Riku Ruokolahti: Reputation, Data Analytics, and Decision-Making
“Excuse me, but are all those factors that make up reputation really equally important and significant?”
Measuring reputation alone is not yet sufficient as a decision-making tool
The previous chapter emphasized the importance of measuring reputation in a leadership context. The purpose of the chapter was to describe leadership challenges and related solutions through a relatable story. The lesson could be summarized as follows: a shared goal requires a shared understanding of the situation. Being on the same page creates the conditions for working together.
In this chapter, we briefly discuss the role of data analytics in decision-making. After all, just because we’re on the same map doesn’t mean we know which route to take. Measuring reputation alone is not enough to support decision-making. In addition to measured reputation, we should also have a fairly detailed understanding of its mechanics and impact.
But why are we only touching briefly on such an interesting topic? Because we are now in an area that comes uncomfortably close to the core of T-Media’s business: the specific details of measurements, data analytics, and related methods. We cannot discuss these in such publicly available documentation.
What does "Evidence-Based Reputation Advisory" mean?
Let’s return to the topic of Jätti Yhtiö Oyj’s reputation. The statistical modeling of the company’s reputation is, in itself, very clear, and it sparked a highly interesting open discussion about causes and consequences. As a consensus of the wide-ranging discussion, the group immediately felt compelled to consider how each topic manifests itself in our actual operations, and, on the other hand, what kinds of channels the information on each topic takes to reach the general public in our case. So how do we communicate, and how are we perceived? However, it is too early for that. Wouldn’t it make more sense to first get a handle on the big picture and understand what is actually meaningful? After all, this metric essentially covers the entire company’s operations, and a discussion about it won’t be very productive unless we narrow it down.
Every now and then, you come across a situation where someone raises their hand and asks, in the middle of a preliminary discussion on reputation building:
Excuse me, but are all those factors that contribute to reputation really equally important and significant?
The answer might go something like this:
We hadn’t planned to discuss this just yet, but you’re on the verge of understanding the most important aspect of reputation management. The individual components are certainly not equally important or significant. Their significance depends on the industry’s operating environment and your organization’s current ethos, the business context, and the driving forces of your stakeholders. For example, sustainability means something different to a limestone quarry than it does to a communications consultant, and consequently, the impact of that specific reputation dimension on an organization often varies quite significantly. And the same applies to all reputation components and all organizations. We’ll return to the specific situation regarding your organization very soon, once we have access to the actual reputation impact analysis.
Let me summarize the previous point. The expected benefits of reputation management can only be realized by understanding exactly what triggers those benefits or what prevents them from materializing. Answers to these questions can be obtained using a research model that applies data analytics. This generates statistical evidence to support targeted decision-making. How much does corporate responsibility affect demand? Does it have a greater impact than, say, dialogue or employer image? And so on. These matters are not self-evident. They must be understood, and where possible, statistical evidence must be built to support priorities so that efforts can be reliably directed toward the right issues and maximum benefit can be derived from scarce resources. This allows managers’ time and resources to be directed toward the most impactful issues in an unbiased and objective manner. This is evidence-based reputation management (Evidence Based Reputation Advisory).
We cannot delve any deeper into data analytics and research models in this context, but it should be noted that there are quite effective solutions for the issues described, which have proven their worth in practical management work.
Riku Ruokolahti has written a handbook on corporate reputation and reputation management. The chapter published here, “Reputation, Data Analytics, and Decision-Making,” is found in the second section of the handbook: “Systematic Reputation Management.”
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