We all have made recommendations based on sound evidence, only to find the decision-maker did not share our belief that it was the right thing to do. The human tendency is to push harder, emphasizing the evidence that supports the recommendation. But the behavioral scientist Kurt Lewin found that decreasing resistance was more effective than increasing force.
When decision-makers are considering actions that involve significant change barriers are common. People must believe that the benefits to them outweigh the costs to them if they are to consider change. Resistance is apt to be very strong if the decision-maker must reverse a prior decision (s)he made. And if an admission that the prior decision was wrong is required the intensity of the resistance escalates. As evidence-based management becomes more widespread it is likely that improved analytics will increasingly show prior decisions were less than optimal. Many of those prior decisions were made using experience, intuition and judgment, since tools like workforce analytics had not yet been sufficiently developed or accepted as another valid form of evidence. As technology develops at light speed decision-makers are often overwhelmed by the volume of new forms of evidence and they may be ill-equipped to understand the data science tools that are used to generate it. Consequently, they may feel their decision making competence is being challenged by things they do not understand.
Practitioners can be intimidated by a specialist in another discipline using “tech speak” since they do not speak the language or understand the underlying technology. Understanding how financial analysts, IT specialists or data scientists have formulated their recommendations helps immensely when one is deciding whether or not to accept them. That is not to say that one must be able to perform a multiple regression analysis on a data base to discover what factors impact something. If I am told that education, experience and performance appraisal ratings strongly predict someone’s pay I may accept that because it is reasonable. If I am told gender and race are strong predictors of pay it can alert me to something about which I should be concerned. After all, acknowledging that gender or racial bias is a problem in the organization is both difficult and distasteful.
It is not necessary to be able to do the analysis in order to use the information it generates. But when data analytics produce surprising results which don’t align with common sense or professional knowledge it is likely that the user may be reluctant to act on the evidence without being certain it is valid. And, as said earlier, if the evidence calls into question strongly held beliefs or shows a prior decision to be flawed the level of reluctance escalates even more. So how does a practitioner acquire the knowledge and skill that is necessary to integrate data science into the decision-making process?
If two parties do not share a common language it is like the time I tried to find insect repellent before going into the jungle in a city where no one spoke English. When I found someone who was multi-lingual to act as a translator the problem was solved. But finding someone who is competent in both data science and the discipline of the decision-maker is a big challenge today. This historically has been a challenge when IT people have dealt with neophytes. It also has been common for academics and practitioners to have trouble communicating. The IT challenge was dealt with by creating Systems Analysts, who knew enough about both what was needed by the user and what it would take for Programmers to meet that need. So the intermediary approach is a feasible way to resolve the communication problem. But this still requires finding a multi-lingual party.
Teams are widely accepted as an effective way to resolve issues, assuming the needed skills and knowledge are possessed by someone on the team. So replacing a sequential process by a concurrent process can improve the creation and utilization of data science efforts. Having data scientists do their technical work and then presenting the results to practitioners can result in ineffective communication… the translation challenge. But if both parties work together from the start it is possible for the practitioner to convey what is needed to the data scientist and for the data scientist to inform the user of the alternative approaches to performing data analysis and how each would be likely to address the needs. If two parties are separated by a canyon building a bridge is the way to bring them together. But in order to fully integrate the two parties it is necessary for each to go halfway across the bridge and to conduct an open dialogue in the middle.
This will require practitioners to acquire at least a basic understanding of the scientific method and of how the analytical tools work. It will also require a data scientist to learn more about the issues faced by the practitioner and the type of analysis that will help resolve dilemmas. For example, if the stakes are high the degree of certainty produced by the analysis may need to be high as well. But if the practitioner is seeking an approach that will improve the ability to predict something (i.e., an employee with critical skills leaving the organization) the data scientist might be able to produce an adequate result with data that is of lower quality. Knowing whether approximation or precision is required helps to guide the approach to analysis. As artificial intelligence and machine learning techniques evolve it is increasingly possible to use qualitative data and even unstructured data to produce an adequate result when a precise answer is not required. By approaching the problem with continuous dialogue the two parties can both become more efficient and effective because they share a common understanding of what is needed and how needs will be met.
Practitioners have a wealth of options for developing a basic understanding of data science. Online courses are available and books are being released continuously, both containing the type of knowledge needed by the practitioner. It is somewhat more difficult for the data scientist, since much of the knowledge about practitioner issues is accessible only by hands-on experience. But by using a continuous dialogue between team members this bridge can be crossed. It is of course necessary to convince both parties that they need to add to their reservoir of knowledge but continued frustration in attempting to communicate across languages without a translator should make it clear that this is the way to ensure that the best evidence is used to make decisions and that the decision makers do the best job of using it.