Creating Enterprise Value with Information

If one observes the focus of Chinese and American investors and the research priorities at Alphabet, Facebook, Amazon, Baidu and/or Tencent, there can be no doubt that artificial intelligence (AI) is a megatrend that will fundamentally influence the development and valuation of companies in the years to come. In the meantime, the development of AI has made such tremendous progress that we find ourselves in the transition from the age of discovery to the age of implementation (cf: Kai-Fu Lee: AI Superpowers). In this phase, the capability to develop ingenious AI algorithms again becomes less important, for here, the respective libraries provide increasingly greater assistance. However, what proves decisive is the access to information that is relevant and as comprehensive as possible so as to enable the development of deep learning algorithms.

With it, the well-known speech about the impact of data for future business value creation becomes even more emphatic: As the performance capacity of applied AI increasingly determines a company‘s competitive edge, the range, quality, and relevance of information accessed by AI also become more important.

Information as an Asset with Special Attributes

In his fundamental book Infonomics (2017), Douglas Laney created the terminological basis needed to regard and valuate information as a corporate asset. A bold example of how information can create enterprise value is our start-up Statista. From the get-go, the value creation of Statista has been oriented towards collecting, analyzing, and monetizing information. To date, Statista has over 500 employees and clients in over 100 countries.

With the example of Statista, it is also possible to elaborate upon the special attributes of the asset category information:

Of course, most companies – be they athletic footwear producers or automobile manufacturers, banks or airlines – have now acknowledged information‘s intrinsic value and have intiated projects for the systematic compilation and central collection of information, especially that from customers.  Often, however, this still takes the form of IT projects that are conducted more in terms of a cost center versus business development mentality. By doing so, such projects are too short-sighted to tap into the company’s full value potential of information.

Information as Levers for Value

In order to do justice to the essential significance that accessing information will play in the future competition of AI-supported companies, it is worth adopting the radical perspective according to which the value of a future company shall result: 

1. from the breadth and quality of infomation available to it,

2. its capability to monetize this information, and

3. the unique selling point that emerges from this available information plus its ability to monetize it.

From this perspective, traditional material production assets derive their value from the fact that they monetize information - such as the vehicle fleet, with which information is monetized when a particular customer wishes to reach a specific location. In this perspective, "big data" analyses are not so much a means of optimising the efficiency of given production assets, but first and foremost establish one's own production as the best way to monetise the information inventory in the company.

Certainly, this perspective makes major simplifications. However, it makes sense to adopt this approach in order to explore how the strategic handling of information can serve to boost enterprise value.

This becomes most apparent when observing the evaluations of news aggregators and marketplaces, which instead of focusing on their own production, concentrate on information processing.

The conventional platform economy examples (Amazon, AirBnB, Uber, etc.) can also be mentioned here. However, this should not imply that the strategic value of information depends upon highly scalable B2B2C marketplaces for its development.

Information as a Basis for Social Progress

How to play

In order to maximize the value of information, information should not be construed as a by-product of a production and marketing process. Rather, information constitutes its starting point, and at the same time embodies one of its most important results, since it establishes the next point of departure. In our examples, the information base has always been dynamically expanded with new data points. Moreover, in most cases it did not merely consist of the information generated from one's own core business. In addition, there was information from

• Third-party sources (examples: meteorological data, information from various medical service providers, sociodemographic information)

• Additional information acquisition processes (example: evaluation of satellite images for soil data)

• Newly defined information categories with new assessment methods (e.g., Self Esteem Index for students, including the relevant psychological questionnaire).

In addition to expanding the information base, the second major value lever entails broadening the use of information. The use of information to optimize existing business is patently clear. To this can be added the opportunities to expand current business on the basis of such information or to roll out additional products. And finally, there is the option of marketing the information independently, either in raw form (as a database) or in aggregated and evaluated form (as a report).

The way to create value with information leads towards the right on the information acquisition axis and towards the top on the information utilization axis.

Conclusion

Behind the information perspective, there are value levers for every company, whose significance becomes progressively more important with the advent of AI. Alphabet, Amazon, Facebook, Baidu, Tencent, and many other flagships of the internet already operate at present as if their enterprise value were a product of the scope and monetizability of the information within their reach. Indeed, there is every reason to immediately begin with the aggregation and expansion of the information base and to take further paths towards monetizing the information, and to not wait for even more powerful AI algorithms to become available.