The Real Challenge in AI is HI - Human Intelligence

Data science in companies serves predictive intelligence to identify market and business opportunities. more precisely and to recognize them. However, this requires more than just building technology.

Cologne Center, Tuesday, 9:00 a.m.. Carla Kowanda, Chief Sales Officer at Talto - Talents of Tomorrow, one of the leading providers in the field of employer branding, opens the Sales Meeting. The company's own Predictive Intelligence Portal (PIP) is her constant companion, because from it Carla can always identify and quantify future market opportunities and business opportunities in an up-to-date and interactive way. This is the be-all and end-all for the 30-year-old young manager, because the company, which was only founded in 2019, has to plan and deploy its budgets in a very targeted and precise manner. Like her, it is now not only the case for many hidden champions, who represent an essential pillar for the domestic economy, but also for an increasing number of large multinational companies, which are having to accept massive budget cuts as a result of the multiple crises currently raging.

"If human intelligence is inadequate,

the best data intelligence technology won't help."

The PIP is the result of a research project that was realized within four months as part of a course on predictive intelligence at Munich University of Applied Sciences in cooperation with the leading B2B conversion rate optimizer (CRO), Frankfurt-based Fynest International. Based on defined key factors, the PIP shows Carla always up-to-date and interactive, which (federal) country has to be targeted in the context of internationalization of the company in terms of the best Return-on-Sales (RoS). It is about company and business model relevant factors such as open jobs, number of students, number of companies but also factors such as number of universities in a region or costs for rent and wage levels. All of these factors are used by the PIP to create a heat map comparing the market attractiveness of the relevant market. This means that the maximum return on sales (RoS) can always be realized and the maximum growth generated. That this works is shown by the figures and current valuations of the still young company with already 10 million euros - and rising! Talto - Talents of Tomorrow thus belongs to those 10% data-driven companies that are no longer data-blind.

Still 90% of all companies are data blind

In the age of predictive intelligence, a recent study by Fujitsu from the year 2021 once again makes it drastically clear what the state of the economy is with regard to data science - namely, it is frightening. This is because 90% of all companies and their managers still consider their own company to be "data-blind". Europe performs slightly worse than American companies. This state of affairs is comparable to an airline pilot who knows the coordinates of the destination airport in Singapore, but without weather and GPS information has to find his way halfway around the world virtually flying blind and using his gut feeling. Would you like to be one of these 300 or so airplane passengers?

 Currently, however, the domestic economy is de facto on the way as described in the pilot, and thus risks not only jobs, but above all also the domestic business location. In a world of data management, data science and even artificial intelligence as an enabler for the actual goal of establishing predictive intelligence, the basis is lacking. Companies have not done their homework and are stumbling back into the CRM paradox. In the 80's and 90's, expensive, then novel "miracle systems" such as Siebel or Oracle were purchased in the context of Customer Relationship Management, in the belief that this would enable the useless mountain of data to be cleansed and organized. According to recent surveys, however, around 80% of all these CRM systems have failed to deliver the expected added value in terms of return on investment (RoI). Why? A simple example can help: Why, for generations, have our children first learned mental arithmetic in elementary school and only then learned to work with a calculator?

The learning approach, which has remained unchanged for decades, is based on the simple and valid theory that in order to be able to operate and use a calculator meaningfully, one must first understand and master the basic mathematical principles. Why should this principle no longer be valid in the context of CRM systems or AI systems? All these systems are - just like the calculator - technical aids, but they must be operated by and with the correspondingly required basic Human Intelligence (HI). If this HI is not available, then the best data intelligence technology is of no help, because "a fool with a tool, remains still a fool! 

Data Science - The Road to Data-Driven Management (DDM)

As always in life, success is the result of many small steps. It takes concept and competence. The concept for sustainable and successful implementation of data science measures in companies describes the maturity model for data-driven management. All required fields of activity are defined and described on the basis of four development stages. The decisive factor here is the step-by-step build-up of knowledge within the organization itself, as concept and competence are required. Data-driven management will only become established and manifest as a management philosophy in the long term if the company itself intrinsically develops the subject area. With the all-important commitment of top management, an organization can pass through the four stages on the way to dynamic modeling predictive intelligence in just 12 to 24 months. The following factors are critical to the success of such a project on the part of top management:

• Model and communicate the essence, meaning, and principles of data-driven management.
• Recognize and manage data as a strategic resource
• Build up knowledge and expertise on data-driven management within the company stringently and step-by-step - only buy in targeted support from external parties on the basis of template-based management (TBM)
• Build, measure and communicate competitive advantage from the start through data-driven management