Monday, February 12, 2024

Scaling a business depends on controllable factors

 One of my favorite musicians is (was) Warren Zevon, who had a great sense of humor and was also a good musician.  You may be familiar with his song "Werewolves of London" but perhaps one of my favorite songs of his is "Lawyers, Guns and Money".  In the song, he describes what it will take to get out of a particular jam, and notes that neither lawyers, guns or money will get him out of his predicament.

While the song is humorous, it made me think about the reverse question.  Why are some songwriters or musicians so successful, while others, equally talented, aren't successful.  And, if we take the analogy further, can we ask:  what are the factors that make some companies so successful, while others, in the same industry, with the same information, aren't successful?

It's not lawyers (although they may help), it certainly isn't guns, at least not yet, and while money can appear to be an advantage, just remind yourself that Yahoo! was the big search engine when Google was conceived, and Yahoo! could have purchased Google for a reasonable figure in the early days.  Money is helpful but not decisive.

What are the factors that dictate the success or failure of a business, and which of them can we control?  Luck and timing play a part in the success or failure of any business, but what's more important is management focus, alignment, communication and setting priorities.  Easier said than done.

What we can control vs what we cannot control

In any venture, there are factors that the leadership can control, such as when to spend money and what to spend it on.  There are also factors that the company could control, but through inaction, distraction or simple lack of acknowledgement they fail to control.  There are also externalities that cannot be controlled, some observable (competitive actions) and some potentially random (timing and luck).

We ought to ask ourselves as leaders in any company:  what can we control?  Do we have line of sight, awareness and accountability on everything we can control?  Next, what can't we control that we need to be aware of?  What externalities could impact us?  What shifts or trends could reshape our ability to compete?  And finally, what factors are important but perhaps unknowable?

We shut down because...

This takes me back to a funded startup in the early 1990s.  I was working as the global VP of Marketing for a software startup doing data mining on large data sets.  We had the right ideas and were just beginning to show value on customer experience and preventing churn, when the attacks on 9/11 happened.  While our product/market fit wasn't perfect, we were learning and getting better, but at that point we needed another round to get the product exactly right and ready to launch.

We couldn't anticipate what would happen during the attacks, or the way that VC funding dried up after the attacks.  So, about eight months later, the company was no more.  

Looking back, I can say with some certainty that with another round, we probably would have created a compelling product, but with equal certainty that we had more to do to create the software the market wanted.  The unexpected and uncontrollable event doomed us.

It's easy to blame the failure of the company on the lack of VC funds, and many companies shut down and blame externalities, or issues that were out of their control.  These factors do cause issues, for startups and larger firms, but my work over the last decade has taught me that while we often blame these unforeseen and uncontrollable issues for failure, the reality lies much closer to home.

Controlling the controllable

After working in innovation and strategy for over 20 years, a few things are clear.  First, most companies don't really understand what factors they can control, and most leadership teams need help prioritizing and controlling the factors within their control.  A list of some of the things that need focus and control include:

  • What is the company's core value proposition?  Why does what you do matter and why are you truly different than competitors?
  • What is each person's role in the leadership team and next level down?  Where does one person's job or role begin and another one's end?  Are there areas of uncertainty or discord between roles and jobs?  Can you build a RACI model for your business that everyone agrees on?
  • What are the fewest, most important things (priorities) that the leadership team must focus on to achieve their goals?  What shiny objects or nice to haves are regularly paraded about, and how does the leadership team dispose of things that aren't top priority?
  • Is your leadership truly aligned to the same outcomes and goals?  Does their compensation and evaluation align to the company's goals?  Are there disagreements or gaps in the leadership team about strategy, execution or measurement?
  • Is there clear accountability, and are people held accountable to plans and execution?
  • Does the leadership team communicate well within the team, and equally well and equally consistently to the rest of the organization?
If you've read the bulleted list, you'll be thinking just about now that these are basics.  A good leadership team should be doing all of these well.  Yet I can tell you that in my experience working with leadership teams (from VC backed startups to rapidly scaling firms with PE investment to Fortune 500 companies) that these issues recur over and over again, and are the main reason that companies fail to achieve their goals.  It's not lawyers, guns or money, or luck or timing that creates success.  It's focusing relentlessly on the basics.

There is a saying that a well-aligned executive leadership team, with a good strategy, can do just about anything, and I believe that is true, if they are truly of one mind, aligned to a great strategy, with clarity about roles and responsibilities and willing to hold themselves and their teams accountable.  When a leadership team focuses on these basics and does them very well, they are controlling for the things they can control, optimizing and anticipating issues and dealing with them quickly and directly.

The problem is the people

Which leads me to another content favorite - Hard Core History with Dan Carlin.  If you don't listen to Hardcore History, you should. It's a great podcast.  I'm listening now to his account of the war in the Pacific in World War II, and he makes a great point.  Both the Japanese armed forces and the US armed forces were filled with great leaders, good soldiers and weapons.  He points out the problem wasn't with the weapons but with the people.  He makes the case that the war could have ended much more quickly, or differently, "if it was fought by Vulcans".  His point is that emotion and ego often created conflict.  A good example is MacArthur, who let his ego cloud his ability to work with the US Navy.  Carlin's premise, and it's one we all should pay attention to, is that if Vulcans, who decide purely on logic, were in charge, things would be done differently.  Instead, humans are in charge, and we decide based on emotion, knowledge, logic and a host of other factors.

My brief segue about Hard Core History aside, the same concepts are true about leadership teams.  What gets in the way of success are different agendas, misalignment, different priorities, poor communications, distrust, lack of clarity, lack of accountability, poor execution, lack of measurement.  When I talk to leaders in businesses of all sizes, these are the issues that really create difficulty and drag the business away from what it could achieve.  These factors exist in small, underfunded startups and in large, public Fortune 500 companies, because, ultimately, we are people with selfish interests trying to work together to create a company that creates value and rarely do people see eye to eye on everything.

These conditions exist in good times and in bad times, by the way.  When a company is profitable and the markets are good, it's easy to overlook or ignore issues or challenges in these factors.  Then, when hard times hit and it's important to button up and become more effective and efficient, poor habits or lack of focus is hard to change, since it has been ignored previously.

Working it out

While leaders in every organization have a tremendous amount of work on their plates, it makes sense to check in on these factors at least quarterly.  Just as people exercise to keep their bodies fit, companies should review their interactions, alignment, execution and accountability to ensure that the organization works at peak performance.  There are simply too many factors, that are too critical to success, to allow these to atrophy or to gloss over non-compliance.  

And, as these factors become clearer and more closely evaluated, two things will happen.  The organization will work more effectively, freeing up time for executives to focus more on the strategy and less on the day to day, because they are no longer bridging gaps, and, as the engine of the business becomes more efficient, growth and scaling is actually easier to accomplish.

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posted by Jeffrey Phillips at 5:00 AM 0 comments

Friday, February 02, 2024

You can't burn data

 As the concept of digital transformation takes root, you may frequently hear comparisons between data and oil.  After all, both are abundant commodities that can create value.  This comparison was strong enough to lead Wired magazine to define data as the new oil in a magazine article some years ago.

On the surface, this comparison seems to make some sense.  Both data and oil are commodities, and exist to some degree in large volumes.  Both have the ability to create incredible wealth when harnessed appropriately.  Both can be used for good purposes or misused.  Both data and oil have interesting issues and side effects, pollution in the case of oil and loss of privacy in the case of data.

But the more you consider the two, the faster the analogy breaks down.  That's because the analogy works at the surface, but when you carefully think about the value propositions and the issues associated with oil and with data, you'll see that there are some really interesting similarities but some stark differences.  We gain value from oil by burning it to generate heat, light or kinetic energy.  The conversion of data to value simply isn't that straightfoward.

Let's look at some interesting similarities first.


I'm not a data scientist, but I did start my career developing enterprise systems and worrying about the value and quality of data that flowed through those systems.  Data quality and data volume may not seem like interesting concepts, but when we talk about using data to "transform" a business or process, the data needs to be of the highest quality if decisions are to be based on the data.

Most businesses have a range of data types, data quality and data management, none of which lead to high quality data.  And, as stated previously, without good quality data, you cannot automate or transform anything.  So, all the talk of digital transformation is talk until companies normalize, standardize, simplify their data and ensure it is of the highest quality and veracity.

Another problem many companies face is the Volume and Velocity of data.  And yes, I am capitalizing the Vs because a good way to think about data is in Vs:  Veracity, Volume, Variety and so on.  Veracity or truthfulness or data quality is obviously key, especially when training a machine learning algorithm.  However, veracity is difficult if the data comes in a variety of types and the sheer volume of data is so high that people and machines cannot determine what data is useful and what data isn't.

In other words, most companies need to refine their data, in much the same way that oil coming out of the ground needs to be refined.  Venezuela has perhaps the largest oil reserves on the planet, but since most of its oil is thick and loaded with sulfur, the oil there is of less value and needs to be refined in order to be used effectively.  Oil straight from the ground can be refined into a number of different grades of fuel we consume, from bunker oil for ships to gasoline for cars to home heating oil and many other types.  But raw oil isn't all that useful until it is refined, and sweet crude from Texas is easier to turn into marketable products than Venezuelan oil.  In the same way, some data is more valuable, and easier to put to work, than other kinds of data.  As Einstein said, not everything that is countable counts.

Like oil, data need to be managed, refined, combined and prepared to be used effectively, and few firms have a strong handle on what it takes to manage data and ensure that the data is of high quality when it goes into a system, and remains high quality as it is used, combined with other data and manipulated.  When you stop to consider that the most common reporting mechanism in companies is Excel, where any cell can be changed or any calculation can be written incorrectly, there are thousands of opportunities for even high-quality data to be mis-used or reported incorrectly.


While it is a simple metaphor to compare oil to data, it is also grossly misleading, for several reasons.

First, oil is a non-renewable commodity.  That is, there is only so much oil in the ground, and it is getting more difficult to extract what's left.  On the other hand, data is getting created every day, by billions of people, about millions of topics, in hundreds of types.  Data is not only renewable, but it is almost inexhaustible, limited only by human creativity and need. Each month we created as much cumulative data as was created from the dawn of writing until last year.  The problem isn't data, the problem is the volume and veracity of the data we are creating.  With so much data generated from so many platforms, how do we know what data is useful and meaningful, and what data is created merely to distract or confuse?  I can imagine it will soon be possible, if it is not already, to create AIs specifically for the purpose of creating seemingly valid information that has little or no basis in reality, to confuse or distort economic projections or scientific inquiry.

Second, while oil was the basis for carving up nation-states in the Middle East after the first World War, data will be the dividing line in the future.  Oil to a great extent is a monopoly based on accidental or purposeful geography.  Some countries - Saudi Arabia, Venezuela and others - have lots of oil, while others - Japan for example - have little or no oil reserves.  Japan has been a major success for a country with limited natural resources such as oil, but data is unlike oil.  Anyone can create data, and almost all of us do so every day.  Increasingly, it's not countries or geographies that control data, but companies.  One could easily say that Meta is the Saudi Arabia of the data stores, since it has so much data about so many individuals.  Nations, which once coveted oil, are just waking up to the value of data and realizing how much power data provides and wondering how virtual companies like Meta and Google have managed to hoover up so much data and become so powerful.  Why is Mark Zuckerberg advising Congress on data?  Because Meta controls more data and has more insight than most agencies in the US Government.

Third, oil has a provenance and a supply chain, for the most part.  That is, we talk about "sweet Texas" crude or Saudi oil or Venezuelan oil.  While oil is a commodity, it typically has a provenance that indicates its value, and further there is a value chain associated with oil.  Oil moves from a driller through a pipeline or other distributor to a refiner and then on to another distributors, a wholesaler and a retailer.  In other words, there are specific value-added components that make up the oil to marketable commodity (such as retail gasoline) that we consume.  Data does not necessarily come with a provenance and does not need a supply chain to reach a consumer.  Anyone can post a data set that they've created, create their own research or surveys, and make that data accessible to almost anyone, instantaneously, on the internet.

Next, oil is a commodity, and certain grades of oil are priced in global markets, regardless of where they are drilled.  Data, on the other hand, is often not quantifiable as to its value or price, and the same data set can be more valuable or less valuable, depending on where it is, who has it and who needs it. A list of names can be very important, if the list is a list of spies in a foreign country, or conversely very unimportant if it is a grocery list for a suburban family.  But if a company could compile thousands of lists of grocery shopping for families in similar circumstances, that compendium of data would become valuable to grocery chains and the brands that sell on grocery store shelves.  The value of data is in the eyes of the beholder, and the value of data varies with a number of factors.   

Finally, since oil is a commodity, it is easy to acquire in a specific grade at a specific price.  Data, on the other hand, is difficult to aggregate, difficult to grade and almost impossible to price.  I recently acquired a list of prospect companies and executives that promised to be up to date and highly accurate.  As I scanned through the list, I quickly found several mistakes or omissions that should have been easy to identify in just the first fifty to one hundred names.  It is hard to keep many types of data accurate and fresh, difficult to validate data without human intervention, which makes almost all data stores suspect to some degree and in need of constant evaluation and refreshing.

Thanks for the analogy, what does this all mean?

In the end, the analogy between oil and data falls apart.  Oil is a standard commodity that for the most part we burn to gain heat or kinetic energy.  We transform oil into one or two potential outcomes, at specified temperatures and pressures.

Data, on the other hand, is not a commodity, is perishable, is constantly renewing and generative, is difficult to price or establish a value for, and we find it difficult to create real quality metrics for data.  Plus, data is not a commodity.  The same data sets have different values at different times to different users.  

What this means is that the talk of digital transformation - using data to fuel a new era of economic growth, in the same way that oil spawned economic growth and benefits in the 20th century, is optimistic or true in some circumstances but not in others.  Companies that want to transform themselves to be more effective and to create new revenue streams based on data must first address the questions of veracity of data, volumes of data, varieties of data.  In a time when analysts suggest than less than 10% of the data that companies currently possess is being used to create value or insight, what leaps of technological advancement and data quality improvement are necessary to base a new economic model on driving competitive advantage from data?  After all, we aren't converting a commodity into heat, light or kinetic energy.  We are transforming trillions of data sets from millions of data sources into useful insight, which companies can act on.  

This means that the benefit to data will be widely and unevenly distributed, highly beneficial in some industries or market segments or niches, and almost impossible to understand in other segments of the economy, for decades yet to come.  Those companies and industries that cleanse their data, understand the variety and volumes of data and who can make sense of the data to convert it into useful insights that indicate future actions will benefit to an enormous degree.  The rich will get far richer.

Many industries will spend billions of dollars trying to get the same benefits, but will lack the basics - good, high-quality data, understanding which data matters, being able to capture and use the most effective and useful data, and being able to convert the data into beneficial actions. 

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posted by Jeffrey Phillips at 8:20 AM 0 comments

Tuesday, July 18, 2023

Sneaky innovation

 I'd like to write today about what I call sneaky innovation.  I define sneaky innovation as the innovation work that is often started and completed in disparate locations in the business, is not strategic and is often completed without a lot of fanfare.  Sneaky innovation is about doing small but impactful innovation, without asking for permission or waiting to see who will approve.  The fact that sneaky innovation accounts for probably 30-40% of most of the innovation that gets done should tell you something:  there's a significant amount of need for new ideas and for innovation generally, but not a lot of will to seek permission, or a good sense of what the answer will be if the request is made.

Eventually the good news about sneaky innovation is that teams can demonstrate that they can get new things done.

The good things about sneaky innovation

There are several very positive attributes of sneaky innovation.  When innovation is small, kept quiet and worked on by a small team, the ideas can flow freely and new insights will emerge.  Often, starving innovation teams of resources, time and attention will require them to think more creatively and generate ideas or solutions that would not have been generated otherwise.  Sneaky innovation is often focused on process improvements and customer service or experience problems that teams can define and implement without a lot of approvals or permissions, and demonstrates that teams can identify needs, generate ideas and implement solutions often without the management team knowing, at little cost or risk.  So, sneaky innovation demonstrates that the capability to innovate exists, as well as the ability to put good ideas to work.  

The problem with sneaky innovation

There are, as you might imagine, a few problems with sneaky innovation.  For instance, sneaky innovation can't work on physical products or business model changes.  No matter how sneaky you are, someone is bound to notice a new feature or a new revenue stream or cost component.  So, if you rely on sneaky innovation for your business, you are limiting yourself to innovations and implementations that few people will notice until after the fact.

Sneaky innovation is, almost by definition, a local phenomenon.  That is, different groups will perform their version of sneaky innovation at random times, and in random ways.  There's no way to scale success from sneaky innovation, and few ways to describe how it works or how others can learn from success, since the point of sneaky innovation is not to raise too much attention to the fact that innovation is going on.

Sneaky innovation will almost always be starved for funding, but not for personnel.  People like to pull one over on their managers, so they will commit time and energy if they think the idea will succeed.  This means that ideas cannot cost a lot of money to implement or test, but can take a significant portion of peoples' time and attention.

Bottom up or top down innovation

In the past, I've tried, with great passion and hopefully deep logic, to try to illustrate why I think innovation should become a business process, ordained by the corporate executives, sustained at all levels of an organization, encouraged by culture and incentives.  However, that vision will require a new set of leaders who may be emerging, but it does not seem to sit well with existing corporate leaders who are happy to isolate innovation in R&D, or dabble in occasional innovation projects but fail to build innovation capabilities and capacity.

If we cannot build the idea of innovation as a repeatable process and a cultural phenomenon, we need to go guerilla.  Find the executives and manager who are willing to take risks, to create smaller, sneakier innovation projects.  Ideate, generate ideas and implement under the cover.  Only claim the results once the benefits are clear.  Perhaps it's time to build from the ground up, rather than from the top down.

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posted by Jeffrey Phillips at 7:27 AM 0 comments

Tuesday, July 11, 2023

Five reasons why few companies are data driven

 For years now, we've heard of the importance of becoming a "data driven" organization.  Being data-driven, we are told, means making decisions not based on gut instinct or what managers believe is true, but based on evidence, on hard data.  This, it would seem, should be simple.  After all, businesses have had robust IT systems and teams for years.  The advent of ERP in the 1990s and beyond created sweeping systems that automated many sectors of the business.  Even smaller companies that did not need the larger ERP systems like SAP could find solutions in Salesforce, Workday, Netsuite and others.

So, why is it that so many companies cannot make decisions based on their data?  If we've known about this need for decades, if we expound upon the importance of data and its use in decision-making, if robust systems exist to allow us to create, gather and use data effectively, why are so many companies no closer to becoming a data-driven organization.  There are several reasons:

  1. The executive team does not like what the data tells them.  In many cases, data contradicts what management wants to do, so in those cases the data must be "wrong".  The data is inconvenient to current thinking or perspectives.
  2. The executive team does not trust the data, and has good reason not to trust the data because it is out of date or incomplete.  The data is full or errors, untrustworthy, incomplete.
  3. The executive team trusts the data and the data is accurate, but the data does not inform current or future decisions.  It is good data, just not the necessary data.
  4. There is too much data and not enough information to inform executives.  The team is inundated with data and has too little information.
  5. The executives have accurate and up to date data that is meaningful and easy to understand but they do not know how to interpret or act on the data.  The decision makers do not have the perspectives or tools to interpret the data and make decisions.
In some cases, all of the above is also true.

From these five reasons, there are three categories of issues:  problems with data itself, problems with policies about data, and problems with people.  The first two are the easiest to solve.

Problems with data

Every company has lots of data, often spreadsheets full of data.  And therein lies the problem:  there is no single source of truth, and data is highly compartmentalized and curated for each team or department.  When decisions need to be made at an enterprise level, but all data is managed and manipulated at a team or departmental level, obtaining a realistic view of the data or information a company has, and what that data means, is difficult because when aggregated, the data has passed through so many filters or lenses that it is often contradictory or meaningless, or so out of date as to be useless.

The problem is also part of the solution:  many managers and executives grew up in a time when IT managed all the data, and IT can be monolithic and slow to respond, full of data governance rules and policies.  So, many companies have gone "free range" with their software selections and data management.  Every department selects its chosen software, deploys how they care to, and does not worry about enterprise data or data management.  The problems with this approach will emerge within 6 to 12 months of the third or fourth departmental deployment, when executives request a single view of the business on one sheet, and no one can provide it.

Problems with policies

Face it, no one likes being told what to do, and no one likes policies, until those policies actually benefit them.  Managers and executives don't like data lexicon or nomenclature, or rules about data usage or data governance, because they impede speed and deployment and impose rules.  However, months or years later, they inevitably look back wistfully and wish they had had the coaching to implement data governance early, because imposing these rules and policies later in a company's life is difficult.  My advice:  start defining your data policies and rules early.  Keep your definitions consistent and clear, your calculations the same across teams and divisions.  Trying to impose data policies, data governance and data quality on a company years later is like trying to fit a grown adult into the clothes they wore in their youth.  What you need is a good suit of clothes that can be upgraded and tailored from youth through adulthood.

Problems with People

Even if your data is correct and your policies are strong and the data is timely, if managers or executives cannot interpret the data or cannot describe the data or information they need to run a business, all the other infrastructure work will fail.  There are plenty of people who are experts at a very narrow range of business metrics - sales people know sales metrics, financial people know financial metrics, but few of us get education or experience in managing metrics across a business - we are often simply too siloed.  But in a small but growing business, understanding all of the metrics is vital.  

As much as we'd like to believe that we educated humans are rational actors, we are attracted to data that reinforces our biases and reject data that calls into question our wants and goals.  A data driven organization needs a Spock-like character who can make decisions not based on desires or emotion but on pure logic, and most of us aren't able to do this reliably.  When you realize that NONE of us are logical or Spock-like, and we all have our own perspectives and biases, you can see how difficult decision making influenced by data becomes.

That's the data, what does it mean?

Finally, being able to understand the data is one thing, understanding what it means, what the data is telling you is another thing entirely.  Here, we can't simply wish our way to intelligent AI bots that will tell us which actions to take based on the information we are receiving.  Good managers and executives will need to combine experience, history, data and information, emerging trends and some gut instincts in order to evaluate data and make determinative decisions and actions.  Most of the existing management cadre in business today are familiar with backward looking data (reporting) and historical trends and precedents but are not familiar with the existing pace of change, or the amount of data and the requirements for interpretation of the data, much less trying to determine future actions based on the volume of data they are receiving.  

Solving for the three big data issues

There are two approaches to solving for these issues - bottom up or top down.  My recommendation is top-down.

The bottom-up strategy will focus first on getting the policies correct, then correcting the systems, how the systems are integrated and the validation of the data.  It's not really all that helpful to start improving the data and systems unless there are policies that will sustain the changes you make.  Once the data, systems and policies are in place, then you can ensure your leadership team can interpret and use the data.

The top-down strategy requires that the executive team and senior users and managers of the data can describe what they need in order to make better decisions, and how they need the data presented to them.  Then, they can create or reinforce policies around data strategy, data quality and data governance, which will influence how systems are implemented and how data is exchanged and aggregated.

Understand that all of these things need to change - the systems and their integration to get better, faster data; the policies to ensure that they data is useful and meaningful; and the users, to ensure that they are educated in how to interpret the data and how to make decisions based on data.  This is yet another holistic change that needs to occur.  Changing policies without changing systems, or educating the executive team without improving the quality of the data leaves the solution incomplete.

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posted by Jeffrey Phillips at 4:05 AM 0 comments

Thursday, June 01, 2023

Objects in the mirror are closer than they appear

 I'm constantly struck by how much faith businesses put in numbers, especially numbers about the past.  All businesses want to be "data driven" and the volume of reporting that occurs at the end of a week, month, quarter or year is astounding.  Of course, we want to know if we made our numbers, or how key metrics compare to metrics from other months or quarters.  But the focus on numbers about the past, even the recent past, pales in consideration to the focus on what's going to happen next.  And there are several reasons for the emphasis on looking back at numbers rather than looking forward to possibilities and probabilities.

There are several problems with a focus on numbers and compilation and analysis, when looking at a quarter or month just completed.  First, most companies don't have great data quality or data flow, so a lot of the metrics they are looking at require a lot of massaging and human manipulation, which eats up an enormous amount of time.  Second, the data is all hindsight - did we make the quarter just past?  How does that compare to the previous quarter?  Of course, these questions are important, but just as important is:  what's going to happen next? 

Far too little data and analysis goes into what's going to happen next.  Yes, there are attempts to build pipelines and forecasts, but those are often wrong by orders of magnitude.  We aren't up to using data to be prescriptive about the future and have too many people focused on what just happened - reporting, rather than figuring out what's going to happen - predicting.

This focus on historical data isn't a problem in settings where there are few uncertainties and little change.  The emphasis becomes a larger problem when data is uncertain, people are at a premium, and understanding what's going to happen next is more difficult.  Welcome to your VUCA world, where things are changing faster than ever, and often in unexpected ways.  Now we need data and people to help us understand what is going to happen and how to prepare for and anticipate what's going to happen, because factors are changing in unpredictable ways and the rate and pace of change is accelerating.  

Worse, we've trained a cadre of managers who are good at reviewing and analyzing data about the past, but don't have the tools or skills to look forward with any degree of certainty.  When I do trend spotting and scenario planning with my clients, it's interesting to hear people talk about the future and what will occur and the ideas they create as if the possibilities are unlikely and far in the future, when many of the ideas and situations they create are actually happening in real time.  As William Gibson has said, the future is already here, it's just not widely distributed.

Doing well in business in the coming years will be more about using data, both quantitative and qualitative, to understand and predict what is likely to happen in the short run, and to develop scenarios and pathways for the longer term, again based on emergent data and observable trends.  This is not yet an area where AI or ML will be as helpful, because the data and patterns are not yet as established.  Analyzing and predicting based on past behavior in somewhat stable environments is a trainable activity.  Anticipating events and scenarios where there is no history or data, with uncertain inputs and conditions requires a significant amount of adjacent thinking and exploration of possibilities.

The old warning on rear view mirrors was that objects in the mirror were closer than the image in the mirror might suggest.  I think we need a new warming, on the forward windshield of your business:  Objects in the future are closer, and moving faster, and more erratically, than you expect.  Is your business prepared?  Is it capable of being proactive, understanding what might happen and being nimble enough to address rapid changes in stride?  Does the word "surprise" show up often in your 10Ks and 10Qs?  If so, it may be time to change your emphasis from reporting to predicting.

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posted by Jeffrey Phillips at 8:38 AM 0 comments

Thursday, May 25, 2023

Identifying and overcoming the innovation resistance

 All good stories need a protagonist and an antagonist, the "good' character and the not so good character to develop tension.  Eventually, in any story, the tension is resolved - the protagonist wins (in the happy ending stories) or the antagonist changes sided, or the antagonist wins (unhappy resolution).  Every story with any meaning or tension has these two opposing forces. 

So, it seems strange to suggest that there are innovation protagonists, and innovation antagonists.  We are led to believe that "everyone" wants innovation.  Everyone likes new things, new ideas, new experiences.  But with every new creation, something or someone is put at risk.  Even people who claim to like and appreciate innovation can become resistant or antagonists to innovation when new ideas threaten their cherished products or positions.

Why innovation is like Star Wars

I grew up with Star Wars, with Luke and Darth Vader as the key actors.  I guess it's not strange that Luke is always shown wearing a white shirt and Darth is always depicted as wearing a black cape.  Much like the old westerns where the good guy wore a white hat, and the bad guy a black hat.  

The strange thing about innovation is that there are Lukes - people who really believe in the power of the innovation force, as a means for good, and people who, mostly in the moment, see innovation as a threat, or a force to be managed or turned to their own devices.

I don't think anyone sets out to be a Darth Vader for innovation intentionally, but many people play the role of Darth when innovation threatens the projects, products or positions that are important to them. Let's consider why, and when, innovation becomes a force that will create resistance and Darth-like characters may emerge.

What causes the rise of the Dark Lord of innovation?

Innovation seems to be a very positive force, creating value and opportunity, creating new products and services that most consumers want and need.  Like ice cream and puppies, it's kind of hard to imagine that anyone would resist innovation, and I suspect that even the people or teams who do resist innovation often find themselves in an uncomfortable situation - resisting a force that they know is meant for good, but in the moment appears as a threat.

There are at least three instances where innovation resistance will arise, and it will arise mostly within a company (although sometimes from external actors) and mostly will arise as a reaction to a perceived threat. 

The "locked in" people and products

You've encountered this before.  Some people are resistant to change, even when you can show them a better and brighter future.  They resist change, and its uncertainty, not necessarily the idea or solution itself.  To these individuals or teams, change itself is the enemy, and innovation is simply another attempt to create change that they will have to deal with.  The language of these folks is:  "if it ain't broke, don't fix it".  Change of any form creates challenges and uncertainty.  These individuals revere the past glory of their product or company, and are very concerned about the future.

The leaders of products put at risk by innovation

In the zero sum game of most corporate budgeting, a new product needs to get funded, and those funds almost inevitably come from an existing product.  Rather than recognize that all products go through a life cycle, some product managers become affixed to their products, and seek to defend them from all new opportunities and ideas.  While these individuals aren't afraid of change and are in fact often open to innovation, innovation that threatens their sacred cows will be fought on all fronts.  The language of these people is:  yes, let's innovate, but not at the expense of my product.  In another setting, these resistors can be innovation champions.

The bean counters

Another segment of the population that will rise to fight off innovation are the people who wield the letters R-O-I like a light sabre.  If an idea cannot guarantee a specific return on investment in a ludicrously short time frame, the idea must be rejected.  These individuals evaluated nascent ideas on impossibly stringent metrics, that they often don't hold even existing projects and products to.  Their language is about investment and risk, while they miss projects and products that have enormous sunk costs.

Defeating the innovation resistance

It's crucial to anticipate each of these dark forces arising to do battle with your ideas, and to understand who will attack your ideas and how to either win them over or to defeat them.  The most difficult to win over are the people who simply resist change, because these individuals often aren't fighting fair.  Like Sith Lords, they show up in multiple disguises and with unusual weapons, but their ultimate resistance is in their lethargy, their foot-dragging and their disdain for change.  The simple fact is that a corporate bureaucracy will not change if it does not want to, unless it is forced to, or the bureaucracy is changed.

The most difficult battle will be with the product or project leader who feels threatened in the moment by a specific innovation project.  In these instances, the individual or team whose product is threatened by a new innovation will literally "go to the mat" to save their product or project, resisting innovations that make sense.  In this instance, the only way to win is to bring in bigger guns - the executives who will make decisions and prioritize projects.  

The most subtle battle is with the funders - the accountants and financial people who will want to understand the potential return of the investment in innovation versus continuing to invest in a proven commodity that exists.  Here, you will need to turn their tools against them, to do your homework to demonstrate that your idea has financial merit, that the investment pays off in a reasonable timeframe, and that alternative investments aren't as good as yours.  Which means you'll need to get one of them on your side, because you'll need to bring an accountant or financial manager to a finance fight.

There's no Death Star

Unlike Star Wars, the innovation resistance doesn't have a Death Star and isn't really seeking to destroy the concept of innovation.  Instead, the battle is really more of a thousand cuts, constantly questioning the value, the direction, the focus, the support or the need for innovation.  Instead of one climatic meeting, innovators have to be ready with all their tools all the time to meet and overcome the resistance.  Or, be ready with a lot of Jedi mind tricks to get the management team to play along.

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posted by Jeffrey Phillips at 6:46 AM 0 comments

Tuesday, May 23, 2023

You can expect an explosion of innovation in the next 18-24 months from June 2023

 I am going to go out on a limb.  Not a big limb, but any time you make a written prediction that people can come back and check, you put yourself at risk.  My prediction has to do with innovation.  Predicting innovation will seem unusual and risky for many people, because innovation seems so random and difficult to anticipate, like a lightning strike.  But if you understand the conditions that make a lightning strike possible, suddenly the strikes don't seem so surprising.  The same is true with innovation.

Why innovation will flourish in the next two years

Innovation will flourish in the next two years for a couple of reasons.  First, we are going into (perhaps already are in) a recession.  As you look around, you can see many large companies cutting staff, in preparation for leaner times.  People are asking questions about hard and soft landings, about the length of the recession and so on.  History shows that most recessions are relatively short, about 18 months, and the economy often rebounds with great strength.

But I'm not forecasting recessions, I am forecasting innovation.  Another thing we know about recessions is that lots of new companies get created by all the people who are released from larger firms.  People start up businesses and pursue ideas that they would not have done otherwise.  While startup money is tighter in a recession, expect thousands of new companies to be created.  This will lead to new products, services and business models.  To be honest, this isn't a prediction as much as it is a recitation of fact - almost every recession in the last 40 years has led to immense innovation.

There are some other factors to consider that make this time period different, however.  First is artificial intelligence, ML and ChatGPT and all of the new technology that will make people obsolete.  Just as the loom did not replace all workers - in fact created more jobs - AI will not replace all workers but create new roles and new jobs, as well as new products, services and business models.  

Innovation creates and spawns new adjacent jobs as well.  New technologies and new services require new marketing and sales channels and messages, new support structures, new payment mechanisms.  The interwoven layers of technology will create new jobs.  New robots require maintenance techs to ensure the robots are working correctly and programmers to improve how the robots operate.  

Creative destruction

What we are going to witness over the next few years is what Schumpeter called "creative destruction", which means that new ideas will reach the market as new products, services and business models and will destroy existing industries and jobs.  That sounds scary, and it can be, for those unwilling to understand the emerging possibilities.  In every transition from one technology to another, people have rebelled.  The printing press was considered a threat to copyists, the automated loom to weavers.  And the people who held those jobs were right - those jobs were under threat, but other, better jobs emerged.

We are likely to see creativity unfold in ways we've rarely seen previously in these tectonic technology shifts, because we have layers of technology that other technology is built on - computers support the internet, which enables machine learning and artificial intelligence.  We are gaining exponential benefits from previous investments in innovation, going up a learning curve.  That learning curve and its impacts will have difficult implications for some jobs but will create other opportunities as a by-product.

We are likely to see the destruction of jobs and industries as well.  Increasingly, white collar work will become threatened by the power of artificial intelligence, and there could be a resurgence of high-tech blue collar jobs.  Machines can already read x-rays as well as humans, and much more quickly and far less expensively.  Plenty of white collar jobs gathering, analyzing and making sense of data will be at risk.  


There are gaps, however, in this possible future, the biggest of which is the growing gap between those who are dialed in to new technologies, either as the technologists or knowledgeable users, and those who do not have the skills or capacities to understand or harness the strength of digital transformation.  It's becoming critical that we rethink how we educate people and how we introduce them to what is about to occur in technology, so they can be involved either in the creation of new AI and ML, or at least become knowledgeable about how to use and deploy these tools and other manifestations such as robots.  If too many people are left behind, unable to create these technologies and uncomfortable or uncertain about their implementation and use, a widening gap between those who understand and control technology and those on the outside will threaten the stability of the markets.

There is a huge opportunity here, and when opportunities emerge, there is another opportunity for innovation.  We cannot afford to create two classes of citizens, one that has access and understanding of emerging technology, and one that is cut out from any engagement of technology other than as a consumer.  I think we'll see a lot of innovation around education and training people to become more adept at the development of AI and ML, user experience solutions to simplify the interface to AI and ML, and methods to learn to use AI and ML more effectively.

Whole Product

Finally, there is the "whole product" model to consider.  Many of these emerging technologies are used by scientists, technologists and early adopters.  As fans of Geoffrey Moore know, that market represents only 10-15% of the total market - the larger segments are in the early majority.  As the title of his book indicates, these technologies need to "cross the chasm" from technologists and early adopters to the early majority of customers.  What the early majority wants are complete products, easy to use, with plenty of support.  Thus, creating the whole product for AI, ML, robotics and other technologies creates an entirely new industry and valuable opportunity.  AI and ML will need to leave the back office and the lab and become everyday products used by people everywhere, and to do that, those products and technologies will need to consider user experience, support, interface management and a host of other challenges that technologists skip over.  Why do you think there are so many LinkedIn posts about how to create useful prompts for ChatGPT/

Will we see it as an opportunity or a challenge?

Innovation is about to unfold in ways we cannot appreciate, due to the stacking of proven technologies to lead to greater computing advances.  Further, a host of people leaving larger companies will be starting their own companies, creating a range of experiments that will pay out over the latter half of 2023 and into 2024.  Emerging AI, robotics and other technologies will feel like an assault on existing jobs and industries, and a wave of protectionism is likely, but ultimately futile if past is any indication.  We will need to ask ourselves:  is all this change an opportunity or a challenge?

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posted by Jeffrey Phillips at 5:33 AM 0 comments