The proposition ‘More data = more rationality – we disagree’ is the first out of four propositions on ‘when data hits the organization’. We developed the propositions following our discussion with Thomas Ramge on Big Data and its role in capitalism. All propositions combine data and data-driven effects with the core business of organizations which is decision-making.
One of the hopes or expectations associated with Big Data and its analysis is that decision-making within organizations will become rational. We argue that data may increase the level of information to be taken into account for decision-making; however, decisions still make the difference. They are produced through the division of labor and the games of negotiation inherent in the structures of organization. The organization is still led by stakeholders and their arguments around conflicting goals – the essence of organizational power.
One essential tenet of organizations is the division of labor. It means specialization, manifesting itself in different objectives, professional convictions, and specific optimization strategies. As a result, there will be no uniform, globally shared assessment in any given situation. The presence of supposedly objective data stocks in organizations will not change this. By the time it comes to the analysis of the data various departments want to answer different questions. In the era of Big Data, this might inform assessments and decisions. However, the selection of data and the form the analysis takes will be influenced by the stakeholders involved. They will use the power of ‘objective’ data to serve their own purposes. The power game will involve multiple players and how successful they are with their interpretation of the available data.
It has not only been since the widespread use of data that organizations have been regarded as havens of rationality, as decision-making machines streamlined for efficiency, governed by procedures independent of personal sensitivities or irrational orientations. The organizational mantra is to minimize subjectivity and drive objective, evidence-based decisions. It is not new, that the basis for these assumptions is rather tenuous and that organizations even sometimes have to accept far-reaching restrictions to rational decision-making. The key concept is called “bounded rationality” and dates back to the 1950s. The diagnosis: in spite of all the organizational benefits, the cognitive capacity of decision-makers remains limited. In the discourse on automation and, most recently, digitization, rationalists have created a new sense of hope, and the magic formula is: Big Data. Of course, the increasingly wide availability of data and its sheer volume suggest that decision-making could now be enhanced with additional objectivity. The capabilities of machine learning technology and the development of algorithms also seem to go beyond the limits of the rationality inherent in the limited cognitive abilities of humans.
Although Big Data might help to increase the level of information taken into account, the promises of increased rationality through the use of data in decision-making processes disregards one major reason: extensive specialization, or significant divisions of labor will develop strong local rationalities, forming organizational silos. In decision making processes, Big data will always be viewed from the specific local rationality.
Organizations, being in themselves a division of labor, have always been characterized by differing rationalities. This leads to different points of view on what makes sense and what the best way forward is. These points of view do not result primarily from a lack of availability of data or lack of absorption capacity, but rather from the organizational structures themselves. Since labor is divided in organisations leading to different perspectives and local rationalities, data does not tell the same story from these different perspectives.
For example, in a highly complex production line certain stages always lead to more wear and tear on the tools than expected – as detected by an integrated multi-sensor system. The type of conclusions which are to be drawn from this is a controversial topic. Should the production or even the design of the product be changed? Does the purchasing department have to negotiate with the supplier of the parts used in these stages so that other materials can be used? Will the tool manufacturer have to be re-briefed or even replaced? Increased production costs may be a catastrophe from the point of view of sales, making a product appear unsaleable. For marketing, this does not necessarily mean there is any pressure to act.
Another example would be in cases where patients with rare diseases frequently suffer from their long journey towards diagnosis. On a medical advisory board, leading physicians developed an algorithm for the patient pathways they were able to oversee for their own patients. This algorithm was applied to a Big Data source of the health care system. The result wasn’t only that the initial pathway was reproduced by data analysis. More interestingly, the interdisciplinary group of physicians were able to discuss and detect opportunities for short cuts to increase the chance that patients get diagnosed and treated more quickly.
Both examples show that data will increase the level of information to be taken into account. At the same time, they also show that the interpretation of data leads to decision points, and at that point, local rationalities will still exist, creating the illusion of increased rationality, when it is simply increased knowledge and information. The challenge is to build organisational forums in which the divided interests battle on the way forward.
DR. SEBASTIAN BARNUTZ
is Partner at Metaplan Germany and a keen advisor in strategic planning and the intra-organizational powerplay.
is Partner at Metaplan in Princeton, NJ; he focuses on cross-functional leadership and co-creation processes.
Stay tuned – we will be publishing the following propositions on ‘when data hits the organization’ in the coming weeks.
2. People overestimate the role of data in decision-making situations!
3. Informal loopholes don’t disappear when data comes into play – they are covered up more thoroughly!
4. Power games will not vanish due to increased transparency through data – they will shift!