Case Study 8.2 Type A 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 support for senior-level decision-makers

Decision Trigger: Evaluation

Decision Type: Strategic

Decision Style: Data-driven

Manager Type: Type A Analytics-Bent

Organizational Analytics Maturity: Mature

Industry Influences: Data-driven

Societal Influences: Regulated, legal reporting requirements, data security and privacy restrictions

John is a Type A, analytics-bent, managerial decision-maker. He is a business analyst working for a large financial services company. The company is New Zealand-based, but has important connections with international partners. Johns bent for analytical thinking was strongly influenced by his university undergraduate studies in mathematics, and statistics during his Masters degree in Business. While he has had no formal training in data and analytics, he has five to six years of work experience with them. Currently he manages a small analytics team that often contributes to strategic decision-making in the organization. He explains:

I report directly to the CEO and provide key analytics, key data, for making strategic decisions for the business. My group oversees all the business performance reporting on a day-to-day, week-to-week, and month-to-month schedule that guides the understanding of where the business is at.

By contributing these data insights, John is an integral part of his organizations strategic decision-making. The decisions he is involved with are usually high-data decisions, but also include balanced decisions with high data use. While this high-data decision-making process usually begins with using judgment as an initial assessment, it is quickly followed by the gathering of as much information and data as possible. John critically examines his initial assessment for any potential biases he might bring to the decision-making process and challenges them before beginning his analysis of the problem using data analytics. He reflects on this process:

Whats my preconceived idea about this? Im not afraid to recognize and challenge it. I try and remind myself not to try to find only the information to prove my initial assessment. I think a lot of people use data that way; they think: Oh yes, I know what the answer is, lets find some data to support what I already believe. I think thats how people misuse data. Better to ask what is the situation here? Can data bring clarity to it? If yes, how am I going to gather this data? And then I make a quality observation and analysis. Its like I follow an almost experimental methodology every time I use big data to make a decision.

John finds that using data is a balancing act and that even decision-makers with the analytics bent have to be mindful of their extent of data use: I think theres always a danger of dismissing it or over-relying on it. Theres always a fine line, even if someone is analytical. As a last step in the decision-making process, he also consults others to sense-check his analytics results: I usually bounce it off other people in my team. His colleagues are open to this process. Furthermore, the analytics that John develops are used in organizational strategic decision-making, and are also checked and challenged by his superiors. John needs confidence and communication skills to defend them when this happens.

John emphasized that he has always had the analytics bent, and describes the influence of the company culture on his decision-making approach as a good fit rather than an influence. He explains: Im not sure if the company culture influences me as much as it supports how I already think and work. The financial services industry also relies heavily on data analytics: again a good match for his analytical personality.

Comment

The data-driven decision-making approach reflects Johns educational and work experiences, and the data-driven environment encouraged by his company and the financial services industry. John is definitely an advocate of data and analytics and sees them as a way to challenge judgment and make stronger decisions, but he also recognizes some of their limitations and the way they can be misused or misunderstood.

Question to answer:

What kind of issues John might have when working with people who are not analytics-bent? How are the issues different when he works with those from executive management?