The Hawthorne Effect, Helter Skelter, and Data Governance

In his book The Half-life of Facts: Why Everything We Know Has an Expiration Date, Samuel Arbesman introduced me to the Hawthorne Effect, which is “when subjects behave differently if they know they are being studied.  The effect was named after what happened in a factory called Hawthorne Works outside Chicago in the 1920s and 1930s.”

“Scientists wished to measure,” Arbesman explained, “the effects of environmental changes on the productivity of workers.  They discovered whatever they did to change the workers’ behaviors — whether they increased the lighting or altered any other aspect of the environment — resulted in increased productivity.  However, as soon as the study was completed, productivity dropped.  The researchers concluded that the observations themselves were affecting productivity and not the experimental changes.”

I couldn’t help but wonder how the Hawthorne Effect could affect a data governance program.  When data governance policies are first defined, and their associated procedures and processes are initially implemented, after a little while, and usually after a little resistance, productivity often increases and the organization begins to advance its data governance maturity level.

Perhaps during these early stages employees are well-aware that they’re being observed to make sure they’re complying with the new data governance policies, and this observation itself accounts for advancing to the next maturity level.  Especially since after progress stops being studied so closely, it’s not uncommon for an organization to backslide to an earlier maturity level.

You might be tempted to conclude that continuous monitoring, especially of the Orwellian Big Brother variety, might be able to prevent this from happening, but I doubt it.  Data governance maturity is often misperceived in the same way that expertise is misperceived — as a static state that once achieved signifies a comforting conclusion to all the grueling effort that was required, either to become an expert, or reach a particular data governance maturity level.

However, just like the five stages of data quality, oscillating between different levels of data governance maturity, and perhaps even occasionally coming full circle, may be an inevitable part of the ongoing evolution of a data governance program, which can often feel like a top-down/bottom-up amusement park ride of the Beatles “Helter Skelter” variety:

When you get to the bottom, you go back to the top, where you stop and you turn, and you go for a ride until you get to the bottom — and then you do it again.

Come On Tell Me Your Answers

Do you, don’t you . . . think the Hawthorne Effect affects data governance?

Do you, don’t you . . . think data governance is Helter Skelter?

Tell me, tell me, come on tell me your answers — by posting a comment below.

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Comments

[John O'Gorman]

Hi Jim,

The answer is neither . . . the primary effect is the same one that works on all things digital, from folder structures to applications to data governance programs:

The structures put in place cannot withstand the inevitable entropy built into semantics.

Combine that with the half-life of facts phenomenon and it’s almost predictable.

Always provocative you are . . .

John O'

[John Owens]

Hi Jim,

The Hawthorne Effect is an interesting, if controversial, study with many interesting outputs. I fear that the social and industrial psychology of that time covers up the true significance of many of the outputs.

My experience suggests that measuring people’s performance can have both positive and negative results. When people are being measured and receive positive feedback on their performance then, not surprisingly, they feel appreciated and productivity rises. When people are being measured and they perceive the measuring to be a means by which they might be penalized (as was too often the case in so many of the old “Time and Motion” studies), then productivity tends to fall.

The same is true today when measuring people’s data quality in an enterprise. It is the perceived purpose of the measuring that will determine whether or not the result will be an increase or decrease in errors/governance/conformance, rather than the actual act of measuring.

There is a significant difference between Going Full Circle and Going Full Cycle with regard to the maturity of Data Governance. The former leaves the enterprise back where it started with no overall improvement in the maturity. The latter, on the other hand, means that the enterprise can loop through many cycles of improvement, with the end of each cycle leaving the data governance at a more mature level than it was at the beginning of that cycle.

I suppose one could say that there is a big difference between going round in circles and cycling into the future!

Regards,

John