Infonomics - The Future of Business

Most likely you do not know the value of your company brand and data. Data and brand value are not computed as an asset on the company's balance sheet, along with real estate, equipment and machinery. And that's how it works in the vast majority of companies.

Companies today cannot calculate the book value of an item that is viewed as intangible. And no doubt they will stop profiting from it.

And that is precisely the starting point of the Infonomics book, written by Douglas Laney, former VP of Gartner, Forbes columnist and recognized as one of the Top 10 Influential Data Leaders. Infonomics comes from “Information Economics”, a theory formulated by Laney that seeks to measure data to measure information as an asset for competitive advantage, and seven steps to take to monetize information assets.

Staying ahead in the fast-paced race of Artificial Intelligence and Big Data requires executives to make agile and informed decisions about where and how to employ AI in their business. However, progress is slow, either in companies' apparent fear of investing in the latest technologies, or in not seeing the relevance of this for the future.

Recent research by McKinsey reveals that only 8% of executives surveyed engage in core practices supporting the widespread adoption of AI at their companies. Most organizations are applying AI in just one business process!

These companies have not yet realized that achieving results at scale requires not only the diffusion of AI resources across the enterprise, but also a real understanding and commitment by leaders to drive large-scale change, with a focus on managing those changes, not just in technology.

When we think of an AI problem, we link our thinking to three main aspects: datasets, models, and environment. The form and frequency of the data, the nature of the problem, and the amount of knowledge available are some of the elements that differentiate one type of AI environment from another. Quantity, depth, quality, diversity, and access are the five dimensions that affect or what data sets can do for Artificial Intelligence.