Case Study 8.3 Type B Manager in a Data-Driven Organization

Textbook used: Management Decision-Making, Big Data & Analytics By: Simone Gressel, David J. Pauleen, & Nazim Taskin Publisher: Sage, 2020 ISBN: 9781526492005

Decision Parameters Decision Situation: Ongoing decision-making in response to organizational initiatives and external factors Decision Trigger: Evaluation, routine check, external Decision Type: Tactical and strategic Decision Style: Balanced Manager Type: Type B All-Rounder Organizational Analytics Maturity: Mature Industry Influences: Best practices, highly competitive, data-driven, but in some ways traditional and conservative Societal Influences: Heavily regulated, legal reporting requirements, possibly pressure from consumer groups, etc. An example of Type B decision-makers is Sarah. Sarah has been working for 20 years in the New Zealand subsidiary of an Australian-owned bank. She has extensive business understanding and domain experience and is now a senior manager in operations. Once very traditional and conservative, banking has become over the last 20 years or so a highly data-driven industry. Banking operates in a competitive environment and is subject to strict reporting requirements by governments. Since starting out in this data-driven company, Sarah has been using data and analytics in her decision-making, which have provided her with an appreciation of their value. While she never received any formal analytics training, she undertook several in-house and external management training courses, which taught her to analyze problems from different angles using various analytic techniques. Sarahs most recent role was as a leader of a team evaluating the operational model of her organization, which she considered a very data-driven task. In this role, Sarah regularly takes part in tactical and strategic decisions for the bank, often involving other senior executives, business analysts and data analytics support staff. Many of the decisions Sarah makes rely to a high extent on data up to 80 per cent but they also incorporate significant human judgment. This matches her general decision-making type of all-rounder, especially as Sarah explains that the data itself is already full of assumptions, and therefore contains significant amounts of human judgment. She explained: You could have a 100 per cent data-driven decision, but its 50 per cent based on assumptions. This awareness of the intertwining of data and judgment usually leads Sarah to follow a balanced decision-making approach. For Sarah this balanced approach begins with a thorough initial assessment involving both judgment and the acquisition of all relevant data and information, similar to Type A decision-makers. However, once the decision-making process reaches the step of developing alternatives, Sarah follows a different approach. While she relies on data experts during this step to conduct the actual analysis of the data, she engages her judgment to sense-check and challenge the data results. As she explains: I know what questions to ask, I have a sixth sense in being able to understand if the data is accurate from my questions and from what I hypothesize in my head. Sarahs business understanding and experience play a significant part in this step of the decision-making process. For the selection step, she then takes advantage of her excellent communication skills to convey the analytics results to other stakeholders: I know how to format and present data so that it helps to tell a story. So, I have people that find and create data. I have specialists that then turn data into insights, and then I have people that create insights that support business cases and presentations. I can work with all levels or areas, but I particularly like to work on the presentation end, in the sense of: I can show meaningful insights and tell you all a story. Comment This well-rounded and balanced decision-making approach incorporates both Sarahs analytics skills and her judgment. The data-driven environment provided by the industry and company, in combination with the management training courses she attended, certainly supported her in becoming an all-rounder. After 20 years with the company, she identifies with their values and approaches. She shows a high appreciation for data and its potential, but she also understands that accessing and analyzing data are often time-consuming processes and are considered deterrents to data-driven decision-making by many. Sarah also recognizes the potential biases built into data sets and analysis. She recognizes these and other limitations of data analytics and therefore values the balance provided by human judgment when decisions need to be made. Question to answer: What kind of issues might Sarah have when working with people who are not all-rounders? How can she manage these issues?