Using Big Data technology effectively, such as Map/Reduce or NoSQL, has two components: technical skills and high level expertise. An article in Harvard Business Review, January 2013, entitled How IT Fumbles Analytics, stated "Improving how businesses extract value from data requires more than analytical tools. It involves creating an environment where people can use the company's data and their own knowledge to improve the firm's operational and strategic performance."
Data Science projects typically start out with a framed business problem, which leads to data collection, running some data mining algorithm or map/reduce program, and then analyzing the results. Data collection and running algorithms are dependent on technical skills. On the other hand, analyzing the results includes discussions, which depend on high level expertise. Analysis is where the work of the Data Scientist really shines in creating competitive advantage because this is where the technical skills are combined with the high level expertise. The technical skills of Big Data produced the results for analysis, and the expertise interprets and applies that information to creating new solutions and levels of success.
The problem then becomes a management issue of integrating the high level expertise with the technical skills in order to capitalize on the results of an algorithm or map/reduce program. There are many approaches to solving this problem, but for simplicity let's focus on one that inhibits open discussions. Two researchers, Mantere and Vaara, analyzed business oriented conversations. They found three approaches to conversations (social interaction) that suppress participation, which they termed Mystification, Discipling, and Technologization. Mystification occurs when a decision is handed out without explanation. The origin, reasoning, and cause of the decision is not known to the person carry out the decision. Discipling happens when the "how" is defined for the employee. The employee is not allowed to be creative or tailor the instructions when carrying out the directive. Technologization is when predefined measures guide employee actions more than the actual directive. If these three concepts are in place, employee involvement in discussions is quite limited.
Each of these three types of discussions has a means of avoidance, which need to be implemented long before the analytic discussion takes place. In the natural execution of daily activities employees happen upon unexpected obstacles continually. Each of these daily obstacles is a decision point for the employee. Without greater understanding of the original objective set by senior management, freedom to deal with obstacles as they come up, and confidence that creative solutions will be rewarded, employee actions can stray unintentionally.
One way to get rid of Mystification is to ask employees to interpret the directives. If different employees give different answers to the same directive, it is time for the senior managers to open up the dialogue more. One way to get rid of Concretization is to ask the employees if there are better ways to getting things done. Ask them if they feel restricted. Also, refrain from telling them how to do everything. Trust your employees to be creative. One way to get rid of Technologization is to ask employees if they feel recognized. Another means is to recalibrate the metrics with past rewards. Are the two in sync?
In summary, these three types of discussions are to be avoided. The three recommendations boil down to 1) asking employees to state the objective, 2) giving freedom to employees to be creative in the "how" part of any task, and 3) questioning whether the metrics are in line with directives. The above recommendations will help you increase employee participation in the discussions that are based on the results from analytic projects. Employee expertise combines with technical skills to produce innovative ideas. This combination will result in even greater insight, more informed decisions, and beneficial actions.