It’s time to bring the “dashboard” into the digital age

Do you use dashboards to run your business? Dashboards are a major improvement over traditional reports. Instead of presenting rows and columns of incomprehensible data, dashboards provide a simple view of key indicators and their status. What could be better?

While there is a lot of hype around dashboards, they aren’t that new. Business dashboards have been around for decades. What is new is the way that current technology has made dashboards more effective. Today’s dashboards can provide real-time data down to the individual transaction. That’s a game changer.  Dashboards also have a lot more data supporting them allowing leaders to dig deeper and more specifically to answer their questions.

Despite the advances made in the technology underlying dashboards, one thing hasn’t changed much – their layout. Many are still modelled after their analog predecessors. Think about the archetypical control panel associated with a nuclear reactor or cockpit of a plane. There are hundreds of buttons, lights, and dials scattered across a large panel. In the analog world, this type of layout was necessary. The dials and gauges were physical items. They couldn’t simply be added, removed, or interchanged. They have to be hardwired to the panel. As a result, their layout was static. The human factors engineers of the day probably put considerable thought and research into the optimal placement of the dials and gauges. With that much information, it was important to ensure that the most critical and commonly used gauges were front and center and that the others were grouped according to how an operator might use them. However, given what we now know about attention and the brain’s ability to process information, with all of those dials and gauges even the best-designed dashboard had its limitations.

The digital age provides an opportunity to rethink the layout of dashboards to improve their effectiveness and efficiency. The dials and gauges are now virtual. They can appear and disappear in any way we choose. We no longer have to put every indicator in a static location.

For example, the system can dynamically arrange the various indicators based on user-defined categories such as current status (high risk, medium risk, low risk). Doesn’t it make more sense to have all of the metrics that are currently “high risk” show up in one place instead of forcing the user to search across the entire page to find them? Dynamic layouts also can provide multiple views. For example, there might be a status view a functional view. Maybe there is a view that shows business units by metric and another that shows metrics by business unit. Users don’t have to settle for a static layout. The dashboard can align with specific questions that the user is trying to answer.

In addition, because the dials and gauges are virtual, it is no longer necessary to display them all at once. You don’t need to access the four or five supporting metrics for a high-level metric unless there is an issue. How often do you look at supporting metrics when a main indicator is doing well? Those extra metrics just create distractions and clutter. Instead of putting them all on a single dashboard, you can have drill down dashboards that allow you to look at the detailed metrics in a more focused, purposeful way.

Today, more data is just a mouse-click (or finger tap) away. There is no need to display all of the data that you might need in a static manner when you can now dynamically call up the exact data that you need, when you need it.

Your data is more dynamic than ever. This drives huge improvements in decision-making. It’s time to take the next step. Make your dashboard dynamic as well so that you can fully unleash the power of your data.
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Brad Kolar is an executive consultant, speaker, and author. He can be reached at brad.kolar@kolarassociates.com.

Eight Rules for Rethinking Data

Are you getting crushed under an avalanche of “Big Data”?  Are you finding that you have more information but fewer insights? Do you feel like you spend more time looking at numbers than making sense of them?  If so, you are not alone.

The emergence of “Big Data” has created a lot of “Big Stress”.  You’re told that you are supposed to be using data more effectively.  But, you can’t seem to get out from under the numbers.  You hear stories about the amazing breakthroughs and insights that everyone else seems to be having.  Yet, it’s not working for you.

The problem is that many leaders are still approaching “Big Data” with a small data mindset.  That doesn’t work.  We need a new approach to working with data more effectively and efficiently.  Here are eight simple rules to help you navigate a world of ubiquitous, voluminous, and dynamic data.

Computers ‘crunch’ numbers, people ‘crunch meaning - Data can tell you what was, what is, and even what might be but only a person can determine whether that matters.  If you are spending more time thinking about, discussing, or presenting numbers as opposed to the implication of those numbers you’re missing an opportunity.  Let the computer worry about the numbers.  You need to figure out what they mean.

The more you look at (at one time), the more you miss - The research is clear.  We are horrible multi-taskers.  Your conscious brain can only deal with one thing at a time.  When confronted with more than one thing, your unconscious mind takes over and starts making decisions about what information to ignore, what information to “fill-in”, and what information to distort.  You are more likely to miss an issue on a spreadsheet with 200 cells than you are on one with five cells.  Pare down your reports.  Instead of having a small number of large reports (that try to answer every question), create a large number of small, focused reports.  You’ll miss less and make more progress.

Your boss doesn’t want numbers, he or she wants answers - You don’t manage numbers.  Neither does your boss.  You manage issues, risks, opportunities, people, products, processes, technology, and ideas.  So why do you keep talking to your boss about numbers?  Numbers should support the conversation, not be the conversation.

The story in the data is always incomplete - A report is like a window in your house.  It lets you see a portion of your world. You wouldn’t generalize about the day’s weather or the amount of rush hour traffic solely based on what you see out of your window.  So stop doing that with your reports.  A report is a discrete snapshot of your business.  It’s your job to understand how that snapshot informs the broader story of your business.  The story is not in the data, the data is part of the story.

Your boss needs a prosecutor, not a mystery writer - Mystery writers wait until the end of their novels to reveal the answer.  That’s a fantastic technique for creating suspense and entertaining.  Your boss doesn’t want to be kept in suspense or entertained.  Neither do your peers, your customers, your team, or your business partners.  Become a prosecutor.  Make your argument and then lay out your case (with data).  It will drive more effective decision-making, and more importantly, it will give people what they wanted in the first place, an answer to their question.

Not all of the data that are related to a problem are relevant to the solving problem - Technology allows us to capture a lot more information about something or someone in a much more fluid and dynamic way.  However, just as a doctor doesn’t send you for an MRI to diagnose a cold, you don’t have to gather every conceivable data point on a topic to make a decision.  The first step in reducing the amount of data with which you interact is figuring out which pieces are actually relevant to your decision.

It’s easy to get lost when you don’t have directions - You wouldn’t cross the ocean without a compass, map, and destination to guide your way.  Yet, we constantly wade into a sea of data with no idea of what we are trying to find. It’s hard to find an answer when you don’t have a question.  Stop reading reports like books (top to bottom, left to right).  Instead, guide you data exploration with a question, a hypothesis, or a decision.  Doing so will enable you to navigate your reports much faster and much more effectively.

People don’t read reports for the numbers - When was the last time you looked at report with the question, “I wonder what the value of X is today”?  It’s probably been a while.  However, you probably looked at your reports wondering if you were on target, or if your performance was improving or getting worse.  But ironically, the report doesn’t often tell you that, it just gives the numbers (or maybe a cute color coding).  You have to figure the answer out for yourself by comparing the numbers.  While that’s not a difficult task, it’s unnecessary.  Instead of organizing reports around numbers, why not organize them around decisions.  If you want to know which sales people are beating their targets, have a heading called “Sales people beating their targets” and list them.  You can also have a heading called “Sales people missing their targets” with the appropriate names listed below.  You don’t need all of the numbers.  You just need the answers.  Once you find what you are looking for, you can always drill down to see the specifics.

Working in a world that is awash in data requires finding ways to navigate and cut through the noise.  Adopting these new rules for rethinking data will make you a more efficient and effective data-driven decision maker.

For more help, check out our Rethinking Data workshop where we'll teach you how to apply these rules in your day-to-day work.

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Brad Kolar is an executive consultant, speaker, and author. He can be reached at brad.kolar@kolarassociates.com.

Sometimes the best way to add value is to get out of the way

The other day I bought an iPad.

The entire experience from walking in to walking out took about five minutes.  I wasn’t that surprised. In the past two months, I’ve been involved in the purchase of four products at an Apple store.  All four experiences combined have taken less than thirty minutes.

For other people, the experience at the Apple store may take considerably longer.  I watched as the sales people patiently worked with customers who were unfamiliar with Apple products, weren’t sure about their needs or had questions about how to use their new products.  The sales people didn’t rush the customer and appeared to have all the time in the world for them.

For me, the Apple store provided incredible value.  I had a need and they were able to get it resolved incredibly fast and effectively.  Conversely, for those people who needed more time, the Apple store also provided value.

The true “genius” of the Apple store doesn’t lie in the tech support people.  It’s the sales people.  Apple sales people provide value by understanding and adapting the experience to the needs of their customer.  They don’t put everyone through the same process.  They skip steps that aren’t necessary and add steps that are.

As organizations continue to scrutinize costs, everyone wants to show that they are adding value.  This is especially true for internal, non-customer facing departments.

While such an attitude is productive, it can backfire.  In their quest for “Trusted Advisor” status, people sometimes confuse adding value with doing more stuff.  As a result, they wind up over complicating processes or slowing them down.

For example, in one company, to get a new budget account set up through finance, executives had to have a one-on-one consultation with an analyst from the Finance department.  However, the consultation wasn’t focused on quickly and effectively getting the executive up and running.

The analysts walked the executive through a long process of defining the business case for the new budget account, an audit of current accounts to determine if it would be better to use one of them, and finally a discussion of budget targets for that account.

Ninety-percent of the time, after this discussion, the account that the executive originally wanted was set up, just as the executive requested it.  The only difference is that it took an extra two to three hours of the executive’s time.

Perhaps two to three hours doesn’t seem like a major inconvenience.  However, sometimes those consultative processes can add days or even weeks to the process of meeting the customer’s need without actually improving upon the original request.

Whether business-facing or customer-facing, the best way to add value is to help someone fulfill his or her needs in the most efficient and effective way.  In some cases that might mean clarifying that need or helping the customer think through their solution.  In other cases, it means helping break down the barriers to the customer having his or her need met.

You don’t always have to “improve” the solution in order to add value.  Sometimes, just getting out of the way is the best thing you can do for your customer.

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Brad Kolar is an executive consultant, speaker, and author. He can be reached at brad.kolar@kolarassociates.com.

Leadership Lessons from Holocaust Survivors

Today is Holocaust Remembrance Day.  Here is my annual re-post in memory of the victims and in honor of the survivors.  Leadership begins with respect for all people and a desire to make the world a better place.  Never forget. 

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A leader’s job is to create meaning and purpose for those around him or her.  In his book, “Man’s Search for Meaning”, Viktor Frankl speaks eloquently about the power of such meaning and purpose in a person’s life.  This book should be required reading for all leaders. 

Recently, I was fortunate to hear from a wonderful woman, Cipora Katz.  Cipora is a Holocaust survivor.  She is a woman of tiny stature.  Yet, when she speaks, her spirit fills the room.  Cipora was the fifth survivor that I’ve had the opportunity to hear speak. 

If you haven’t had the opportunity to hear a Survivor speak, I recommend doing so (and doing so quickly as unfortunately, their numbers are thinning).  These are incredible people who in the course of thirty minutes will provide a lifetime of lessons.  Reading their stories or seeing snippets of a video doesn't capture what makes these people so special.  Seeing them in person allows you to see the spark in their eye, hear the conviction in their voice, and feel the burning desire to live that resides in their hearts.

Their stories discuss horrors that many of us couldn’t even imagine yet alone endure.  However, when reflecting on their lives Survivors use words like “fortunate”, or “lucky”.  This must be what Viktor Frankl meant in “Man’s Search For Meaning” when he said that “Man’s search for meaning is the primary motivation in his life and not a ‘secondary rationalization’ of instinctual drives.”  Each of these survivors had a burning, insatiable desire to live.  They understood that their life had purpose even if, at the time, they didn’t know what that purpose might be.

There are five lessons that I took away from their stories.  Each provides a way to summon the spark within us even when there is darkness around us:

The first lesson I learned is that leadership is about how we frame issues.  We cannot always control the things that happen to us.  However, we can control how we frame and react to them.  We can view them as tragedies that disable us or as challenges to which we must step up.  Imagine a ten year old boy being told by his mother that he must run away and fend for himself in order to survive.  Now imagine him actually doing it.  Survivors overcome.

The second lesson is to create opportunities for yourself.  Every survivor’s story seems to contain a combination of determination and luck.  There is an old adage, “I believe in luck.  The harder I work, the luckier I get.”  Survivors got “lucky” partly because they capitalized on things that others missed, ignored, or didn’t have the courage to try.  In one story, a complete stranger approached a mother and child telling them to run after him when he gave them a signal.  Perhaps a twist of fate placed that opportunity before them.  But, it was their internal spark that moved them to act on that opportunity when others might have been too afraid of the risk.  Survivors constantly sought a way past the next hurdle and didn't let an opportunity slip by.

Third, create a purpose for yourself and others – Even today you can tell that each survivor lives life with purpose and meaning.  For some that purpose has changed since their experience in the Holocaust.  But, it is unmistakable.   In the past few years the business world has become filled with advice and articles on “employee engagement”.  Yet, after meeting these survivors, I realize that we don’t really understand what engagement is anymore.  We consider a person who is willing to do a good day’s work for a fair day’s pay as engaged.   Go hear a survivor speak.  Listen to what they say and how they say it.  Watch them.  Analyze how they view (or viewed) the world.  You will leave with a new definition and appreciation of engagement. 

The fourth lesson is about mental agility. Survivors knew which of their expectations to compromise on and which to hold fast.  This allowed them to recalibrate what was "normal" in a world that lost all sense of what was right.  By reframing their expectations they were able to create small successes on a daily basis which gave them the extra energy needed to look ahead to the next day.  Just as today, people who held too tightly to standard definitions had difficulty adapting.  But the Survivors didn't compromise on all of their expectations.  They maintained a clear line on the meaning of humanity, life, and purpose.  Lowering some expectations allowed them to adapt and achieve success, while maintaining the important ones drove their sense of purpose and longevity.

Finally, the Survivors understood their role in a broader community but also relied first and foremost on themselves.  They created their own opportunities.  They didn't wait for a handout.  Yet, many of the most touching stories were of people, who despite their own starvation broke the scrap of bread that they found into as many pieces as possible so that all could share.  This reliance on self integrated with contribution to community provided these people with strength, even when they didn't personally have any left.

Not surprisingly, many of the Survivors that I met and heard from achieved great personal or professional success after the Holocaust.  It wasn't easy.  Many restarted their lives with absolutely nothing.  Their will and passion for life combined with the ingenuity, determination, and ability to overcome adversity must have made navigating the "regular" world somewhat trivial.

No workshop or book will ever provide better lessons than what I learned from listening to these extraordinary people.  Of course, I realize that it wasn't knowledge or skill that enabled these people to do what they did.  It took a spark deep inside of each of them.  You can't build or buy that spark.  But, if you are lucky, perhaps you can capture some of the energy from those who have it.

Are you an effective data consumer?

Companies are scrambling to board the Big Data train.  Most of their efforts seem to be on building their big data production capabilities.  Whether it be transforming their data management processes and tools, hiring “Data Scientists” or enlisting the services of “Big Data” consultants, companies want to squeeze ever bit of insight out of their data.

However, from what I’ve seen, most companies are ignoring a big part of the Big Data equation – the data consumer.  It doesn’t matter how many numbers are crunched if there isn’t anyone around who understands how to use them.  In fact, an increasing number of new articles on Big Data have cropped up that specifically point to the risks of Big Data in the absence of Big Data understanding.

For example, in a February 8 opinion column in Wired magazine, Nicholas Taleb points out that an increased number of data points produces an increased number of spurious correlations:

“I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack.”

In other words, if you toss enough data into a barrel and shake it around, you’ll eventually find some that sticks together completely randomly (e.g., Nate Silver’s illustration of spurious correlations between stock market performance and which teams won the Superbowl.).

Other articles point out that Big Data is increasingly becoming a “black box” where end users don’t understand the underlying assumptions, models, or transformations being made to the data.  In these cases, interpreting the results of such data becomes muddied and can lead to poor decision-making or conclusion.  For instance, in the recent HBR article, Advertising Analytics 2.0, one CEO lamented:

“When I add up the ROIs from each of our silos, the company appears twice as big as it actually is.”

Being able to crunch huge datasets is not enough.  Companies must focus the same or even more effort on helping their people use Big Data.  Being a competent consumer of data requires rethinking three ways in which we work with data and information:

Our brains – understanding and attending to the limitations and cognitive challenges we face when trying to make sense of information

Our approach – shifting from an information-focused approach to a decision driven approach to using data.  This reduces the noise and clutter associated with big data.

Our reports – shifting from providing numbers to providing answers.  There are too many numbers to plow through and our brains can’t process them anyway.  Good reports should have fewer numbers.

In addition to this, the successful data consumer must have a strong understanding of the business or content area in which they are working.  At the end of the day, data still do not tell you what to do.  They tell you what is happening, what things are connected to other things, and sometimes why things are happening.  Acting on that information is predicated on understanding the world in which that information was produced.

Is Big Data Killing Democracy?

NOTE: The point of this entry is to raise the question of how analytics are being used in the political process.  The techniques described and concerns raised apply to all  politicians and all parties. This is not meant to question any specific candidate but rather the system as a whole and the way that it is using data and analytics.

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The 2012 Presidential election will go down in history as a turning point in American politics.  While much attention is placed on the outstanding job that President Obama’s analytics team did, other candidates jumped headlong into the deep end on analytics as well.  In the future, we will see increasing investments of a campaign’s time, people, and money placed on crunching numbers.

Generally speaking this shouldn’t be a problem.  Good analytics give greater insight into customer needs and ideally allow organizations to improve their products and services to meet the needs of those customers.  That should bode well for the American people.

The problem is that in the election analytics weren’t used to create a better product or idea.  Instead, they were used (by both sides) to efficiently and effectively manipulate individual behavior.  In others words, the data weren't used to get the candidate to respond to better to the person.  They were used to get the person to respond better to the candidate.  So what's the big deal?  Isn't the the whole point of marketing?  Candidates have been trying to achieve this since people started being elected to political office.  However, when our understanding of voters and voting behavior was more crude, candidates had to think and focus broadly to win votes in order to hedge their bets.  However, now, we've reached a level of sophistication i understanding and predicting behavior that fundamentally changes the dynamic between candidate and voter.  This creates three problems:

1)      The electorate becomes resources to be optimized in support of the candidate's needs rather than the candiate being a resource to help his or her constiuents.

2)      Candidates can now win (and win much more efficiently) by reaching out to fewer rather than more of their constituents.  In fact, true resource optimization is about reaching the fewest people necessary to win.
3)      Candidates can  more effectively (they’ve always tried) manipulate voters into basing their decisions on things other than the candidate’s policies, abilities, and vision.

In this past election data weren't being used to understand people’s needs in order to formulate policy.  They were being used to manipulate people's attitudes and actions.  As Sasha Issenberg stated in her article, How President Obama’s campaign used big data to rally individual voters:

"The campaign didn’t just know who you were; it knew exactly how it could turn you into the type of person it wanted you to be."[1]

Isn’t that backward?  Our government and our leaders are supposed to be serving our interests.  We are not here to serve theirs.

Advances in accurately predicting voter behavior at the individual level allows candidates to pay attention to fewer and fewer people.  This played out clearly on the election with repeated campaign stops in certain locations and almost no attention played to others.

In politics, uncertainty necessitates being inclusive.  If a candidate doesn't know for sure where someone (or a group) stands or how they will behave, he or she has to try to reach more people.  On the other hand, certainty enables exclusivity.  Candidates now know who is more and less likely to respond to their message and will use their time accordingly.  As a result, candidates will increasingly reach out to people with whom they are already have an ideological connection and will miss the opportunity to hear and take into account opposing views.  In the name of expediency and votes, the emperors of tomorrow may never encounter the people who see that they have no clothes.

For example, suppose a candidate’s team had the following data for a group of 100 voters:

Candidate	% Favorable	% Expected to vote	Expected votes
Candidate A	48%	50%	24
Opponent	45%	60%	27
Undecided	7%	40%	3*

^* Actually, 3.15 but only “whole” people can vote

Candidate A has four choices (in increasing order of difficulty)

·         Try to get four more of his or her supporters to get out and vote

·         Try to win over the three undecided voters who are likely to vote
·    Try to win over and convince enough of the undecided voters who aren't voting

·         Try to win over two of the opponent votes

Reports from the election indicate that the first two options seemed to be the preferred approach.  This approach allows the candidate to exclude over 80% of the other voters since they don't have to worry about those who are already voting for them and they don't need the votes of those who they know are not.

This type of strategy makes sense in business.  Businesses calculate the "lifetime value" of a customer.  This helps them determine how much, if anything, they should invest in acquiring and retaining that customer.  After all, why invest $50 to acquire a customer who is only "worth" $10 to you.  This helps them maximize the use of their resources.  It's good business.

The problem is that the same principle falls apart in politics. Do we really want our political leaders to determine whether to engage us based on how much they believe we are worth to THEM?   Don't they work for us?  The flap over Mitt Romney's statement about not focusing on the people who weren't going to vote for him drew rightful criticism.  However, a large part of President Obama's analytic's success was based on the same premise.  Whether doing it through simple, "old school" policy or a sophisticated analytical model it's wrong.  Unfortunately, the new analytical tools make it easier and more extreme than in the past.

Finally, for those who are targeted, it’s going to be easier to manipulate their behavior. The winners of future elections might not be the people with the best ideas for the nation but rather the ones with the best marketing departments and targeted and tested micro-messages.  Zeynep Tufekci, in his Wall Street Journal Op-ed, Beware the Smart Campaign[2]s states

“What I really worry about, though, is that these new methods are more effective in manipulating people. Social scientists increasingly understand that much of our decision making is irrational and emotional. For example, the Obama campaign used pictures of the president’s family at every opportunity. This was no accident. The campaign field-tested this as early as 2007 through a rigorous randomized experiment, the kind used in clinical trials for medical drugs, and settled on the winning combination of image, message and button placement. I agree that his family is wonderful and his daughters are cute. But an increasing role of “likability” factors, which we now understand better how to manipulate, is not good for democracy.”

This election was won by skillfully and expeditiously manipulating the margins  (and this is not meant to be an indictment against President Obama’s campaign.  Mitt Romney’s campaign tried as well as did all of the other candidates throughout the primaries.  The President’s team just did it better).  As analytics help candidates hone those margins to even greater levels of granularity, what will happen to those of us who are statistically determined to be “in” or “out” and therefore don’t warrant attention.  Will our voices be heard?

I know I’m being naïve but I hope that our politicians will see the power in analytics to isolate the core issues that unify us as a nation instead of continuing down the path of using data to separate us to get a vote.  Personalization is great for businesses and consumers.  But, as the President himself has said many times, we are supposed to be a collective society, not a group of 120 million individual voters, each who received their own special, personalized promise from the government.  That’s not sustainable. A business might be able to personalize a computer or car to a customer's exact specifications.  However, the government can't make 120 million personalized laws and polices.

Big data and analytics provide a tremendous opportunity to provide better products and services to customers in more efficient and effective ways for companies and governments.  However, as with any tool or technology leaders still must challenge themselves to use these tools in morally, ethically, and socially responsible ways.

Brad Kolar is an executive consultant, speaker, and author.  He can be reached at brad.kolar@kolarassociates.com.

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[1] Sasha Issenberg, How President Obama’s campaign used big data to rally individual voters, December 16, 2012 (http://www.technologyreview.com/featuredstory/508836/how-obama-used-big-data-to-rally-voters-part-1/)

[2] Zeynep Tufekci, Beware the Smart Campaign, Wall Street Journal Op-Ed, November 16, 2012 (http://www.nytimes.com/2012/11/17/opinion/beware-the-big-data-campaign.html?_r=0)

Corporate training effectiveness – Optimizing the ‘last mile’

According to ASTD’s 2012 State of the Industry Report, in 2011 US companies spend just over $156 billion on training.  That’s roughly equivalent to New Zealand’s entire GDP for the same year.

The top of mind question for executives is what type of return they are getting for that investment.  There is no question that business executives and L&D organizations want to deliver value through training.  However, many are optimizing the wrong part of the process and are jeopardizing the potential value of their investments.

Companies put a lot of time, money, and attention into the front-end part of the training process – assessment, design, and development.  They invest in rigorous methodologies, development of templates and standards, and implementation of tools to make the process more efficient and effective.  This is all critical for creating a good product.

However, while there is considerable rigor and discipline placed on the production of training, delivery is often treated as an administrative task at best or an afterthought at worst.  Training often takes place in whatever rooms a training coordinator can find (after all of the higher priority meetings are scheduled in the good rooms) whether those rooms are conducive to the training experience or not.  Instructor pools often consist of people who were looking for a career change or in some cases, from whoever was available to teach on a given day (and who had some tangential experience in the subject matter).  In many cases, course content  often gets simplified to make it easier for "anyone" to teach.  Course length is often cut to fit the training into broader agendas or to accommodate travel preferences. The number of participants in a session is often left unchecked under the guise of gaining economies of scale from the costs of each session.  And, as we move into more on-line learning, choices for delivery are often based on the tools or communication infrastructures that were created by software designers and engineers not learning experts.  As a result, genuine interaction is lost and artificial interactions (e.g., ask a question or poll the audience every three minutes) become the standard.

The value and impact of training does not come from the needs assessment or design of the course; it comes from the delivery of the course.  I’m not trying to suggest that assessment and design those are not important.  Having the wrong content or a poor design will reduce the impact of a training experience.  But, value doesn't come from the product, it comes from its use.

As many on-line retailers learned during the early days of eCommerce, having a good product is worthless if you can’t get it delivered properly.  A good design can’t offset a poor instructor, a bad room, or a hurried experience. Yet, a good instructor or engaging environment can easily and effectively counter a poor design.

If you want to start getting the most value from your training, start focusing more on where that value actually occurs.  If your presentation standards are more rigorous than your room, technical platform or faculty standards, you’ll never get the full value of the training experience.

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Brad Kolar is an executive consultant, speaker, and author. For more ideas on how to be an effective leader, visit his blog at www.leaderquest.blogspot.com.

If you don’t have the answers, give them the questions

Lack of answers is a common problem that leaders face during times of change and uncertainty.  It’s not that they don’t want to provide answers.  They just don’t have them.  Like their people, they too are waiting for decisions to be made and solutions communicated.

However, this shouldn’t be a reason to stop communicating with your team.  In fact, this might be the time to communicate even more.

But if you don’t have answers, what is there to talk about?  Isn’t that what your people want?  Not necessarily.  It’s important to remember that during times of change the most important thing is for people to have some way of making sense of what is happening around them.  Anxiety comes when they don’t know what to expect or when to expect it.  Of course, providing answers is the best way to help people regain that sense of control.  But, if you don’t have answers, there are plenty of things you can discuss.

The next best thing after the answer is the questions being discussed to come up with the answer.  The key is to understand where people’s anxieties lie and respond to them.  By helping your team understand what issues are being explored, you help them gain some sense of order.  For example, suppose there is a major re-organization happening.  Your team’s primary concern is probably for their jobs. However, you might not know what jobs they will have in the future.  But you might know that the current discussions are around how to redeploy the current workforce and use managed attrition to meet future requirements.  Tell them that.  While your team doesn’t know exactly what they’ll be doing, they will at least know that their employment is probably secure.

If you don’t know the questions, you might be able to talk about the goals or criteria that are being used for making the decisions.  For example, in the case of the reorganization, perhaps the goal is to improve quality or customer experience as opposed to reducing payroll costs.  This would help your team understand that their job security might not be at risk.  Or, perhaps the goal is a reduction in payroll but the strategy is to figure out how to do that by expanding people’s roles rather than hiring new people.  That would help alleviate your people’s concerns as well.

Finally, in the absence of answers, questions, or criteria, you may be able to explain the process and timeline.  While it won’t provide specific content, at least it will give people an understanding of what to expect and when to expect it.

Of course, none of these can substitute for definitive answers.  However, by providing people with broader information and context you can help alleviate some of their anxiety.

As a leader, your job isn’t to have all of the answers.  Instead, try to understand the concerns that your people have and give them as much information as possible to help them make sense of their situation.

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Brad Kolar is an Executive Consultant, speaker, and author.  He can be reached at brad.kolar@kolarassociates.com.

Separating context from action

There is an old story about a newly married couple who decided to host a dinner at their home for their families.  It was the first time they hosted such an event.  The husband was busy preparing a roast when his grandmother walked in.  The grandmother was puzzled when she watched the man cut about two inches off the end of the roast.  She asked him why he did that.  He responded, “I’m making it just like my mom used to make it.  She said she followed your recipe and that the first step was to cut off the end of the roast.”  The grandmother smiled and said, “Well, that’s how I did it, but it was just because my oven was never big enough to hold the entire thing.”

You’d be surprised at the number of your employees who are mindlessly “cutting off the end of the roast” without really understanding the purpose or reason for their actions.

Too often leaders focus their most of their attention and communication on details and actions (e.g., new processes, policies, or systems) without providing the context for why they are being done in the first place.  While this increases compliance, it often decreases judgment and impact as your employees start to act mindlessly, on auto-pilot.

Good leaders help their people both understand the actions they must take to be successful as well as the context for which those actions are being taken.  In a rapidly changing, ambiguous world, context is increasingly important.  If your people understand WHY they are doing something, they are often more adaptable and can even figure out HOW to do it, even as things change.

A common complaint on employee engagement surveys is a feeling of constant, never-ending change in the organization.  Employees lose confidence in leaders who appear to routinely change course.  Most organizations go through a lot of change.  However, the change is not nearly as dramatic as it appears.  Often the changes are in the mechanics of the work rather in the intended result.

If you want to give your people a sense of stability, frame your conversations around the things that change less frequently.  What is your primary product or service?  How do you make money?  Where do you spend money?  Who are your customers?  Those things probably aren’t changing very much.  What is changing is how you address them.  Keeping the conversation focused on what you do as a business will help your give your people the stability and direction for which they are looking.

The following model provides a simple way to organize your communication.  The top part of the model focuses on the context or the things that change most slowly.  The bottom part of the model focuses on the various activities needed to succeed within that context.  When communicating changes, be sure to start with the context and then link the actions to them.

Context – this is ultimately for what you are striving

• Goal - What are you ultimately trying to achieve?
• Metrics - How will you know and measure whether the goals are being met? What would look different because of this?
• Success Criteria - What must you get right in order to achieve the goal?
• Levers - What does each person control to achieve the Success Criteria? What actions can s/he take to influence the outcome?

Action – the tools for achieving success

• Key principles - What should remain in the back of people’s minds as they pursue this goal? 
• Frameworks and processes - What processes and frameworks exist to support decision making in this area?
• Trade-offs - What are the key trade-offs that must be managed?
• Obstacles- What common obstacles exist that prevent us from reaching this goal? 
• Unlearning - What attitudes or beliefs do we need to change in order to truly meet the goals of this role? 

Success isn’t about simply executing the actions properly.  Success is defined by the context.

Think about your business and what it ultimately provides the customer, how it makes money, and how it works.  That probably hasn’t changed in a long time.  What’s changing is the way that the company is doing those things.
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Brad Kolar is an Executive Consultant, speaker, and author.  He can be reached at brad.kolar@kolarassociates.com.

New Year's resolutions for Leading with Big Data

The year is wrapping up and many people are looking forward to some rest, relaxation, and celebration of the holidays.  It's also a time for reflection as we think about our resolutions for the new year.  2012 was certainly the year of Big Data.  Let's see if we can make 2013 the year of using Big Data effectively.  Here are ten resolutions that will improve your effectiveness at using Big Data:

1. I will focus on communicating a logical, reasonable story about my business and the issues we face.
2. I will use data to SUPPORT my story rather than BE my story (e.g., I'll stop presenting numbers and charts).
3. I will start my presentations with conclusions, interpretations, and recommendations and then lay out my evidence.
4. I will use my bias to generate questions rather than answers (the data will provide the answers).
5. I will not jump into data without a question or hypothesis to guide my thinking.
6. I will move the charts, tables, and graphs to the appendix and instead focus my presentations on the issues and recommendations.
7. I will stop looking at or presenting too much data at once.
8. I will separate the data that is relevant to my decisions from data that is simply related to my decisions.
9. I will organize my reports around answers or decisions rather than just providing information.
10. I will collaborate with others to find meaning in my data.

If you need any help these resolutions, you may want to consider our "Beyond Big Data" workshop for your organization.  Click here for more information: http://www.kolarassociates.com/downloads/BeyondBigDataOverview.pdf.

Why movies don’t start with the credits

What do movie makers, televangelists, advertising executives, infomercial producers, and even high school speech team students have in common?  They all know that the first few moments of an interaction provide the greatest opportunity and the greatest need to grab their audience's attention.  Unfortunately for the rest of us this principle remains elusive in our day to day lives.

Think about your last meeting.  How did it start?  Most likely someone went through the agenda or discussed the logistics for the day.  How many people got excited about that?  What about your last training session or annual retreat?  Probably the same.  I’d bet that in most instances where you get people together, the first thing the audience hears is something administrative and logistical.  In one meeting that I attended recently, the first thing the organizer told us was the location of the restrooms.  It’s no wonder that there is so little engagement in the workplace and in meetings.  Perhaps it’s because we don’t capitalize on opportunities to engage people.

Contrast that with the last movie or television show that you saw.  How did those begin?  They didn’t start with the title, theme song, or “opening” credits.  They generally start with a scene to capture your attention and draw you in.  Then, they take an abrupt break to formally introduce the event.  That’s how they create an engaging experience and pull you in.

I’m not suggesting that the agenda, logistics, and especially the location of the restrooms aren’t important.  From the perspective of Maslow’s hierarchy of needs, if people are concerned about those things, they probably won’t attend to the issues you really want to discuss.  However, just because they are important, you don’t have to address them immediately. There are very few emergencies in the first three to four minutes of a meeting.

Shakespeare once wrote, “All the world’s a stage" (As you like it, Act 1, Scene 7).  Every interaction is an experience.  If you want to engage your audience, capture their attention in the first few moments that you have it.  Then you can sort out the details.

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Brad Kolar is an Executive Consultant, Speaker, and Author.  He can be reached at brad.kolar@kolarassociates.com.

Predicting the past

In honor of Thanksgiving, here is a re-post of an earlier entry on the dangers of historic data.
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The City of New York is considering suing BP for losses to its pension investments. (Reuters, June 24, 2010).  They claim that BP misled them regarding safety procedures that led to the current oil spill.  Perhaps that's the case.  Or, perhaps, as with any investment, they took a risk.  At the time the investments were made, the data predicted a positive return from BP.  But then an unexpected event occurred which changed the results.  Now they want their money back.

Despite all of the advances that have been made in analytics, we still cannot predict the future.  We can do a pretty good job of understanding what's probable, but that's not the same as predicting.  It's important to understand the difference and the implications of that difference.

Many executives fall into the trap of thinking that if they just had the "right" data, they'd be able to make the right decision.  There's no such thing as the "right" data.  There will always be supporting and contradicting data for any decision.  And, the best data we have is only from the past or present.  All we can do is model that and make our best guess as to whether the future will look the same.  And, make no mistake - regardless of how much data we have, when talking about the future we are making a guess.

Often the future doesn't behave in quite the same way as the past.   For example, a company's stock price can vary greatly despite the company turning in similar business performance.  A positive national economic report might drive all stocks up.  In other quarters, there might be an oil spill.  While understanding a company's business performance data is a good start toward understanding what it stock price might do, there are no guarantees.  There is a broader context in which that performance plays out

Sometimes we forget that data don't occur in a vacuum – the context surrounding it matters.  For example, one company performed an extensive ROI analysis on one of its internal departments.  They used the results of that analysis, which were quite positive, to justify and drive a new operating strategy.  However, the new strategy fundamentally sought to change the very operating model that had driven the ROI data. Predictive models are good, but they are based on past conditions.  Changing context will also change results.

In The Black Swan, Nicholas Taleb provides a striking illustration of this:

"Consider a turkey that is fed every day.  Every single feeding will firm up the bird's belief that it is the general rule of life to be fed every day by friendly members of the human race 'looking out for its best interests,' as a politician would say.  On the afternoon before the Wednesday of Thanksgiving, something unexpected will happen to the turkey.  It will incur a revision of belief." (p. 41)

Taleb uses the term "learning backward" in describing this line of thinking.  He argues that the thousand days of historic data (of the Turkey being safely fed) actually provide a negative value.

"Consider that the feeling of safety reaches its maximum when the risk is the highest.  But the problem is more general than that; it strikes at the nature of empirical knowledge itself.   Something has worked in the past until – well, it unexpectedly no longer does, and what we've learned from the past turns out to be at best irrelevant or false, at worst misleading" (p. 42)

In this case a simple understanding of Thanksgiving is much more useful than the thousand days of data.  While our world is a bit more complex than a turkey's, we often fall into the same trap. I'd bet that the New York Pension Fund's feeling of safety (for their investment) increased proportionally with BP's exploration and drilling activities.

I'm not suggesting that we abandon all data and pull out the Ouija board to make decisions.  Having solid facts and data is the foundation to good decision making.  However, it's just a foundation.  We still need to use our brains, our experience, and yes, even our gut.  When we combine those things with data, we are more likely to make a good decision.

At the same time, we also have to acknowledge the limits of data.  Too many executives get stuck in the illusion that if they just find the right set of data, they will make perfect decisions.  In the midst of that search, they wind up making no decisions or are surprised and unprepared when their decisions don't turn out as expected.

If the data are accurate and reasonable (not just to you but to others who understand the context of what that data is describing) they are probably sufficient.  It's then time to make some judgment calls and move forward.

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Brad Kolar is the President of Kolar Associates, a leadership consulting and workforce productivity consulting firm.  He can be reached at brad.kolar@kolarassociates.com.

Data didn’t win the U.S. elections, decisions did

It’s hard to read a newsfeed since the U.S. elections without reading about big data and analytics.  Nate Silver has established (or further established) himself as the king of all things data.  Obama used it well, Romney didn’t.  Case studies will be written for years about the impact that big data had on this election.  But, I think it’s important to step back.  Data did not win the election, decisions did.

It doesn’t matter how much data you have if you can’t quickly and effective transform it into decisions and actions.  It’s true that without good data, you can’t make good decisions so I’m not trying to minimize the importance of data.  However, data are inanimate.  Data don’t “do” anything other than sit there.  It takes a leader, using his or her judgment, experience, and understanding to turn that data into action.  Yet, all too often, it seems like leaders are waiting for the data to make the decision for them.  If they can’t make a decision, get more data.  If they don’t like someone else’s decision, get more data. If they don’t know what to do with the new data they get, get more data.

More data won’t solve your problems.  Data only provide you with facts.  And, as Nate Silver points out in his book, “The Signal and the Noise”, sometimes those facts can be misleading if they aren’t viewed within the right context.

Move past the numbers.  Let them show you what is true and what isn’t true (or to borrow an idea from Silver, how likely it is that something is true).  Then connect the facts with your past experience and understanding to make a decision.  Only then will data help you succeed.

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Brad Kolar is an Executive Consultant, Author, and Speaker.  He can be reached at brad.kolar@kolarassociates.com. For more ideas on how to be an effective leader, visit his blog at www.leaderquest.blogspot.com

Relevant versus related data

Ok, here’s a memory jogger.  Think back to your high school algebra class and see if you can answer the following question.  You should be able to answer it in less than ten seconds.

A train leaves from Boston at 10:00 am travelling at 300 miles per hour.  Two hours later, a train leaves Los Angeles travelling at 150 miles per hour. The distance from Boston to Los Angeles is 2,982 miles. When the trains meet, which one is closer to Los Angeles?

If you are reaching for a pencil and paper or firing up your spreadsheet, stop.  It’s not necessary.  To solve this problem you only need one data point – the fact that the trains have met and are therefore equal distance from LA.

This problem is a simple example of the difference between relevant and related data.  The time, speed, and distance are all related to the problem.  However, none are actually relevant. With the abundance of data available these days, it’s increasingly important to separate the two.  

Having more data isn't necessarily better.  In fact, more data can actually make your decisions worse.  

At best, extraneous but related data is a distraction.  You spend time looking at them and making sense of them only to find that they don't inform your decisions.  Perhaps this is why so many leaders feel that they are constantly behind – they are spending too much time looking at data that don't matter.

Worse yet, related but irrelevant data can negatively impact the quality of your decision by causing you to miss the data that actually matter.  Recent research in psychology and neuroscience has shown that we can only process a limited amount of information at once.  As a result, our sub-conscious brains regularly filter information before we become aware of it, leaving certain things in the background.  Too much extraneous data can cause you to miss the actual data that you need to make your decision.

Even worse, sometimes too much data create an illusion of understanding which further diminishes your decision making.  For example, if you visit the roulette table at most casinos you’ll notice a lighted board showing all of the numbers that have recently won.  Why is the casino providing this data?  Does it help gamblers make more informed decisions?  Of course not.  It does the opposite.  Every spin of a roulette wheel is independent.  Therefore, every number has an equal chance of coming up on each spin.  If the number one comes up ten times in a row, the odds of a one coming up on the eleventh spin are the same as the odds for any other number.  So why provide the data? The answer is simple – to get people to bet more.  Most people who see these numbers don’t understand (or believe) the statistics behind the game.  As a result, when seeing the board of numbers, they believe that they have an edge in knowing which numbers may (or may not come up next).  And, when gamblers become more confident, they bet more.  The numbers on the lighted board are related to the decision that the gambler is making, but they are completely irrelevant.

Relevant data are the pieces of information that are specific to and drive a decision.  While all relevant data is related to the decision, not all related data is relevant.  Once you learn to differentiate the two, you will find that you spend less time collecting and looking at data, and more time thinking about data and decisions.

As a leader, if you want to speed up your decision making, get rid of related data and start focusing on relevant data.

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Brad Kolar is an Executive Consultant, speaker, and author.  He can be reached at brad.kolar@kolarassociates.com. 

Gaining independence

Those of us in the U.S will be celebrating Independence Day tomorrow.  Like our founders, we all want some level of independence in our work.  But wanting independence and achieving it are two different things.  Independence doesn’t come cheap.  You can’t just ask for it.  It requires work, vision, and even sacrifice.  

Fortunately, in our time, leaders who want independence don’t have to wage a war.  They do, however, have to fight for and earn it, just as our founders did.  Here are some lessons from our country’s battle for independence.

Know what you want – The founders had a clear goal in mind.  Granted it was a rather large goal, but they knew what they were after.  Before asking for more independence or autonomy, be clear on what you want.  You can’t run everything yourself nor do you have the talent, passion, or resources to do so. Figure out where you want more autonomy and build your case for why you should have it.

Be willing to sacrifice – Those who signed the Declaration of Independence and fought in the Revolution risked death.  Yet, they believed in their cause and the value of their independence.  You can’t ask to be left alone without assuming some of the risk in being on your own.  If someone hands over a part of the business to you, you may have to work a little harder and give up a little more in order to succeed.  If you are looking for greater responsibility but don’t want to upset your status quo, you might not be the right person for the job.

Own the outcome - Don’t just take ownership of the decision, take ownership of the result.  You can’t take credit for success if you also don’t assume responsibility for failure.  Too often, our leaders, especially politicians will provide a litany of excuses for why they failed, but will take full credit when they succeed.  If poor results are due to forces outside of your control then the good results are too rendering you irrelevant. If you want independence, show that you are willing to take accountability for its successes and failures.

Over promise and overwhelm – The founders didn’t go after a few concessions from England, they went big.  They made up for being outnumbered, out-funded, and out-trained with passion, commitment, and desire (and a little help from their other European“friends”).  They won a war that, at least on paper, they probably should have lost.  Contrary to conventional wisdom, leaders who gain autonomy and independence aren’t the ones who set a low bar and jump over it.  They are the ones who make bold promises and then surpass them.

Independence and autonomy aren’t part of your company’s benefits program.  They must be earned and deserved.  Show that you are ready, willing, and able to act on your own and you will be allowed to do so.

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Brad Kolar is an Executive Consultant, speaker, and author.  He can be reached at brad.kolar@kolarassociates.com. 

Why are we so afraid of subjective data?

Data and analytics continue to dominate the agenda of most businesses.  However, somewhere along the line, we’ve become myopic in what we consider to be valid data.  In particular, I’ve noticed a backlash against “subjective” data. To some, it is the enemy of good decision making.  To others, it plays second fiddle to “real” (e.g., numeric) data.  I think both attitudes are a mistake.

Data consist of any and all information you receive.  At times that information may take a numeric form but at other times it arrives as words and opinions.  Both are important and necessary in fully understanding your situation.  Both can also equally lead you astray if you don’t look at them critically.

There are three problems associated with this artificial distinction between the efficacy of objective and subjective data.

1)    Confusing quantification with objectivity
2)    Using a single data point to tell a story
3)    Misaligning outcomes and how those outcomes are measured

Confusing quantification with objectivityThere is a general belief that if you can assign a number to something, it is objective.  On the surface this seems true.  After all, there is universal agreement on how we count.  Four equals four anywhere in the world and it is one more than three and two less than six.  How can that not be clear?

The problem is that as soon as you decide to quantify something, you have to create rules about how and what gets quantified.  That process is subjective. 

Consider the presidential election in 2000 and the manual tallying of votes in Florida.  Depending on who was counting and how much a chad was “hanging” four might not equal four. 

In a prior entry, I wrote about a company that boasted a 99.6% safety rating in their rockets despite the fact that 8.5% of them exploded upon launch.  The company explained the discrepancy as a result of their not including explosions due to human error in their safety calculations. So was their objective number correct?

Finally, consider statistical significance.  People love to cite statistical significance as the ultimate objective criteria for assessing their data.  But there’s a catch.  Statistical significance is not absolute.  It requires an opinion - the level at which you want to measure significance.  Something that is not significant at a .001 level might very well be significant at a .1 level.  The person doing the analysis has to choose a level of significance at which to assess the data.  More often than not, that choice is subjective.

Counting is objective.  But, choosing what to count is subjective.  Numbers that appear absolute often have a story or set of assumptions behind them.  Those assumptions can often drastically alter what the data is actually telling you.  If you don’t believe me, just read the footnotes on a company’s financial statements sometime.

Using a single data point to tell a storyOne of the arguments against using subjective data is that it often doesn’t represent the whole picture.  For example, one customer can only account for his or her experience.  What if they happen to interact with your company on a bad day?  What if they got the customer service representative who was your lowest performer?  These tend to be the arguments against using subjective or anecdotal evidence. 

They are partially correct.  However, this isn’t an issue that is limited to subjective data.  It’s a sampling issue.  Any data you use must fully represent the subject area that you are trying to understand. Sales data taken from only one store in a chain can be equally misleading as an indicator of the entire chain’s performance as just using one customer’s opinion to draw conclusions about service.

In addition, any argument that is based on a single data point is probably going to be suspect and hard to sell. This is true for subjective and objective data.

I once had a person push back on me for using employee focus group data in making an argument about a particular issue in her department.  The issue had to do with problems with supervisors and managers.  She said that focus group data wasn’t reliable because one person raising an issue didn’t make it a problem.  She was confusing the use of a single data point with a conclusion based upon a multiple data points across a sample.

I told her I agreed. I would never extrapolate a single data point (subjective or objective) into a trend or finding.  However, in this case, the same issue consistently came up multiple times in multiple focus groups, across diverse audiences, more than any other issue.  The issue was real, even though we didn’t have any “hard” data to support it.

Ideally, your story should be supported by both objective and subjective data.  At a minimum it should be supported by disparate data sources.  In the case of the focus group, while we didn’t have any “hard” data, the issue was corroborated by discussions with leaders who said that they’ve observed similar behaviors in their supervisors and managers. 

If you are relying on a single data point to tell your story, it doesn’t matter whether it’s objective or subjective.  In either case, your story is likely to be flawed.

Misaligning outcomes and how those outcomes are measuredThe final issue that leaders face is mismatching their data and outcomes.  Some outcomes, especially those involving your customers, employees, or other people, are subjective.  If your outcome is subjective you need subjective data to asses it.  If your outcome is objective, you need objective data to assess it.  Misaligning in either case will provide an incomplete and possibly incorrect story.

A marketing department had a goal to improve the clarity of their communications.  I asked them how they were going to measure that.  They provided a list of objective measures:  Overall length in words, FOG index, use of acronyms, etc.  All of those were good measures and were certainly related to clarity.  However, none of them actually measured clarity.  Clarity is subjective.  The only way to measure clarity is to ask the person reading the message whether it was clear.  When I suggested that to the mangers they pushed back.  They were concerned that such data would only provide opinions.  And, what if one person’s definition of what was clear was different than another person’s?  I suggested that was what mattered.  It doesn’t matter what people’s definitions are, if half of your audience doesn’t think your communication is clear, it’s not clear regardless of their (or your) definition of clarity.  The team finally reconsidered.

In an age of sophisticated process measures and techniques for gathering process data, we sometimes forget that process data only measures the process.  It doesn’t tell you what people think of that process.  That is a subjective assessment.

Of course, not everything is so black and white.  Sometimes an outcome is a fuzzy.  For example, suppose you are trying to improve decision making in your organization.  Ultimately your decisions impact objective measures around quality, efficiency, and results.  However, those connections aren’t always direct or improvements can take a while to translate into results.  In those cases, a combination is needed.  Look at the objective measures but also ask people their opinion (individuals, their bosses, their peers) if decisions making is improving.  The combination of objective and subjective will give you a much better (and quicker) understanding of what is really happening.

The story of your business happens regardless of whether you have the tools and metrics to needed quantify it objectively.  By limiting yourself only to “objective” data, you are limiting yourself to seeing only half of the picture.

All data is helpful but all data is incomplete and subject to problems.  Be smart.  Take advantage of all of the information available to you.  Just be critical, understand each data set’s strengths and limitations, and use multiple data sources to tell your story. 

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Brad Kolar is an Executive Consultant, speaker, and author  He can be reached at brad.kolar@kolarassociates.com.

Why “dummy” lights are so smart

For several decades car dashboards have been equipped with “dummy lights”.  We rarely see these lights (except briefly when starting your engine) but they are always there ready and waiting to alert you to a problem (check engine, oil pressure, tire pressure, airbag problem).  Their less than flattering name comes from the fact that they don’t require much knowledge/information about the car in order to use them.  In fact, they are designed to purposely limit the amount of information we receive since most of us wouldn’t know what to do with the details anyway.

Despite the fact that many of us successfully use these on a daily basis, their application hasn’t translated to the business world.  We can drive a car perfectly well and determine when a problem requires our attention without on-going, detailed metrics on each of the car’s system’s performance. Yet, for some reason we still think that unless we monitor a large amount of data in business we won’t be able to manage. Ironically, that constant barrage of numbers is probably what distracts us, misleads us, and otherwise makes managing more difficult.

The reality is that when trying to assess a situation and where your problems lie, we really don’t care about the numbers.  The numbers are just an input.  What we really care about is what the numbers are telling us.  For example, it doesn’t really matter if my sales are 100 units or 300 units if they were supposed to be 1,000 units.  What I care about is the fact that I am behind.  So why do we need to show current sales, the sales goal, and last year’s sales on the report?  That’s too much data.  It’s not efficient.  Why give three data points (and an implied fourth, the difference between actual and target) when a simple “on target”, “ahead”, or “behind” is really all you need.  That would be like displaying current oil pressure, desired oil pressure, and last week’s oil pressure on your dashboard.  Trying to drive and monitor all of that data would inevitably lead to a crash.  Not surprising, it does in business as well.

Many of us intuitively recognize this.  That’s why we use colors or symbols on our reports.  In essence, we create “dummy lights”.  When I ask people what they are looking for when they read the report, they’ll often say, “I look to see what’s red and what’s green.”  They don’t look at the numbers, they look for the colors.  However, the difference between these dummy lights and the ones on our dashboards is that in business the dummy lights are in addition to, rather than instead of the detailed data. The result is an even more busy and confusing report.  I’ve seen reports that look like the control panel of HAL in 2001: A Space Odyssey.  

If you find that you are using colors and symbols on your reports you are halfway to designing more simplified reports.  You’ve already identified what you really care about.  Now, take the next, harder step, and drop the numbers.  Organize the report around the colors or symbols (e.g., put all of the reds together, all of the greens together, etc.).  You will get your questions answered much faster.

This isn’t to say that specific numbers aren’t ever important.  When you need to diagnose a problem, the data are essential.  But assessing where you are at and diagnosing why you are there are two different things and should be treated that way.  When your “check engine” light goes on, the mechanic plugs a diagnostic tool into a port under your steering wheel and gets all sorts of data and error codes related to the car’s performance.  But, you only need that data if there is a problem.  Tracking it while you are driving will only create a distraction.

More numbers don’t always equate to better decisions.  In fact, as the volume of numbers increases, decision making often suffers.  The more data you encounter, the more your brain ignores, distorts, or invents in order to try to manage the data.  That inevitably leads to poor decision making.

Let go of the numbers.  You don’t need them as much as you think.  Focus on creating reports that simply and directly show you where you have problems and where you are doing well.  Then, for those areas that require your attention, you can jump into the details.

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Brad Kolar is an Executive Consultant, Speaker, and Author.  He can be reached at brad.kolar@kolarassociates.com.

Leadership is about outcomes, management is about activities

The distinction between outcomes and activities isn’t new.  Most leaders will tell you that it’s important to focus on outcomes.  Yet, beyond talking about it, many have difficulty with the “focus” part.

The problem that I often see is that leaders struggle to manage outcomes and activities simultaneously. Outcomes become book ends.  They are discussed during goal setting and then revisited during annual reviews. However, at point it’s really too late to do much about the ones that were missed.  So instead, most people simply reverse engineer an outcome to justify their activities.

As a result many of us don’t deliver the outcomes we originally intended or if we do, it’s often in spite of rather than because of the activities in which we engage.

An alternative is to manage outcomes and activities together.  Each of us continually wears two hats: manager and leader.  Management is about controlling and optimizing activity.  Leadership is about driving outcomes.  By constantly alternating between the two, leaders can ensure that they are on track to deliver real results to their organization.

The first step in doing do is to remember that activities support outcomes.  They are important and necessary, but they should be your second consideration. Too often we begin our dialog with activities and work our way back to the outcome.

Instead as a leader, drive your entire dialog from an outcome.  Start by asking if you are achieving the RESULTS that you set out to achieve.  If not, then assess your activities to understand where the problems might lie.

This is a subtle but important shift. When you begin the dialogue with the activity, you’ll almost always create an illusion of progress.  After all, you’re almost always doing something.  In addition, when starting with activity, you tend to take get a false sense of assurance because you make assumptions about the result that the activity SHOULD produce sometime in the future which is always positive.

By starting with the actual outcome you remove any doubt as to whether progress is being made.  Either you’ve saved money or not (if that was your goal) or your customers are more satisfied or not, or your quality has improved or not.  There are no activity-based illusions.  You don’t plant the victory flag until you start to see the outcome or change to which you committed.

Starting with the outcome, in this manner, is difficult for many people.  One of the reasons is that we often set activity-based milestones rather than outcome-based milestones. Activity-based milestones outline the steps you’ll take (and when you’ll take them) to reach an outcome.  Outcome-based milestones show where you’ll be at different points in time relative to the final outcome.  For example, if your overall outcome is to save $100,000 in expenses over the course of a year, the outcome based milestones would be commitments as to how much savings you’ll realize each month (or quarter) throughout the year.  That way, each month, you can’t simply show that you’ve been working to save money (activity).  Rather, you must demonstrate the actual cost savings that you promised.  If you aren’t achieving the savings, you know that it’s time to change your activities.

A second reason that outcome-based management is difficult on a day-to-day basis lies in the way that many leaders set goals.  There are two common problems:  1) goals that don’t contain an actual outcome or 2) goals that combine the outcome with an activity.  A true outcome-based goal should be short and simple.  It should state what is going to change in your business, the direction of that change, and the magnitude of the change.  For example, “Consolidate vendors” is an activity.  Even if quantified by the size of the reduction, it’s still an activity.  On the other hand, “Reduce contractor labor costs by 25%” is an outcome.  Note that in the outcome-based goal, there is no mention of how the goal will be reached.  This is intentional.  By splitting the actual outcome from the activity, you create more options for achieving your goal.   It also keeps you focused on achieving the goal since that is the place that you start all conversations.

The table below summarizes the difference in managing outcomes and managing activities.  A good leader has to do both.  However, from now on, try to start with the outcome and then move to the activity if needed.



Execution: Leadership v. Management
 
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Brad Kolar is an Executive Consultant, speaker, and author with Kolar Associates.  He can be reached at brad.kolar@kolarassociates.com.