Emergence of Cognitive Enterprises

Rather, this forms the basis to discuss what capabilities and enablers are needed for a company to journey from one stage to the next.

Data as an Infrastructure

Very simply put – Garbage in, garbage out. If your predictive analytics and intelligent systems are being fed inaccurate data, then the right answers can never be produced. Therefore, the right data infrastructure is absolutely key to a cognitive enterprise.

Within the scope of data architecture, centralization of the enterprise’s valuable data is arguably the most key aspect. With data in silos, even the most basic of automation will be hard to achieve and sustain. By placing all our data in one (logical) place and having the means to access it in a timely and secure manner really multiples the value of the data. At SGX, tying together gateway latency information with members’ individual orders made it possible to identify orders with anomalously high latencies and determine the root cause, whether it was order throttling, network congestion or other causes.

We all know what KYC is, but how many of us practice KYD? This is an often overlooked aspect of data analytics - Know Your Data. There are software and tools available that help to understand the shape and content of the data, but there is no replacement for simply spending the time to get to know your data. That means understanding not only how it is organized and what is contained within, but also how it was generated and collected. Knowing what is not included in the data is as important as knowing what is included.

Building the Right Team

Developing competent data and analytics capabilities is a combination of building the right team and building fit-for-purpose platforms, with the former being arguably more challenging. Implementing cognitive technology requires many different functions, including data engineers, machine learning engineers, data scientists and data visualization analysts. Balancing out those skills is not simple, and furthermore there is the question of whether to hire specialists for each function, or put together a team of generalists.

A recent Harvard Business Review article (“Why Data Science Teams Need Generalists, Not Specialists”) argues for full-stack data scientists over specialists, although my take is that you will need both types of people at different stages of your journey. Build a team of well-integrated generalists that are enhanced by specialists in the areas that you need faster growth and immediate results.

Start at the Top, and Start Small

Cognitive technologies can be bought, but integrating it into the heart and soul of your company means that a sustainable approach is needed. This is not an effort that is driven out of IT; rather, it is a concern of all business units and has to be part of corporate strategy and goals. Getting senior executives on-board to put together the vision and direction will increase the chances of success, and having that alignment and clear direction at the beginning will make it easier to know where to go.

Data science, which plays a big role in cognitive technologies, is often exploratory and experimental. When starting the cognitive journey, identify a small number of impactful use cases. Once those efforts gain traction, and the value of cognitive technologies has been demonstrated, then start scaling from there.

The End Game

The immense growth of data will continue for the foreseeable future, and at the same time, cognitive technologies are continuously improving and evolving. Organizations that can effectively harness the capabilities of innovative - and intelligent - technologies will be able to differentiate themselves and delight their customers, while others will find themselves being increasingly left behind.