Building competitive data advantage
Several years ago, my company faced a significant challenge: A large swath of small new entrants relying heavily on data and artificial intelligence provided services faster, cheaper, and more flexibly than we could. They were not slowed down by legacy information systems, archaic business processes, and an outdated workforce. To add insult to injury, the new entrants would use our customer-facing transparency opportunistically to pick up the low hanging fruit, and gradually started to compete with us on our core practices. At the same time, other incumbent market participants had started to innovate.
In this article I would like to share our lessons learned, and discuss how data assets can both be used and should be protected, as to build a defensible competitive data advantage. This is a perspective based on my experience on capital markets and other industries that places innovative technology at the active business foreground as a critical success factor, rather than in a passive support role.
Based on my experience, I believe the questions an organization should be asking are:
As noted, a radical shift to data driven business processes was urgently needed in my organization. We had to pioneer new AI-based business processes with and for my teams quickly. We decided to set up a new production centre apart from our existing business and competitors. I decide to hire and manage a dozen persons that specifically had never studied or worked in our industry, to start with fresh and open minds. This team included a high school maths teacher, a statistician, a lawyer, an economist, a farmer, and an accountant. What they had in common was grit and competitiveness.
The above illustration shows some of my team’s lessons learned, and symbolizes how data and AI go hand in hand, wholly depend on each other to add business value, yet should strategically be directed differently. Our business goal is or should be to build on our data assets to drive organizational effectiveness. We aim to accomplish this goal whilst shaping our external data exposure in a manner that enhances product and service offerings yet greatly burdens competitors who try to copy our data, freeload on our platform, or use our public data to pick us off opportunistically.
Looking at how we build on data with AI for internal purposes, three clusters of business value can be identified. I believe leveraging data with AI amounts to making it easy to work with data, automating task execution, and using the data to leverage human talent:
By enhancing ease of access and the interfacing experience with (legacy) information systems, users can spend their time more effectively. Some of these usability systems are colloquially referred to as chatbots. Due to the human-natural language interaction, the user base scope is broadened, as users no longer have to express themselves in a scripted manner as pseudo-programmers. In addition, these systems can help process and present complex information. A practical example of this is how at Watergroep (a water utility) we demonstrated the value of this concept with resource management (cars and meeting rooms) through a native Dutch-speaking chatbot system.
Now that a lot of work can be represented through data points, the domain for automation now extends to repetitive knowledge work. At the desktop level this involves automating user interface interactions through so-called robotic process automation tools. Automation is not limited to the digital realm however – for example in logistics robots are replacing human workers. In that setting drones collect visual data, which feeds supply chains and the robots that operate them.
