Wednesday, April 25, 2012

Comparison, Context, and Connotation: Turning Data into Insight

How can we turn inert data into dynamic insights? We turn measurement into data, data into information, information into meaning, and meaning into insight. Each of these four steps is critical. If you are building talent measurement systems or using talent measurement as part of your management responsibilities you should know the three key Cs.

Turning Data into Information: The Key of Comparison

Many organizations have generous amounts of data filling their databases, but few organizations make use of it. Data by itself is meaningless. And at this point, it’s clear that our processing skills haven’t kept pace with our ability to collect terabytes of it. 

Imagine looking at a column of numbers. What does it tell you? Nothing, I imagine.
Now imagine that you can look at the same column of numbers with other data points for comparison. You might compare each piece of data to:
  • Time (each data point is part of series across time)
  • Employees (each data point is one employee’s performance)
  • A norm (one number represents typical performance, and another number represents actual performance)
  • A goal (one number is the target, and the other number is the actual amount)
  • An implication (one number is an employee’s performance, the other number is an incentive payment associated with it).
Data is useless unless it is compared to something. Trends matter. 

The need for comparison isn’t always obvious, however. Occasionally, I’m asked to analyze an employee survey and there’s no good way to compare the data. Often, data is collected without considering how it will be turned into information. you have to compare across time, subgroups or at least across different questions. To turn data into information, you need a comparator.
To understand a survey, comparator can be previous survey results, a goal, normative responses, or the results of measurement in similar organizations (the basis of benchmarking).   In a pinch, you can compare survey questions to each other, but this provides limited information.

Turning Information into Meaning: The Key of Context

Information, however, is also never useful in itself. To have meaning, it needs to  be placed in context and interpreted. It’s critical to ask why.

  • Why does the pattern of numbers vary with time?
  • Why do employees’ levels of performance vary? Are there differences in aptitude, skill, or motivation? Or are they working in different environments?
  • Why is our data above or below the norm?
  • Why are (or why aren’t) we achieving the goal?
  • Why am I receiving a smaller bonus than other employees?

In many situations, meaning is elusive because it requires a broad understanding of context. If two employees have very different performance results, are they working in the same context? Does one employee have more difficult tasks—a more involved project, a larger territory, more complex machinery to run? Only when you’ve determined that the context is comparable can you infer that different levels of skill or motivation underlie the difference in results.

Management is drowning in information. As we collect more and more data, comparison becomes easy. But, putting information into context requires bridging different data sources, integration, and creative thinking. The strategic and operational environment matters. Important information is relevant, given the context. 

Turning Meaning into Insight: The Key of Connotation

Given the process of translating data into meaning, it’s easy to see why there is miscommunication. Two people looking at the same data can have completely different interpretations—and they may not even know it. 

This may seem paradoxical at first; after all, measures are precise way of communicating. We wouldn’t say a car “doesn’t need much gas.” Instead, we use mathematical precision to talk about miles per gallon. Imagine the reactions of shareholders and analysts if an executive talked about “a pretty good investment,” rather than discussing a percent return on equity.

Nevertheless, people with different perspectives or goals see measurement results very differently. As with other types of communication, measures have denotative and connotative  meaning. Making a distinction between the two types of meaning is the key to understanding measures.

To illustrate the difference, let’s look at the term re-engineering. In literal (denotative) terms, re-engineering is a way to understand business processes and optimize them. In subjective (connotative) terms, there are implications of re-engineering—mass layoffs. I once used re-engineering as an example in a speech that I was giving in a company and received a strange and hostile response.  It turns out that the there was a history of using the term euphemistically!  That speech never recovered—the mere use of the term destroyed any trust between the audience and me.  

In measurement, the denotative meaning is often defined mathematically. It is the connotative meanings, however, that often matter to employees. In other words, the implications of the measures are more important than the measures themselves.  

It’s important to remember that the implications of measures are personal. How employees interpret measures, and how they react to performance appraisals, are influenced by their upbringing, their personalities, their motivations, and their worldviews. 

For example, if a salesperson is focused primarily on money as an indicator of success, he may consider performance measurement only in terms of the size of his bonus. It’s likely this will lead to misunderstandings with a company executive, who is looking at the measures to answer different questions: Does the salesperson need more training? Is the product “good.” Did the customers have good experiences?

In another common example, it may not be possible for an employee to receive measurement-based feedback constructively for any number of reasons. If the employee is perfectionistic or competitive, she may only be able to receive the feedback as criticism. Another employee may be so consumed with feeling miserable about failing to meet last year’s goal that he can’t engage in an authentic conversation about the future.

In my experience, many employees will try to avoid measurement because they distrust management, or are afraid of being targeted in a blame-oriented culture. This example of connotation is probably all too familiar to the readers of this blog.

Of course, personal perceptions and assumptions can be influenced. To build meaning, and ultimately insights, organizations must spend time decoding both the denotative and connotative meaning of measures. Unless both the denotations and connotations are addressed, there is little chance of communicating with the measures successfully, or gaining insight from the data.

Decoding measures is a dialogue. As a consultant, I need to discuss both the objective and subjective implications of measures with my clients. Managers need to do the same with their employees. It is only through dialogue that the measures’ contrasts and contexts, the meanings and implications, can be understood.

This is the path from data to shared insights. Of course, the path is littered with suggestions from many sources. But the salient point remains: To reach shared insights from measurement, organizations need to confirm that there is shared meaning. We can think about this as a four-step process:

There is no data without measurement, no information without data in comparison, no meaning without an understanding of information in context, and no insight without communicating shared meanings.

Is your organization taking steps to make sure that insights are gained from measurement?

In the next posts I’ll talk about motivation, organizational learning, and accountability. In the meantime, I welcome your thoughts.
Charley Morrow

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