Sharing Too Much Data With Everybody Doesn’t Help Anybody

I’m afraid that the latest vogue in information sharing– radical transparency, where everyone allegedly knows everything about what’s going on in your business– is one of those virtuous endeavors that starts out with the best of intentions and fairly quickly ends up in tears and a swamp of confusion. It’s just the latest instance of the ancient caution– “be careful what you wish for” — but it’s also something that every business, especially new ones, needs to address before things get completely out of hand, with the inmates demanding to run the entire proceed. Watching Netflix and a host of other companies try to drag back the drawbridge, rewrite their corporate philosophies, and explain why not everybody in the company any longer needs to get a “say” in everything is a case at point. Asking your engineers about art direction is like inviting a turkey to Thanksgiving dinner.

But the issue is much broader than simply one of conflicting politics, busybodies, and concerns about transphobia and cancel culture. To do their own jobs well, there’s no question that your team needs the proper desire, direction, and data. This is a critical component of both innovation and iteration, which are the keys to progress. But sharing critical and sensitive information isn’t an invitation to a free-for-all. People butting in and adding their two cents to the way that everyone else is doing their jobs is a formula for failure and chaos. The fact that they’re just trying to help might be an explanation, but it’s no excuse. The key is to give your people the resources they need and the tools to track how they’re doing and then to get out of their way as long as they stay in their own lanes. If they don’t, your job — among a million others — is to run interference and back off the butt-in-skis.

To help your folks do their jobs and do their best, there’s nothing more essential than timely and relevant metrics. As management guru Peter Drucker said many years ago, you can’t manage what you can’t measure and that’s still mostly true. Even more to the point, it’s clear that what gets measured is what gets done and, in true learning organizations, what gets measured and modified appropriately gets better over time. Constant iteration and successive approximation mean you’re always improving.

But, as with everything else in life, too much of a good thing can simply be too much for an organization to ingest and digest effectively. Bean counting, in and of itself, is never good for business. If you can’t swiftly and successfully integrate the data you’re assembling, it’s just make-work. And frankly, even in today’s hyper-technical world, there are plenty of important but intangible concerns and considerations that you still can’t simply measure.

Unfortunately, when there’s constant pressure for results and “accountability,” there’s often a tendency to invent and massage the facts and figures so that the numbers add up. In far too many businesses, a slavish allegiance to budgets and projections and a fetish with false precision and made-up metrics can lead to disaster. When the measurement and the process itself become the goal, you can easily lose sight of the real objectives. If you insist on overdoing it, the very act of measurement will alter whatever it is that you’re trying to measure and, most often, not in a good way.

Examples of this kind of make-believe management reporting abound in marketing. While direct mail marketing is completely quantifiable, it’s clear that the impact and results of most brand marketing is, at best, a touchy-feely guess. In areas like these the best plan isn’t even precision guesswork; it’s fencing in the parameters within reason– taking your best shot at a realistic estimate and moving on. After the fact, when you have some actual numbers and results, you can fine tune your approach and strategy.

Ultimately, the real job is pretty simple. You need to decide who really needs to get what kind of information to do their best work and then make sure they get what they need. Spoiler alert: practically no one in the entire company needs to know what everyone else earns. Compensation issues, competitive comparisons, and constant complaints are the personnel problems that have sunk more startups than just about any other matter. Whatever the alleged benefit might be of widely sharing sensitive and highly personal material like pay or performance rankings, I assure you that the pain is never worth the hoped-for gain.

So, how do you determine who really needs to know what? Four simple questions.

1) Is the requested information available and readily accessible?

As noted above, for example, impact and effectiveness data for brand marketing is easy to come by, but it’s mostly the product of wishful thinking. Call it “anecdata,” an intoxicating cocktail of facts and factoids. While it may make people feel better, the data adds little to their future performance or results. Understand, too, that the right data may inform ongoing decision-making, but it’s not going to ultimately make the correct choices for your people. The final call and the responsibility are theirs; using the data as a crutch for their decisions is like the drunk using a lamppost for support rather than illumination.

2) Do they need the specific requested information to better do their jobs?

Even if it’s good information, you still need to know the difference between nice (or interesting) to have and need to have. Everyone likes to keep score. Showrunners constantly complaining that the streaming services don’t tell them how their shows are performing until the decision to renew comes up. But telling them after production is complete about some random and relative numbers has nothing to do with the progress, quality or success of the next show they’re working on. It’s more likely to create anxiety and anger rather than any changed or improved behavior.

3) Can and will the team members use it effectively if it’s provided in a timely fashion?

Here again, the writers and teams that assemble 8-to -12 shows for Netflix or Hulu typically deliver the finished series before even the first episode or three-pack airs. So, telling them how specific episodes performed may make them feel better or worse, but it’s not information they can use to revise their completed work. Makers in Hollywood are still treated mainly like mushrooms and management sees no reason to think about changing the rules.

4) Can the data be assembled and provided at a reasonable cost?

Good, clean data isn’t cheap. It’s essential to determine whether the likely benefits will outweigh the costs before you start down the path because–much like rabbits –both the demand and the dimensions of the undertaking will multiply over a relatively short timeframe since the desire for more and better guidance is perpetually progressive and relatively insatiable.

Nielsen used to track home TV viewership and even as the only game in town its reports were accepted by the industry, reasonably priced, and fairly valued for the actual quality of the guidance they provided. But then the world changed in two critical ways: (1) a scrappy new competitor, Comscore entered the market with more advanced and precise measurement technology across multiple media delivery platforms; and (2) the media marketplace fragmented and exploded with viewership in and out of home on cable, digital, desktop, and mobile delivery systems.

Today, there are twice as many smart phones in the average American home as TV sets and each one constantly consumes media. Both the challenges of capturing accurate usage and viewership data across an ever-expanding spectrum and the users’ costs of acquiring such data continue to grow exponentially.

Every use case in every industry is going to have different data needs that will also change regularly, but never diminish. No one is likely to get the parameters exactly right and make the best choices on a consistent basis, but the critical conversations and the time-sensitive decisions are unavoidable and imminent.

All we know for absolutely certain is that data is the oil of the digital age and that the volume of the data being created and aggregated will grow exponentially forever. Each organization will need to develop strategies and firm but flexible guidelines for its information policies.

It’s important to have data, but it’s infinitely more important to know how to employ and interpret it. Having more data is not the same as having better information, even if your people want it all.

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