Why I Love Pokémon GO

By Source (WP:NFCC#4), Fair use, https://en.wikipedia.org/w/index.php?curid=47798657

 

Pokémon GO is changing our civilization and I love it.   The game itself is slightly amusing at best, but addictive in the same vein as Angry Birds.  Simple enough to get started, but challenging enough to continue playing.  Themes always help entertainment mediums grow – Disney being a great example.

Overview

An overview of the game (in case you’ve been vacationing in an underground bunker for the past week or so) can be found on Wikipedia.  In summary, it is an augmented reality game that makes use of your mobile phone’s camera to give you the illusion of capturing digital creatures in your everyday surroundings.  It uses GPS to make this a constantly varying activity with points of interaction and competition called “Poké Stops” and “gyms”.

So what makes Pokémon GO different?  As readers will have noticed in the past, I am a believer that data is useless without people.  It takes a human element to interpret and fully utilize technology of any kind.   Pokemon GO builds the human element.   The gyspy-29535_640ms and Poké Stops require a physical human presence.  This is part of the design.  What is likely far beyond the design intent is the relationships that it has created around and through our mobile devices.

Spies like us

We’ve seen for years the trend towards mobile device use in public.  We walk around eyes to the screen, texting, tweeting, snap chatting – all with someone connected via a virtual tether.  Here’s what is different:  when is the last time you stopped a stranger at the park and read your text message out loud?  Told them of your next Facebook post?  Yet I’ve seen and personally experienced intelligence sharing on par with the Rebel Alliance to capture digital critters.

People talk to each other.  When email and video chat first became popular it opened communication dramatically across the world.  Mobile devices were different – they had the same tools but really allowed you to ONLY talk to the people with whom you’d established relationships. You could filter out casual social contact.  Honestly, I’m not objecting to this most of the time.  You feel more connected to your existing network.  What is lost (and regained via Pokémon) is interaction on a casual basis with other humans.

The Result

Data is about people. Technology is for people.  Pokémon is serving as a brilliant reminder of those facts to our rather isolationist society.  I hope it continues.

 

 


Article Copyright ©2016 Bright Beach Consulting

 

Pokémon Logo By Source (WP:NFCC#4), Fair use, https://en.wikipedia.org/w/index.php?curid=47798657

Top 5 Ways to Fail at Project Management

Project management is a precise blend of art, science, caffeine, and ibuprofen.  Too little of any of these, and your project will suffer.  Many business guides focus on driving change.  This is a great thing on a macro level – but for those of us in the trenches, we must focus first on accepting what we cannot change.  Bounding a project with a charter or problem statement is a common and helpful tool, but doesn’t tell the complete story.  Today I am going to focus on critical assumptions to make when working projects in corporate America.

 Many people have an idealistic or textbook view of project management.  That paradigm says business leaders are inherently rational, and if you only present the DATA in the right way, you will have all the time and resources you will need.  Data is important to solve problems – specifically making sure you are solving the RIGHT problem.  It does not turn water into wine.  Here are my top 5 assumptions about project management.  If you forget any of these – you are likely to fail. Or reset, pivot, or some other euphemism for “try again.”  Remember them – and you can gain valuable insights into your project.  Note for each failure mode, there is a message in green text.  Accept the reality and move on!error-101409_640

  1. Resources.  You will not have enough qualified people on your team.  If you add more qualified people, their ideas and enthusiasm will drive scope.  If you add unqualified people – same thing.  It is a never ending cycle.  Learn to work with the challenge.

  2. Priorities.  Competing priorities will impact your ability to schedule work. Sometimes they are business priorities. Someone else’s pet project, or a more near term deliverable.  Sometimes they are personal priorities – life events like children, vacations, or removing the remnants of a winter storm from their driveway.  Accept that no one is 100% available.

  3. Requirements.  They will change – either from a lack of original understanding, a technology change, or sometimes just a better idea.  Don’t resist changing requirements.  Holding fast to an outdated requirement will usually cause more lost time in the end than adapting quickly and moving on.

  4. Deadlines.  The timeline will not be long enough.  Similar to qualified people.  If the timeline increases, so will the scope.  This is where sprints or some other time-boxing methodology is useful.  Instead of failing to deliver, compartmentalize delivery so you always have progress.

  5. Politics.  Don’t forget, corporations are people too.  Everyone has an agenda.  This is not a bad thing – treat agendas as a leading indicator for an avatar or persona – it helps you understand how to effectively manage.  A much bigger problem is to pretend that we don’t have agendas.

Bright Beach Consulting has a project management approach that helps promote success.  If you would like more information about the Six Step process that we use which actively accounts for these challenges, you can sign up here.

 

Alien abduction and project management

ufo-1379888_1280“Wouldn’t it really suck to get abducted by aliens in a foreign country? They wouldn’t even know what language to use when they probed you.”

These words of wisdom were spoken by a companion during a drive on French country roads in the wee hours of the morning. This is really quite a revealing phrase; both of the underlying psychological issues of my companion, and of what we as modern citizens consider really “foreign”.

Let’s set aside the fascination with alien probing, and look at our perceptions of foreign.  While it may technically mean a different country – a physical, cultural, or political boundary – I don’t believe that’s how most of us view it.  If you’re scheduling a trip to tour the Guinness brewery in Ireland, you’re much more likely to call it an international trip, or an overseas trip.  Foreign has a connotation of something we don’t understand.  Language illustrates this.  You might consider French a foreign language, but a visit to Paris an international trip.  Why the distinction?  I think the basic difference is that you know how to find Paris, but not speak French.  You have the ability to understand the context around the geography but not the language.

Understanding

extraterrestrial-1287037_1280This is where alien abduction meets project management.  You don’t know what you don’t know.  Poor Zlarp the Alien Project Planner has an impeccable plan.  He’s identified a perfect area outside of Paris, optimal times to avoid radar detection, and briefed his team on communication methods using French.  Instead, by bad luck he ends up with Americans.  What did his project plan say?  How many potential languages could his abductees speak? This is a variable that is not accounted for in his plan – and realistically cannot be.  We see the same things in our project plans – variables that just weren’t even imagined.  Long-service employees leaving, technological advancements, changes in the marketplace – all things that cannot be predicted.  I say cannot be – but truly it is that they should not be predicted.  Analysis paralysis is what can occur if we try to capture all of these variables.  Inability to move forward because we just don’t know enough.  That occurs often with traditional project planning due to the appeal of things like Six Sigma or PMP, they pretend that if you’re good enough, all can be controlled.

Some methodologies talk about trusting the judgement of the people (agile) or pivoting based on new information (lean start-up).  Regardless of this most companies want adherence to a plan.  “We’ll work it out and adjust accordingly” isn’t acceptable to most finance departments.  There needs to be scope and controls on the process.

Failing according to plan

How does Zlarp keep his commander happy?  Assume that Zlarp has some kind of critical path map that shows successful communication as a milestone.  He can’t just drop you off, and go pick someone else up . . .he’s spent time, money, and flying saucer fuel to get you.  Pivoting may sound good logically, but won’t look good on his mission performance review.  He should have thought of potential foreign visitors.  It will definitely be held against him.  So he juices up the probe anyway – and your lack of ability to understand French results in the average intelligence of humans being underestimated for the next 200 years.

This is the result of a highly structured and politicized organization.  Many of us have experience in this environment.  There is NO methodology that can save you once you’ve reached this point of the process.   And – most methodologies take you right down this path.  Sound depressing?  Here is the solution: negotiate outcomes.

Outcomes

Not tollgates, milestones, sprints or any of the other semi-useful mechanisms for project control.  If you are in Zlarp’s position and getting your project defined, focus on outcomes and boundaries.   For example – our alien friends don’t really care about individual probes – they want data.  Task based methodologies would quantify the number and cost of tasks in some fashion.  If you focus on outcomes you look at the data requirement alone.  This gives Zlarp the freedom to quickly drop us back in the wilds of the French countryside and move on to a more opportune target that may have more data.   Even better – if the first three visits were very successful, he could move on to another species not repeating a task just because the plan said to do so.

The challenge here is management acceptance.  Program plans are not designed for success.  They are designed to deflect blame and show due diligence in case something fails.  It takes leaders and project managers with a high risk tolerance to step outside their comfort zone and focus on what really matters – the outcome.

French nobility, crepes, and data science in 1700

An excellent grasp of data analytics determined who got to watch King Louis XIV eat dinner more than 200 years before the first programmable computer.
I had the privilege of visiting the Palace of Versailles last week. The architectural achievement, majestic beauty, and historical importance certainly was visible to me — just like any normal, non-data-geek visitor. So now my more geeky of readers must be thinking — but of course, you noticed the fireplace and thought of The Doctor. No, that’s not it either. Well, not entirely.versailles

Structure drives flow

What I noticed was the design of the structure and how it supported the flow of information. French nobility had the basics of life under control. Food, water, shelter — accounted for by the labors of others. The information that was useful to nobility was where they ranked in the constantly shifting hierarchy, and what the people a step above them were doing. This resulted in a complete lack of privacy for those at the top as constant observation was the key mechanism for data gathering and a strictly regulated schedule was necessary for all players to participate in the routines.
Think of your last visit to a nice restaurant. If you had a reservation, you probably made it online or at least via telephone. The host has an iPad or computer where scheduling is managed. Now think of coordinating all of the guests without knowing who may come, at what time, and dealing with a strict protocol about who would be seated first, closest to other guests and such. What a mess. This is what the palace staff at Versailles managed impeccably on a daily basis.

Decide what is important

How? By focusing on only important data elements. Jumping back to 2016 — if you made a reservation online, there was likely extraneous information that you were required to provide — some for the reservation service, some for marketing purposes, some just because the system may be sub-optimal. Let’s take your email address as an example. There is a corresponding physical address used in the 18th century — but I am confident in saying it wasn’t included on dinner placards. What mattered was rank. The design of the buildings routed people to the right places. Think of it as a funnel. Various corridors serviced the purpose of educating the visitor of the importance of who they were about to see, his achievements, and based on the location of the visitor (which level of apartment or dining) the king or higher nobility could judge the importance of the guest. Physical location drove assumptions about data elements.
This is a tactic that we should see more of today. We greatly tax the patience of our business partners and customers by asking them to fill out form after form — collecting data for the sake of future use. Think like a French noble. What do you really need to know, and how can you know it? Be efficient. While digital technology is a great tool — it shouldn’t stop us from thinking about the analog world. If you wouldn’t take the time to track the data in an analog fashion, you should ask yourself if you really need it. What problem does it solve for you?

Royal Stalking

There really was a room designed for the king to be observed while he at dinner, and the other nobles would provide running commentary among themselves. That’s a rather creepy level of data gathering.
crepes

And the crepes — they were just what I ate for lunch and quite memorable.

Melted Dead Dinosaurs or Super Heated Sand?

Dishwasher full and running — I need a cold drink after working in the yard. My wife offers me one of the kids’ plastic cups — I choose a half-size juice glass telling her I prefer super heated sand over melted dead dinosaurs.

Married readers will be quite familiar with the look she gave me.

It did get a laugh (eventually) and a suggestion that it was a great article title. So here it is. This kind of labeling is fundamental to data analysis. What you call something influences your perception. I won’t get into the current political circus, but you can see where names and labels in politics drive opinions and emotions across the spectrum. Democrat or Republican — Liberal or Conservative. These cause people to react viscerally, instantly disagreeing with or defending the viewpoint. Some percentage of people overcome that reaction, examine the data more closely, then confirm or adjust their position. As a data analyst we don’t have the luxury of letting people get a second look. Emotion can stomp on our most carefully crafted reports. That emotion causes a lack of trust in the data, and without trust there is no value, or action.

Vulcan Clarity

 Do we strip all emotion from our analysis? Use only titles like “Column 1” or “Attribute Z”. Absolutely not. We construct the data to convey selected emotions. Not bias, or an intent to sway our audience -but to convey confidence, trust, and reliability. We do that by using emotion — and ADMITTING IT. Show the origin of our labeling. Be clear about methodology.

A Better Name

Let’s look at my title: melted dead dinosaurs or super heated sand. Clear bias in labeling. I wanted the glass, so labeled it such. This could have been: colorfully formed specialty cups or well-stirred burned dirt. Much different messaging. Put this in the context of an analysis.

Report Showing Lack of demand for dead dinosaur products

100% of sweaty husbands in the study prefer super heated sand over the alternative.

Knowing the story, you can see it is ridiculous. It is also entirely factual. To present this professionally, there are a few key points we could change.

  1. State the purpose of the study.
  2. Indicate your population.
  3. Show your yardstick (Measurement Systems Analysis).
  4. Use consistent names for data elements.
  5. Be honest about gaps.

Report on Consumer Demand for Types of Drinking Vessels

Produced by National Glassblowers Association

Study Population: 1

Data obtained via verbal survey. No repeatability or reliability tested.

Test products: Plastic 12 oz drinking cup and Glass 8 oz drinking cup.

Study inconclusive due to MSA and statistically insignificant population.

Why it matters

Repeat business. Bottom line, you might make a single customer happy with the marketing spin on the first report but not only will he not trust your integrity, you’re not building a reputation that will drive additional business your direction. Here’s the key: offer next steps. The study was not sufficient. Clear, factual, but so what? The business owner can’t act upon that. Your report needs to identify actionable next steps. Something like: suggest 10 study groups comprised of 15 people each, ages 28–35, male, ethnic and income diversity to reflect population of southern Miami. Even better — give a cost estimate and a time line. Biased data should not drive biased decisions. . . it should drive better data.

For the record, the kids chew on the edges of the melted dead dinosaur cups and it really grosses me out. No offense intended T.Rex.

 

Warm hugs or cold data?

For those of us that have been involved in studying business processes for a long time, we’ve seen the pendulum swing back and forth between abstract data driven decision making to a humanistic approach where opinions are valued, people are validated, and warm fuzzy hugs matter.

Conflicting Guidance

Study after study shows each of these as successful. Or a failure. Really depending only on which one you’re attempting at the moment. Why the confusion? The study authors (or book sellers) will tell you about tremendous numbers of variables or the changing culture of the millennials. Things that are accepted as truth and very difficult to disprove.

My experience as a business leader and data analyst tells me that they’re both right — and both wrong. Data matters. It matters very much -but ONLY when it is related to people.

Let’s look at an example. Churchgoers. Depending on your choice of denomination you might argue the warm hugs/cold data side of the equation, but still a very important data element tracked by churches is attendance. Another is weekly donations. Most churches have a sign visible and are proud showing these numbers.

You might try to draw a correlation between the number of attendees and the donations. For short periods of time, there will probably be a correlation. Taken as a whole, it is NOT a good indication of the health of the church. It doesn’t address the people. The data alone is not enough. Which people attend matters. How they FEEL about attending matters. For churchgoers in the industrialized world a significant increase in their individual donation would have a negligible impact on their personal finances. The National Center for Charitable Statistics shows that for people earning under $200,000 per year, they give from 2.6% to 4.0% of their income . . . in descending order. That is — the higher earners give less. Why does this matter to us? It shows room for growth in giving. Another interesting item noted by the The Center on Philanthropy (COP) at Indiana University was that for about half the population, giving increased around the holidays. So when people have more of their own expenses — they also give more. Financial pressure clearly not the root cause. Finding the sweet spot is a combination of people and data. Why don’t people give more?

Anecdotal data has been given a bad name by analysts. I think it is key to good judgement. Taking the church example — I could track and trend every possible item like weather, sermon topic, regional economy, and try to correlate to attendance and donations. Not a great use of time. A better approach is to look for the variations in the data that you do track then apply it to people by talking to them. Be direct. Attendance was down for two weeks — start calling people and ask how they felt about the sermon three weeks ago. Use the data to ask the people. This does multiple positive things: it gains engagement in a solution – lets them know you care; it is much cheaper than additional data gathering; and it creates a spotlight effect that causes some improvement WITHOUT additional action.

Most of us don’t run churches, which makes this a great abstraction. Now let’s apply it to our own businesses. Say you run a call center and average time per call has increased (usually not a positive trend). Option 1: berate employees. Option 2: Gather and analyze multiple variables and look for correlation/causation — extremely difficult with any degree of reliability as I’ll discuss another time. Option 3: Engage key team members (not managers) and ask them for input on causes and solutions. No process mapping activities or death by PowerPoint justification for existence — but direct questions of people.

The same approach can apply to your customers. Sales are down. You have some data as a starting point. Ask customers. Don’t try to be cagey or shrewd. Direct, honest questions with a sincere interest will get you the additional data that you need to make good decisions about people.

Cold data should only be a pointer — a place to start when deciding what questions to ask. People are the key to a complete understanding.

Data is the beginning of a solution, not the end.

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Deceptive Facts

“There is nothing more deceptive than an obvious fact.”
― Sherlock Holmes

What is a fact?

Dictionary definitions essentially say it is information supported by data; something that is true; objective.  We have previously looked at the importance of perspective in gathering and using data – calling “facts” into great question.  Even without the dictionary, those of us with children understand this perspective.  “He started it” is a accusatory mantra that even the great detective Holmes would find frustrating.  So when we are trying to plow through the landscape of business tools and understand a good process for improving our results – what do we do?  If there isn’t reliable factual information, do we just guess?  Use instinct or personal preference?  Recommendations or social media?  None of these are great options.  They are all dependent on someone else’s experiences.  What worked for them, at a given point in time, with specific market conditions MAY NOT work for you.  Hard to believe, right?  The internet is filled with sites proclaiming “if you do it my way, you’ll get rich.”  Not likely.  sherlock-holmes-147255_640

Facts that change

Good mentors, advisers, consultants – whatever the terminology won’t give you a path to follow.   They will provide tools for you to find your own path.  Think of this example.  Henry Ford built cars.  Lots of cars.  He industrialized the assembly line, basically created the middle class with stable, good paying jobs, and would give you a car in any color as long as it was black.  There is a great legacy in the US from the Ford Motor Company and his creations.  If you were Elon Musk (cool thought), and building Tesla cars, does following the manufacturing practices of Henry Ford make sense?  Is that a successful path?  Why not?  Ford was working with factual information.  Simple answer: Facts are specific to their environment.

Extreme example, you weight 100 kg today.  Go to the moon.  You weight 16.5 kg.  You change environment, the facts change.   Ford was well known for paying factory workers enough to create a market for his cars.  For Tesla or Ferrari – not a great idea.  Increasing pay scales to that extreme would make them unprofitable.  No one even suggests they attempt it, but still many “business gurus” sell that same philosophy of “do what I did.”  It is a failing proposition.

Finding your own facts

We should learn lessons from success (and failure).  Look for tools – not answers.  Environment is both a place and point in time.  Ford evaluated the marketplace, understood his product and manufacturing, his employees, and his customers.  He built a very specific plan that worked in that environment.  So how is that different from following a path?   You should not evaluate the SAME variables.  You need a data collection strategy to find out what’s important to you and your customers.  Build a model of your environment, then map out your own path to success.  If you need help, let us know.  Comment below with specific scenarios and we’ll be glad to discuss.

 

The Result of Failure

thomas-alva-edison-67763_1280I haven’t failed. I’ve just found 10,000 ways it won’t work. – Thomas Edison


Failure in 2016

We have a modern national culture that despises failure. That’s not too strong a statement today. Everything from the health care insurance nightmare to ability to rent an apartment depends on a low risk approach to life and finances.  Participation ribbons and trophies for every team show how much we dislike failure.   Everyone’s a winner!

Why is this? Certainly it is not the case historically – one can see the pride with which we chronicle the business failures of Abraham Lincoln in textbooks and children’s stories. There was a time that failing and trying again was respected.  Even through the early part of the 20th century, our great inventors and industrialists were comfortable with failure being part of the road to eventual success.


An inventor fails 999 times, and if he succeeds once, he’s in. He treats his failures simply as practice shots.  – Charles Kettering


Path to Success

There are commonalities in the great historical failures.   Those who achieved greatness did not fail in a random fashion.  They had a plan – and modified it as data and situations changed.  Look at the example of the Wright Brothers.  Many of their contemporaries were better funded and more experienced, yet they were successful in creating their aircraft.  Why?  Certainly they were smart, and worked hard – but that wasn’t what differentiated them.  Collection of data and modification of their approach was critical.  They studied the field (their competition), and conducted experiment after experiment.  Historical collections such as those at Carillon Park or Wright Cycle Co show models they created, miniature wind tunnels they invented to test their theories, and notebook after notebook filled with systemic observations.invention-60529_640

Similar approaches can be seen with Charles Kettering and Thomas Edison.   Kettering frequently commented on the struggle in teaching students to fail with purpose – gaining knowledge with each step.  Edison inspired the creation of the first corporate R&D center at GE.  Clear application of the scientific method to business and engineering problems.

Failing Better Today

These great figures in history were able to apply themselves completely to the task at hand.  They were committed to solving problems and through great effort collected data and tested solutions.  This is a model that you can follow.  Don’t be afraid of failure and you can stand out from the masses!  Find your business problem.  Determine how to test your theory.  There are many resources depending on the nature of your business.  Anything from A/B testing for web sites to Six Sigma quality tools might be useful.  A business analyst comes armed with a sophisticated toolbox and is a great option, but you don’t need to wait!  You can start with a notebook and the scientific method.  The general steps are provided for reference below.  Just remember  – a failure in the scientific method is still a success.  You’ve proven what doesn’t work, and narrowed the field of possible choices.  Good luck, and great failures!


Failure is always an option.  – Adam Savage, Mythbusters


Scientific Method

  1. Define a question (business problem)
  2. Gather information and resources (observe – check out existing knowledge or competitive landscape)
  3. Form an explanatory hypothesis – what do you think is the right answer?
  4. Test the hypothesis by performing an experiment and collecting data in a reproducible manner. Limit the scope and impact!  Small steps.
  5. Analyze the data
  6. Interpret the data and determine steps forward.

 

The U in Usability

receipt

Usability is often confused with graphic design.  Clear, clean design can certainly aid usability – but isn’t the core.  Usability is essentially providing the most efficient way to accomplish a task once it has been taught.   Note the bold text.  Usability doesn’t mean absolutely intuitive – just efficient.  Take the iPod as an example of beautiful design and great usability.  Not intuitive.  Takes a few attempts to figure out various functions – no blinking lights or automated wizards to take you down a path – however, once you understand the functionality it is simple, easy, and fast.  So let’s assume you don’t design electronics or software for a living.  Why does it matter to you?

The same tools we apply to usability of software can be applied to any process.  Imagine you have an auto repair business, and each customer invoice takes an extra 2 minutes to complete due to extraneous boxes, non-sequential layout, unclear instructions and the like.  You have 2 customers an hour.  Thirty-two minutes a day of your time not billable.  One hundred and sixty minutes a week.  You have given up 6.5% of your revenue for one of your employees because of a form.   Take the next step – if this extra task is interaction with your customers – you are costing them time and money as well.  That is the perception your customer will have – that you are wasting their time.  Not the impression most of us would like to leave.

So what’s the solution?  A business analyst can assist with detailed evaluation of your data and mapping of your processes.  A great thing, and something Bright Beach Consulting can provide.  But that’s a longer term solution.  What can you do today, on your own?

 

  1. Only collect information that you can and will use.  There is a tendency to make complex forms – just in case.  In case of what?  Organ donation?  Building a team for the Zombie Apocalypse?  The information you need will vary based on your business.  Renting an apartment, finding a nanny for your kids – you want more data.  Retail services like a repair center, salon, 2 day clown college – not necessary to know very much about the customer.
  2. Think sequentially.  A great example of this is an address.  You wouldn’t ask for the Zip code first – that’s not how the customer stores or presents the information.  Yet, I’ve personally seen forms that require County before Zip.  Slows down the process – and is probably both unnecessary and redundant with the address and Zip.
  3. Group like items.  If you have to move up and down a form, or have more than 3 columns (boxes across) you are adding too much mental processing to the exercise.  If you want to save paper – focus on only collecting essential information, not adding boxes.
  4. Audit yourself.  Go look at the last 20 or 30 forms you’ve completed.  Where did text get crossed out or erased?  Where are the blanks – fields to eliminate.  Likewise – what extra information is written in Comments or across the page – you may need to redefine fields.
  5. Finally – Ask Why.  Inefficiencies are in every process.  The key to improvement is not accepting them, but beating them into submission.  Condition yourself to ask “why did I do that”?  You’ll find asking why drives more improvement than any other technique.

We would love to hear from you!  Did any of these techniques work well for your business?  Is there something we can do to help?  Comment on this article, or contact us.

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