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By Dr. John P. Kotter
Our ability to find, create, manipulate, and use data has grown hugely every decade for at least half a century. Looking to the future, pronouncements of more exponential growth in the numeric tidal wave are often discussed by business thought leaders with an almost giddy enthusiasm. But today, already some evidence shows that too much reliance on increasingly abundant numbers is driving only incremental growth, not the leaps in strategic creativity and genius that creates markets and keeps organizations in positions of industry leadership. And the fundamental reason data can move from friend to foe is not clearly recognized or capable leaders would have already dealt with the problem.
Question: So what are they missing?
Answer: The impact of human hardwiring.
Let me explain.
When humans emerged on this planet many, many millennium ago, they were far from the largest, fastest, or most ferocious creatures around. Yet, despite many predators, ice ages, poisonous plants, famine and more, they managed to survive – in part, say the brain scientists, because they developed a very powerful mechanism that helped them spot threats and act very quickly to eliminate those threats. You might think of this mechanism as a hardwired Survive Channel.
From what I have observed studying people, the Survive Channel includes something like a very powerful radar in our brains that operates whenever we are awake, and quite possibly at a reduced level as we sleep. When the radar picks up something it sees as a threat, much happens very quickly. Chemicals shoot out and increase blood flow to certain parts of our bodies, tighten muscles, and prepare us for “flight or fight”. A number of “negative” emotions are triggered instantaneously: fear, anxiety, anger (directed at the source of the threat), perhaps even shame (“how did I get myself into this situation?”). These emotions can act like a dose of freezing water; they certainly get our attention yet without sending us into panic. Our minds quickly focus on the perceived threat. All other thoughts can disappear. Even our perceptual field of vision tends to shrink greatly. And we don’t have to tell ourselves to do any of this. It just happens because it’s hardwired into our bodies.
When all is working well, our minds go into very rapid problem solving mode. With energy exploding, we then follow through on the action that seems sensible, whether it is to climb a tree impossibly fast to avoid some predator or to work impossibly long days to somehow correct a big quality problem in one of our plants or the service problem with an important customer. We eliminate the threat, the chemicals, muscle tension, emotions, and the like reduce back to normal, and perhaps we sleep most of the weekend.
This appears to be a very powerful system which has evolved over a very long period of time—starting, I am told, well before humans came on the scene. Survive tactics are hardwired at the cellular level, which takes us back a very long time ago. All of this does not always work perfectly. If you cannot find a solution to a threat, fight and flight impulses may cancel each other and you freeze “like a deer in headlights”. If your radar has never encountered some threat, you may totally miss it and seem exceptionally complacent – which, as I have written about before, can be common in organizations with some history of success. If the radar picks up too many threats, for too long a period of time, you can become stressed out, or exhausted, or have trouble thinking clearly.
The Survive Channel helps explain, to some degree, why we were not eliminated as so many millions of species have been over the centuries. It does not explain why we have grown so much and, more importantly, invented so much that we have gone on to literally rule the Earth. To understand our rise above all other species, we need to look partially to a second channel. It seems to have evolved much, much more recently than the first, and perhaps because of its more recent development and perhaps because it’s not as essential, it’s not nearly as powerful as the first. One might call the second mechanism the Thrive Channel.
The Thrive Channel also can be thought of as also having a radar system. But this one looks for opportunities instead of hazards. When it sees what might be an opportunity, it too alerts emotions and sends off chemicals to muscles. In this case, the emotions tend to be what people would generally call “positive” ones: excitement, pride, passion, the joy of winning, the wonderfulness of loving. These are emotions which can “win over our hearts” and lift us off the ground but not, like Survive, as a rocket to the 40th floor. The chemicals send blood to our muscles, but to push energy up instead of producing the spike in activity seen in the Survive Channel. The mind seems to open up more instead of narrowing both because 1) positive emotions do not create the instant and narrow focus that fear does, and 2) because opportunities are so often less clear, forcing you to think and clarify exactly what they are. This cognitive piece of Thrive moves, on average, slower than the thinking part of Survive. It also seems to exhibit curiosity and a capacity to contemplate.
Action flows from this instinct. You try to take initial, or further, steps to capitalize on the opportunity. You do this having increased your pace to a light jog, not a sprint. If progress seems to be made toward the opportunity, positive emotions will remain high. The increased energy level becomes sustainable, which is important because capturing big opportunities may take months or years. Also aiding sustainability is the fact that the body seems to like the hormones that Thrive triggers and, having tasted them, will work to maintain the flow over time.
The two channels are very dynamic in the sense that both can be operating at the same time, or cycling back and forth. They may operate effectively or not effectively. Both might be responding to multiple threats or opportunities. The total activity level can probably go from very light (little anxiety or excitement, few chemicals going out, etc.) to very heavy (a great deal of emotion, a fire hose of chemicals, and so on). And the two channels affect each other, most obviously when a screamingly loud Survive essentially overwhelms any Thrive (much more on that later). Add up all the combinations and permutations and it can appear that we have more than a dozen channels or mechanisms like them driving our behavior. But the beauty of this perspective is that you can trace so much back to just two, which can best be thought of as working together as one system.
Data is essential to running organizations: putting together any rational plan; knowing whether you are operating on-plan or off; keeping things under control; finding and responding to immediate problems. As is often said, “you can’t manage what you can’t measure”. Data has also emerged as a great potential asset in inventing the future – especially going beyond traditional numeric strategy exercises. We are told it can help us speculate intelligently about customer needs in new ways, even inventing entirely new ways of serving those needs. Then it might guide the allocation of resources to create a prospering future. AI and machine learning might give us powers to thrive and dominate that are totally new. Some scenarios sound more like science fiction than others, but overall it definitely can sound exhilarating.
The question is not: is data good or bad? Not enough data can be very bad. We can probably all think of a situation where a mid-level manager is being pressed to make a decision without relevant data and inevitably does not handle the situation well, hurts his or her unit, and may end up mad or demoralized. Conversely, we have all at least heard of examples where data mining has helped firms offer a new and better web product to advertisers, where they can target the right customers in the right way at the right time to greatly increase sales or sales per ad dollars spent. Or where sophisticated AI sitting on big data bases can increase your capacity to forecast, and thus to plan. Or to better segment your customers and thus help use marketing and distributions dollars more effectively. Or perhaps, even reach the science fiction vision of self-driving cars.
The problem with data is that, in general, an increasing volume is often colliding with our hardwired nature to over-activate Survive, crush Thrive, and produce a myriad of problems associated with slow strategic action, a lack of innovation, only incremental improvements in products and services, too little growth – and sometimes an inability even to act efficiently and reliably in the short term. And this includes many instances where data is thought to be used in the service of driving Thrive, but in fact only overheats Survive.
And we are generally oblivious to either the problem, or the cause, or both.
Many, many factors can contribute to an over-heated Survive. Most of these forces are already recognized as a problem, though rarely if ever seen as a problem due to human hardwiring. For example, it is relatively easy to see many situations where a boss activates too much Survive in subordinates. He brings them into a meeting and by design or accident is seen as too threatening in light of the nature of the issues involved. Or he fires a person who is thought of as a good employee, leaving everyone worried that they too are vulnerable. Or he sends his attack-dog assistant to threaten or intimidate others. But we already see this problem, through a slightly different lens, and try to eliminate it. “Not a great manager”, we say, “he needs a coach, a lecture, or to be replaced”. For the most part, this is not a controversial issue.
The same might be said for bureaucratically choking policies. People don’t want to get into trouble (threat!) by violating them, but the policies are sometimes so abundant that they drive people (fear) to be risk averse in the extreme. Extreme risk aversion today slows you down, kills innovation, and is generally a big problem. So bureaucratically choking policies must be dealt with. No controversy there.
Or consider a culture that is too status oriented and overvalues hierarchy. People don’t fight it (fear) even though not doing so can hurt performance. But in this case, and almost all others, there are libraries of books recognizing and helping us to work on the culture or boss or bureaucracy problem. But data?
Data is almost always seen as good, essential, and even more promising now with artificial intelligence and big data. Yet virtually all spreadsheets, monthly budget updates, customer satisfaction surveys, employee engagement or morale or attitude surveys, dashboards, project management updates, market research studies, industry statistics, strategic analyses – you name it – bring some news that the brain can view as threatening. Threatening because they reveal we are not entirely on plan, because a number is not as good as in the last report, because something is less than the boss has said he expects, because possible futures for services fall below our hopes, because it is plausible in our projection into the future that things would be less than, off plan, unprofitable, hurting my division or department and on and on. Threatening because these new statistics on how we should radically change how we advertise, or automate the plant, are going to upset many people and silos in our company and produce who knows what potentially difficult problems for us (not the least of which is killing us as the messenger). Threatening to this year’s performance evaluation, or bonus, or salary increase, or promotion, or career. The possibilities are endless. Too much threat then overheats Survive, which can make it less effective and can slow down or literally shut down Thrive.
How often does this sort of data driven threat assault managers? Do some simple math. In email attachments, old fashioned paper reports, PowerPoint decks in meetings, in the form of customer surveys, financial updates, project timeline updates, reports from regulatory agencies, sales forecasts created by AI and big data – the list really is endless – a manager could easily be hit with a minimum of three sets of reports per week, all containing a minimum of 200-1000 numbers. At the low end, 200 times 3 times 4 weeks a month equals 2,400 potential pieces of bad news a month. In a good organization 90% of the news is actually good, showing that we are on target today or that we have new opportunities in the future. But the remaining 10% leaves us with 240 threats! And this does not take into account your ability to manipulate reports to look at multiple scenarios. When you consider that, the 240 can triple, or much more. And that could easily be a minimum.
You also find a feedback loop that happens all the time where a threat brought to our attention by data leads people to set new goals, add new measures, and generate new reports to make sure we achieve those goals, thus increasing the number of gaps, problems, and thus new threats that hit us weekly. It easily adds up to a 1,000 threats a month to send us into fight or flight, create anxiety, keep us thinking narrowly here, then over there, then all the way over there. Our inability to focus impairs how we eliminate important short term threats. And all the exhausting focus on the short term threats means human Thrive channels, and by extension organizational Thrive channels, are compromised if not totally crushed.
Even data-driven activities that are designed to help organizations thrive in the future can add to the general problem. Strategic planning, as it is practiced in most places today, is a highly numeric, data driven exercise. Strategic consultants have created the standard which has been increasingly adopted by organizations everywhere. At its best, the planning exercise will identify incremental opportunities. Wildly creative, big market opportunities are more often developed by entrepreneurs who never “discover” the big idea as a result of a labor intensive data gathering and analysis process, summarized eventually in a PowerPoint deck of 100 slides or more. In standard strategic planning exercises, data often helps set off Survive and ironically limits Thrive in two ways. In the first, the PowerPoint deck alone is so hard to comprehend that it triggers Survive channels in all but the most numerically sophisticated executives, set off because the brain thinks “if I show I don’t understand I am in trouble with my bosses” or “how am I going to ever be able to explain this to my people”. In the second case, detailed and numerical strategic plans are translated into more and more measures, which assault managers on a regular basis, which always carry some bad news, which adds to the Survive activation, which…
People will often say that we are rational animals. We have learned the usefulness of data. And since we want to maximize on dimensions of importance, we must have the data and we can handle it sensibly. Look at people in a typical data-rich meeting. They may not be happy with all the news. Yet they need it, know they need it, and they are professionals. But this seemingly reasonable viewpoint does not take into account our actual hardwired nature. We don’t see the problem because of how we were raised as children (to keep our emotions under control), because of how we train managers (data is good, emotion in the workplace is usually inappropriate), because of the nature of professional norms (which stress rationality), and other factors – all of which keep our emotional appearances out of sight and under control. But none of that changes how our hardwired channels work.
If these were not enough problems, the recent exponential explosion of data and the increasingly sophisticated analysts who do the grunt work with the data has created a data fluency gap between lower levels and middle and executive ranks in organizations. So managers ask for analyses without understanding the work involved or the limitations of what they are receiving back. The work puts stress on the young analysts, kicking in Survive. The low data fluency means the middle doesn’t fully understand the limitations of what they are given, leading to less than ideal decisions. The poor decisions always eventually come back as problems which sets off threat radar and Survive channels among management, which can be translated into threats and pressures put on their people, kicking off more Survive in the organization.
And the fact that virtually no one recognizes data as a problem in all these ways, or talks about this as a serious issue, is the biggest problem of all.
There are thousands of books on data. Any discussion of volume probably talks about how we might benefit from more of it – to keep costs or inventories or projects or whatever on time and budget. Or to harness the great potential of having even more of it as a competitive weapon.
“Look at how Facebook and Google are reinventing advertising,” we frequently hear. It would not be possible without a quantity of data that is incomprehensible and without very sophisticated AI that can find patterns in those numbers. Look at what Amazon is doing in shipping alone. Start with data on tens of thousands of products, precise to-the-minute inventory numbers in dozens and dozens of locations, along with multiple transportation possibilities and costs. Work over this in an instant with programs that would make NASA proud. Deal with the dynamics of who knows how many customers asking interrelated questions at the same second. Make calculations and send back answers that are almost always correct about timing, and at costs acceptable to the firm and its customers. This is not good. It’s FANTASTIC.
Yes, a few thought leaders have argued that we generate too much data. But the criticism there is almost always about efficiency or cost. We are wasting money gathering and sorting and sending and discussing. Another argument is that we are focusing too much on the wrong data. But there the issue is about causality. I say A leads to B which then goes to C, where C might be profitability. So I say we must obviously be monitoring A and B. You say no, D leads to E leads to C, and measuring A and B can get us into trouble. Both of these points of view – 1) too much data costs us money (or time to discuss it) and 2) wrong data leads to poor decisions – may have some validity, though our most common solution is not very helpful. In the first case, more often than not there is some constituency that benefits from creating the extra data and can make a rational argument as to why it is needed. So we say nothing or back off. In the second case we usually settle arguments about which data is most relevant by including everything anyone wants. The point is that neither of these perspectives – which are occasionally seen as issues – address, much less solve, the massive problem we are suggesting here.
Meanwhile, the real data issue, which will only grow, is not even discussed, much less solved.
Our predecessors, probably driven by their Survive channels, discovered centuries ago that numbers could help them reduce the threat of organizational calamity. Simple budget variance reports, they found, were enormously useful in helping to eliminate potentially lethal surprises. With the larger and larger organizations created by the second wave of industrialization in the early 20th century, they found (or their Survive channels drove them to find) many more numbers to allow people in senior positions to know what was going on in ten or thirty product lines, not just two, in ten or forty countries, not just four. The numbers available to help began to explode at mid-century as computers could manipulate and analyze figures in ways impractically expensive before. Then came both more powerful and less expensive computers along with software, like VisiCalc and Lotus and Excel, which could produce reports almost impossible to create before. We could suddenly look at not just two or three future scenarios but dozens. Then personal computers empowered not just corporate staff groups but masses to create ever more numerical analyses and reports and projections – resulting in not just more data, but more people seeing the data. And now we have the infinite internet to collect information on your customers and the like along with massive storage devices to give us endless numbers to analyze.
I have seen no informed estimates of the sheer volume of data-assault after WWII versus today. But do some estimates yourself. The increase is staggeringly, almost incomprehensibly large. One recent estimate says we have generated more data in the last three years than had been generated in our entire human history – more in three years than in countless millenniums.
And with a faster-moving world, the number of new projects and initiatives launched by businesses has also exploded, bringing with it more activities to be tracked and managed. Project management organizations are report factories. And there is no reason to believe the world will slow down and this trend will stop, especially since it is being fed by so many forces coming at you from so many directions.
Since the mid-20th century the management consulting industry has also exploded. Consultants are PowerPoint deck factories. In large organizations, it is not unusual for top management to have no clue how many consultants are working on how many problems and shooting how much data at executives, managers, and staff.
And do not forget educational institutions. MBA programs are training more and more people in quantitative methods. Once on the job after graduation, they create more data, seek more data, communicate more data. It is what they have been taught.
With small and inexpensive computers, tablets, and smartphones, not even the factory floor is removed from this data deluge. There are manufacturing plants where workers are being confronted hourly with data that might be good news or bad. If bad, and the problem cannot be quickly and easily fixed, this bounces up to supervisors who are now being hit by threats from above and below.
And on the horizon is the hardware and software generally referred to as big data which could take us to another level – where more people spend more time creating more reports sent to more staff and managers. Yes, one possibility is that AI will combine with big data to sort through the masses of information and just answer key questions for us. It is possible that machine learning will create new products for customers and actually reduce data deluge. We would get less but more powerfully useful information. We all see examples of this constantly if only from limited sources (e.g. Amazon, Uber). Since, we have been taught and re-taught as college economic majors or STEM specialists that data is our friend, is modern, is essential. This upbeat possibility seems logical, possible, actually quite obvious.
But why is it not a more logical possibility that a zillion computers and tablets and smartphones with access to this AI could make matters worse? Why should we not expect an uncountable number of people asking and re-asking the AI to run this scenario and that scenario, to look for this and that problem or opportunity, and to flood us with more reports that highlight opportunities but also terrible threats – where we find a possibility to disrupt our own business, before someone in Silicon Valley does, but those studying the data cannot possibly see how to practically make this happen without huge short term economic losses or vicious internal political battles? If we are suffering now, why would we ever expect that we will not be even more unequipped biologically to handle this bulging tidal wave? Not because we are not smart or quantitative enough, not because technology does not offer us new interesting possibilities, but because we all have brains which, beyond a point, can turn numbers into an overheated Survive channel beast? Why should we expect that with raging Survive we would ever calmly and creatively look for patterns in large data sets – patterns that help us to truly innovate? Why not expect more of a narrow Survive-driven focus to identify and eliminate perceived threats? Wouldn’t this, at a point, limit Thrives’ capacity to think more broadly, boldly, and strategically?
Ultimately we need to begin a very big and probably controversial discussion that is not happening now.
While we sort this out, we have found that with the simple Survive/Thrive idea, you can use common sense to diagnosis more accurately a wide range of data problems. You can also start developing personal habits and organizational discipline to avoid those problems in the first place.
An example. You are the CEO of $1B business. To help you deal with the data problem you develop some new practices. First, whenever you receive data documents –live in meetings, in paper, in the mail, as email attachments – you pause before you look at them and ask a simple question: why am I receiving this?
There are many possible answers, most of which may lead you to not even look at the document or spreadsheet. A not uncommon answer: because we have done this for years – since before you got into your job – and for reasons that have no rationale today. For example, a computer glitch which was not caught fast enough once cost the firm $50M along with bad publicity. So, new controls were installed including a report which went weekly all the way to top management. But now it is twenty years later, technology has evolved two full generations, so the probability of that type of problem has gone down radically. The cost of such a problem is also much less. But the report lives on. And it goes into such over sufficient detail that all measured indices never give a perfect score (threat!). So why look at it? Why have it?
Another common reason: someone incorrectly assumes that you want the data or need it. Perhaps new regulations are passed and three levels below you the belief is that important new regulations mean new risks and controls, thus new reports that the big boss needs/wants. But, in reality, there is no reason for you to look at the report. That is someone else’s job – someone one or two levels below you.
Or: you receive 100 page decks at top management briefings because someone is trying to show off (e.g. consultants, insecure subordinates). Or because someone knows, at some level, that he needs to justify the huge expense in cloud storage, AI, and the staff to deal with all this. Maybe there is data buried in the 100 slides that you need to see. But why are you and your team not going there immediately and scrapping the other 98% of the numbers?
More good questions: if the data should not be going to me, where should it go? Anywhere? If somewhere, should all of it go?
And then there are questions about frequency, which is directly related to volume per period of time. This report that is coming to me or others, and is clearly needed, comes at what frequency? Is that the right frequency? Why do we look at these numbers virtually every time my executive committee meets? Why is it sent out weekly? Do we really need to see it more than quarterly?
These sorts of questions are relevant everywhere, including for dashboards that are tracking Thrive-like strategic initiatives. These dashboards are increasingly common in organizations as the number of important and timely strategic initiatives goes up. But does the dashboard, or much of it, help, or does it actually create Survive actions which start to undermine your business-building strategic activities? Imagine 30 measures and only 5 of which are color-coded red. This could be a very good report. But what if the brain pretty much ignores the 25 yellow and green items and drills into the five red. What then happens to the way we think and feel and act when the red marks are just more drip-drip-drips that over-activate the Survive side? What happens to passionate talk about opportunities? What happens to thinking about what else can be done to move some longer-term strategic initiative along faster, or how to use big data the way that it can ideally be used in some businesses today?
Again, the point here is not that data is inherently harmful to activating Thrive. It can be useful, but not using the habits and conventions found nearly everywhere today.
You can start asking these sorts of questions tomorrow and start having a personal habit developed in a month. Better yet, although it will take longer, is to develop the organizational discipline for everyone to ask these questions. It is hard to imagine what that would do – especially if you did it before your key competitors.
We know that one of the reasons incredibly capable people do not lead complex change well goes beyond Thrive being crushed by Survive. Even in well-managed organizations where the Survive impulse works well to do its job, our training, business culture, and organizational systems lead us to use overuse data to drive Thrive-like activities. But data, and yet more data, does not create aligned, passionate focus on big opportunities. Quite the opposite, it can make successful design and execution of bold corporate strategies, big IT system advances, and innovation efforts more difficult. And it can make sustaining the successes in those initiatives even harder. Not always, but…
It is possible to use data to help mobilize the Thrive side of our brains. But that is not the way numbers are typically used. People who are passionate about some bold numeric goal for making an organization better, faster, and smarter can monitor progress in a way that keeps energy and excitement up. Suppose, for example, the goal is an unprecedented $20M increase in revenues for 8 straight quarters – nearly doubling the size of the company – by jumping on a new market opportunity faster than the competition. People are passionate about this. Progress could be reported weekly, and to large numbers of people, not just the executive committee. But instead of a complex dashboard, or multiple reports on various aspects of this initiative, what about two numbers: the quarterly goal, and the current status relative to the goal? You also send relevant pictures or stories that give the same information, but more viscerally. Yes, the case where three weeks of zero increases may take people toward Survive, but zero for three is unlikely. And it is probably good if Survive is activated if that unlikely outcome is reality because there could be a very real problem. Once the problem is solved, under those circumstances, the brain will most probably slide back to Thrive, if you help it.
Use your imagination as the world flies by you daily and much more will occur to you: where data can help activate Thrive, and where data will most probably undermine Thrive and just add to an overwrought Survive. Just pay attention and ask logical and straightforward questions.
Such as: what do you do now to try to activate Thrive that is not just a numbers driven strategic analysis? Or a detailed and complex numbers driven “plan”? I have seen discussions of opportunities, serious and often, help. If you could look at the ratio of your talk of opportunities versus data-identified threats, what would that be? In most organizations today the threat/opportunity ratio in conversations could be 20-1, or 50-1. That assertion may be hard to believe, so look at your calendar for the last month. If you attended a meeting about disruptive possibilities in your industry, try to remember the actual discussion. Was it mostly about threats or opportunities? Or opportunities that you simply could not imagine a realistic way to exploit given your business model and current staffing? Was any conversation about innovation possibilities really about threats, threats, threats, and more threats if you don’t pursue the opportunities? Was the meeting a thoughtful, yet exciting analysis of the future, or PowerPoint decks with overwhelming data?
Conversations about opportunities and experiences with sincere, positive emotion is what you need to help activate Thrive. How often does that happen? I am not talking about meaningless or manipulative hype, neither of which actually creates the progress toward real opportunities that will sustain Thrive activity.
Think about the most astonishing business start-ups ever. Think of the most astonishing causes or political leaders of our time (i.e. Mandela, or Martin Luther King Jr.). The point is not to measure yourself against once-in-a-lifetime people. But it is worthwhile to ask: what do these people do to activate Thrive so much without it potentially sedating Survive in bursts of passion? How much was Mandela’s miracle a data driven exercise?
I could go on. But it is just the same idea applied to nearly anything involving data. Then it is developing some new organizational discipline. At one level this is no different than trying to develop some new “best practices” you are convinced would help you. At another level, it is revolutionary.
Imagine the impact if your people could develop the habit of routinely asking a few questions:
—When data comes at them anywhere or time, they ask: “Do I really need this? Really? Why?”
Data comes at us all the time for reasons that are flimsy at best. Habit. Because of a problem from a decade or more ago. Because subordinates, peers, or consultants are (unconsciously) showing off or trying to justify their worth. Because it is available, maybe because we pay for it and therefore we should use it.
—If yes, they really do need it – they ask: “Do I need all of it? Why do I need all of it?”
People have been trained to provide totally comprehensive data. So if they are asked questions, the answers are probably there, somewhere in the presentation. Since the number of questions smart people might ask are often endless, so is the data presentation.
—If yes, they need some or all of it, they ask: “With what frequency (if it is an ongoing report of some sort)? Weekly? Monthly? Quarterly?”
This is a question people do ask. But all the time? And do they occasionally revisit the question as circumstances change?
—If no, they should not be getting any of this data, “Who should be getting the info? Anyone? If anyone, are they getting it, and the right amount at the right frequency?”
I have seen more than a few cases where the answer here is that the report should eliminated. The presentation should not take place (nor all the work done to create the presentation). Or, sorting this out is not your job. You pay someone reporting to you to be accountable for that task.
—When people reflexively start asking for new data, others learn to (politely) ask: “Do we really need it? Really? Why?”
You might be surprised at how you have developed a habit over the years, or more likely have been taught a habit, of automatically expecting and asking for data as you measure the present or look into the future. And habits are habits: we often do not notice or think about them.
—If it’s a new routine for regularly getting a report or a spreadsheet or a dashboard or a budget update, and people are convinced you need it, “What other reports can I discard?”
Rule of thumb: accept one new data report and get rid of one old. Don’t just add to the pile.
Imagine institutionalizing this perspective and discipline throughout your organization. That would require two steps: cleansing the current system out of what are essentially bad data habits and routines. Then, developing a culture which routinely has people raising the sorts of questions I just presented above. If not all, much of the tidal wave of influences that overheat Survive behavior and undercut Thrive behavior could be removed. And the data problem would not continue to grow as it surely will for most of the world, including your competitors. The competitive benefits for you could be incredible.
With this sort of discipline, there is no reason to believe that the true benefits of big data and AI and machine learning will be lost. To the contrary, using these new technological possibilities well will require some complex changes, as does the installation of any sort of new technology. Strategic initiatives that succeed with that sort of change are always driven by high powered Thrive behavior. The sort of discipline I am discussing here makes Thrive behavior more possible.
Yes, you may have to fight your own corporate culture of “That’s Not How We Do It Here.” But unless you are drowning in cash, you have any number of powerful weapons on your side to create the sense of urgency needed to launch cultural change. You have the always legitimate questions about the unnecessary cost or inefficiency of too much useless data. You have the opportunity argument: do it before the competition and the benefits could be huge.
And you do have power over your own consultants. Retrain them if they understand all this, or get rid of them if they simply contribute to the problem. And your new MBAs are nimble enough to learn to operate in a new way. Or you can hire the ones who are not narrow quants.
And, to repeat again, if all this seems a bit much, keep in mind that your competition is facing the same set of problems. Get out in front of them and you may easily have a whole new advantage. And that advantage will only grow stronger in a faster moving, data-deluged world.
© 2017 Kotter