itSMF Bulletin February 2022 | Page 6

1. People

Digital transformation starts with people, which is a useful reminder that whenever we talk about data — especially valuable data — there are humans at the end of it. For most organizations, the people aspect of transformation refers to the access they have to consumers, clients, and employees. Historically, these relationships yielded poor or dispersed records. Think about analog and informal small businesses, such as a stand in a Turkish bazaar: the salespeople have a great deal of access to, and knowledge of, their customers and clients, but it’s all “trapped” in their minds. In the same way, a London cab driver or a Parisian bistro waiter might have in-depth knowledge of their customers and what they want, or a small business founder might know the 20 employees that make up her workforce rather well, without needing much tech or data. But what happens when an organization becomes too large or complex to know your customers or employees on a personal basis?

2. Data

If you want to scale the knowledge you have about your customers and employees, and replicate it across a big organization and in far more complex and unpredictable situations, you need to have data — widely accessible and retrievable records of interactions with consumers, employees, and clients. This is where technology can have the biggest impact — in the process of capturing or creating digital records of people (e.g., what they do, who they are, what they prefer, etc.). We call this “digitization,” or the process of  datafying human behavior, translating it into standardized signals (0s and 1s). It is useful to remember this, because the real benefits from technology are not “hard” (i.e., cheaper systems or infrastructure), but “soft” (i.e., capturing valuable data).

 

 

3. Insights

Although data has been hailed as the  new oil, just like with oil, the value depends on whether we can clean it, refine it, and use it to fuel something impactful. Without a model, a system, a framework, or reliable data science, any data will be useless, just like 0s and 1s. But with the right expertise and tools, data can be turned into insights. This is where technology gives way to analytics — the science that helps us give meaning to the data. To the degree that we have meaningful insights, a story, a notion of what may be going on and why, or a model, we will be able to test this model through a prediction. The point here is not to be right, but to find better ways of being wrong. All models are wrong to some degree, but some are more useful than others.

4. Action

But even getting to the insights stage is not enough. As a matter of fact, the most interesting, captivating, and curious insights will go to waste without a solid plan to turn them into actions. As  Ajay Agrawal and colleagues argue, even with the best AI, data science, and analytics, it is up to us humans to work out what to do with a prediction. Suppose that your insights tell you that a certain type of leader is more likely to derail — how will you change your internal hiring and development process? Or what if it tells you that customers dislike a certain product — how will this influence your product development and marketing strategy? And suppose that you can predict if some clients are at risk of going to your competitors, what will you do? AI can make predictions, and data can give us insights, but the “so what” part requires actions, and these actions need the relevant skills, processes, and change management. This is why talent  plays such a critical role  in unlocking (or indeed blocking) your digital transformation.