Data And AI Alone Won’t Bridge The Ignorance Gap
: when AI can help build new knowledge which lead to competitive advantage. AI will help analyzing the complex system of modern business, providing new insights, and helping determine direction in areas not seen before. But to be efficient, cognitive strategy relies on 2 conditions: a shared sensemaking to foster collaboration between silos and on an awareness of one’s beliefs.
The first milestone in the digital transformation is the development of an open and centralized data and technology infrastructure. These elements connect consumer applications, enterprise systems, and third-party partners and provide access to a single source that contains relevant, up-to-date, and accurate information for all internal - and some external - stakeholders. Doing so will enable companies to streamline enterprise-wide data analysis, accelerate product development, and respond more quickly in evolving markets. Internal APIs* will reduce the communication overhead needed to hunt down specific data, negotiate access, and interpret variations. Enabling data to flow freely is far from obvious today. While open and centralized data allows the company to more efficiently and more intelligently assess its overall state of being, the process of collecting that data is complicated by the existence of organizational silos. It can even worsen misunderstandings. Different business units generate data at different rates and in different, often incompatible, formats. What’s more, the existence of whatever was generated is often not common knowledge to the rest of the company. Therefore, using data still means using it selectively as it comes from a single perspective. Furthermore, under conditions of uncertainty which often characterize managerial decision making, individuals stick to both data and decision making processes with which they are confortable. Not surprisingly, for example, finances guys rely on financial indicators. Open and centralized data needs public support from C suite. Non-data driven companies typically see this condition as mainly technological and as a secondary priority.They consequently leave these software projects to siloed business units without reframing the problem as organizational. This leads first to technical sprawl: different business units implement their own initiatives without consulting each other, build often conflicting or incompatible solutions providing unshared data: so long for the common sensemaking…
Assumptions make strong beliefs, Executives’ assumptions make
When you have all these open and centralized data and technology infrastructure packed in the same basket, you can unleash the full power of innovation. Really? This is in fact, very difficult to do when executives had much success in the previous era ;) Do your executive team value data and analytics? You know that they shape the culture of your company. Deploying AI successfully requires that your organization be “AI-ready," i.e. have a strong culture of data-driven decision-making and test & learn culture. There is no point in laboriously gathering data and running sophisticated machine learning models if the analysis will be ignored by executives who don’t know or don’t want to make sense of it. Many of them have climbed up the corporate ladder through gut decisions, not from collaborative, data-driven decision-making. Digital transformation is slow, then, because these executives are so proficient, knowledgeable and caught up in the status quo that they are unable to see what’s coming. Due to their past successes, they prioritize their own beliefs and methods and can be openly hostile to analytical approaches and centralized technology. They insist that their strategy is the right direction for the company, based largely on the fact that they came up with the idea. This process goes unnoticed. They believe that they think carefully when in fact they rely on assumptions without being aware of it. All reasonings are based on assumptions. And assumptions are the beliefs they hold that don’t require much thought. They contain inferences or interpretations by which they draw conclusions and give justifications to decisions. These beliefs are rooted in their subconscious. This is what makes them so powerful and difficult to bring up to the surface and examine. They represent a paradox: executives take these beliefs for real without really thinking about it. But theirs beliefs are limited by theirs own experiences. They can mislead them in new situations. For that matter, imagining something completely new is practically impossible. They would always start from what they already know when they are faced with a new challenge. Intuition-driven approaches may have worked in a bygone business era when no one had access to data or computing. While data alone cannot make decisions for them, combining the right information with experience, creativity, and an unbiased perspective will enable executives to make better decisions. Better then, you understand that before your main competitors ;)
On the managers’ level, supposing that access to all data they need is possible, you won’t be a good manager, if you are not familiar with analytics and good at structured thinking. As an analyst, you are expected to put structure to unstructured problems. As a manager, you are expected to excel at putting the structure in place. You will be soon expected to tell data based stories. The base expectation is to be good at communicating your thoughts. A good manager will be able to visualize data effectively and present it in a manner that it narrates a coherent story. In others words: being able to accelerate knowledge production from data. There is indeed, a difference between data, information and knowledge. From one to the other, managers must make cognitive leaps aka enrich data with meaning: from having a very basic knowledge of a topic but only in one area to knowing that there are several different components to this topic without seeing the connection between them to understanding the interconnectedness of ideas within the topic. Supreme added value: some managers could reach the level of a complete understanding of the concepts and stretching it to create a new idea: welcome innovation! Adding value to data and turn it into knowledge that allow relevant decisions to be made is the next edgy advantage companies will race for.
Rather than working to perpetuate the myth of the omniscient leader, executives should be searching for ways to collect and correlate the organization’s distributed wisdom. A little more humility at the top, and a lot more bottom-up knowledge aggregation, could substantially reduce the ignorance gap every company has to bridge.
API: An application programming interface (API) is an interface or communication protocol between different parts of a computer program intended to simplify the implementation and maintenance of software. Wikipedia.