, and for product and algorithm improvement
  5. Rich customer and User Experiences that deliver context and personalization via intuitive interfaces
  6. A Multi-Model approach to product and systems design, bringing together the above opportunities into a hybrid infrastructure

An Intelligent Application is built upon these innovations, and the changes of mindset that they bring. It represents a new way of thinking, by offering technologies allowing us to design systems that model how an organization thinks, not just the data of the product, service or customer relationship.


Modeling the speed of thought

There has been a lot of talk about big data, and the holy grail of harnessing the 4 Vs:  Volume, Variety, Velocity and Veracity. In parallel there have been somewhat philosophical discussions of when big data 'is' big data, and big for whom. What’s important is not whether data is big, or streaming in real time. Instead, it is whether it is usable at the speed of your organization’s thought.

Capturing fraud within financial transactions requires real-time discovery of patterns in the data, with swift human-in-the-loop interception when a red flag requires a judgment call. At the other end of the scale, global trends analysis is often annual, as a gift to muse about over the Christmas period in the lead-up to a new year.

But as the world speeds up, this kind of ‘manual’ analysis will move into Intelligent Applications that provide insight to leaders at the speed of boardroom thought - perhaps for quarterly reporting or key strategy meetings. The insights will be delivered via next generation infrastructures, and the humans-in-the-loop are the analyst contributors and the members of the board.


The art of multi-model ricochet design

Many organizations have, on the surface, very similar problems. So a standard way to solve technology requirements has frequently been to resort to enterprise products. A features checklist manages expectations, and a team of consultants build something pragmatic.

As the pressure for competitive advantage rises, so will the need to build more bespoke systems to increase insight or efficiency not possible before, particularly for smaller or niche companies. But build what? This tension is visible at high-level tech and big data conferences. It’s usual to see a flurry of camera phones come out every time a diagram is shown. Heads of IT seek models for how to imagine this new opportunity, to then share and discuss with the heads of other departments.

Amongst the first necessary steps are lean development, cloud services with reduced overheads for DevOps, Machine Learning micro-services, and learning by doing and prototyping. A credit card and a few willing accomplices will go a long way to start. Once an Intelligent Application shows sufficient promise to influence change within an organization, it can then receive wider feedback, and input of ideas, before getting hardened into a secure and reliable future infrastructure.

Add to that some personal thinking tools. Become a ‘Design Thinker’. Model and graph the way your organization thinks and classifies its knowledge: what taxonomy of entities and data relationships defines the flow of ideas, analysis, and production, and how does senior decision-making get represented?

Then combine micro-services into an eco-system as a multi-model architecture. Companies like IBM are investing US$1 billion into their Watson division, and have an incredible range of cloud services available, which help to circumvent the talent shortage and need for complex DevOps. There are many other suppliers with similar kinds of infrastructure offerings. The art is in defining which pre-created service, connected up to which other pre-created service, will deliver the desired results.

This is not only the design of a technology pipeline. It’s about picking, choosing, and joining together services from multiple vendors. And it’s about building a ricochet of value between services in your multi-model architecture. Imagine real-time streaming data using Spark, performing analysis to look for patterns in the data - identified as relationships in your organizational Knowledge Graph; then performing Machine Learning entity extraction that powers a recommendations engine; and the behavioral results are fed back and stored in the graph. This is augmentation between services. And the concept of having a 360-degree view of your customers evolves dramatically when you go bespoke in this way.


Rising to the Uber challenge

Most organizations don’t have the luxury of a huge IT department, or the talent to innovate the next Airbnb or Uber, regardless of how often company Directors wheel out this ideal. However, there are a few underlying principles at play.

The comparison is not only about rapid, disruptive innovation. The aim is for intuitive interfaces, delivering context and personalization via a rich customer and User Experience. It has to be perfectly simplified. The tasks being performed via the interface need to be streamlined. The focus of attention should not be distracted by irrelevant features or uncertainty over where to click next. Real-time features bring engaging, time-sensitive, or necessary contextual information, such as a cab approaching. And the mental model that users build up whilst interacting should feel natural to the way they think without realizing it. All of that takes time, and an attention to psychological detail.

That part of the Uber challenge is just about the ‘front end’ of the application, the tip of the customer-focused iceberg. And if you’re not thinking that way on the front end, chances are that you’re not thinking that way on the back end either. Bad data models, poor infrastructure inter-operability, and terrible interfaces wreck productivity and analytical clarity. Modeling how a user thinks on the front end is every bit as important as modeling how your organization thinks via the back end, and how your systems do some thinking for you. The parity and complementary relationship between the two is itself a competitive advantage, and an ethic that drives an organization. Intelligent Applications think like both the consumers and the underlying organization, together.


Cognition Management Systems

So the biggest opportunity for the creation of Intelligent Applications is to imagine that you’re building a Cognition Management System that works across the entire organization, from front end to backend. For some time, processes have been modeled to enable efficiencies, and analysis can be modeled too. Think of each service as an outsourcing of grunt-work thinking, so that a team is liberated both by efficiencies and to perform at a higher level of value.

In the next wave of competitive advantage and Intelligent Applications, it’s not via data structure alone that value will be created. You need machines that can reason, you will have to describe to them what your view of the world looks like and what knowledge you have of it. You will need to teach computers to think and what to think about, and what conclusions they can draw for you, and when to hand the decisions over to a human.

Cognition Management Systems may well become not just an evolved CRM, but an essential brain for your organization, and one that may well have a dialogue with your clients, both with and without you.


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