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Justin Trowbridge

Business Development Officer

justin.trowbridge@canworksmart.com

Justin has been innovating sales and marketing for over 19 years. Having worked for small startups and fortune 100 companies alike, his experiences help him understand client needs.

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*ominous music begins to play*

The year is 2056 and all of humanity has been enslaved by robots…ok that’s enough of that.

Hollywood has done a lot to help educate the public about technology. Artificial Intelligence (AI) seems to be the one that is “scariest” to humanity because of all the ways it can go wrong

So lets start there with de-mystifying AI and why it’s not going to try and conquer the world without you. Artificial Intelligence is a program that gets smarter every time you run it. If you’ve ever seen a Roomba robotic vacuum…everyone would assume that is AI. It is not. The robot is simply running the program: if you hit a wall, change direction. If you hit another wall, change direction again. When full or when low on power, find docking station with sensors. Repeat until obsolete with newer model found in Skymall.

Behold, a weaponized Roomba!

If you’ve ever owned one of these, (non-weaponized Roomba) you know they randomly attempt to clean your floor. They don’t run 1000 cycles and are significantly better on the 1001 cycle versus the first. If a Roomba were Artificial Intelligence, it would “learn” every time it ran a cycle. If the robot was running that kind of programming it would learn where your furniture is and never run into objects. It would also find the optimal pattern to cover 100% of the floor over time

Until now we’ve not used the term Machine Learning (ML). We did that on purpose to help make a clear delineation of what ML consists of. Oddly enough, ML and AI get confused all the time, even among technologists. Most examples of AI is just machine learning done really well.

Everyone’s friends, and unofficial wiretapping devices, Siri and Alexa, are not AI. They are glorified Roombas running a scripted program. So how where do you draw the “line in the sand” between machine learning and artificial intelligence?


Machine Learning: Machine learning is a program that looks for patterns and provides recommendations for those patterns. It’s up to the human being to test out the recommendations to see which one is the best option.


Artificial Intelligence: AI is a much more complicated decision making program than machine learning. It has multiple decisions it could/can make when presented with a scenario. Not only does it have the ability to make multiple decisions, in most cases, it remembers what it did previous and looks for ways to improve using those different decisions.


So let’s put this into an example people can understand: a Roomba robot vacuum. Your Roomba is using machine learning to figure out how to cover a 100 sq foot room with furniture. It bounces around and eventually will clean the entire floor. In the process its hit some items multiple times and essentially uses “dumb luck” to complete the task.

Imagine coming home and your Roomba has made crop circles in your living room carpet!

Now, you rip out the computer chip and install AI because that’s how you roll. (you rebel you) Your Roomba now starts running random patterns and hits a table leg. It then circles the table leg, mapping out its location, and then moves on. After dozens or even hundreds of cycles like that it starts running parallel lines like its mowing grass, missing every table leg in the room. It then changes to diagonal lines, then circles, then a spiral square, then herringbone pattern. Eventually, the AI learns the most efficient pattern to clean your floor, all while avoiding furniture legs, and still gets close enough to clean the corners and avoid cords.

Your Roomba has taken basic programming concept of “run until hit wall, turn left” to “find the most efficient way to become full without running out of power or hitting furniture”.

The interesting thing is the line between a high function machine learning solution and a low level AI is tough to discern. As consultants we’ve seen a lot of AI solutions that are just machine learning.

There’s a bartender joke that applies here. “The difference between a rum and coke and a Cuba Libra? …$1.50 and a lime.” There is a significant cost difference between machine learning and AI. There’s also more marketability of AI over machine learning.

So, looking back at the Data Hierarchy we’ve already covered and how you get to AI. Your incoming data Reporting is comprehensive and all inclusive, it’s not being filtered or scrubbed. Your Business Intelligence is able to report the real time analytics from your data. Descriptive Data is diving deeper into the why behind your BI. Predictive Data is narrowing the “what if” scenarios down to a manageable number of options. Prescriptive Data is running up and down the data looking for how to optimize the outcomes to your desired results. And now *drum roll* you flip the switch on Artificial Intelligence and it does everything else for you…right? Wrong

AI takes all the data results you’ve had previously and learns your business. It learns what happens when you move any given variable. It learns what kind of outcomes you are looking for. It is the eccentric and task obsessed employee everyone needs but never finds. It learns how to make your business better 24/7 and it isn’t afraid to fail.

The “scary” AI from Hollywood is autonomous and/or sentient–meaning it comes up with a solution and executes it on its own. It inevitably determines humans are virus, they need bio electricity to themselves, humans become batteries, Neo is the one, and he can’t die because Trinity loves him. Hollywood AI is just that–Hollywood.

As it turns out, AI isn’t scary. AI is the super helpful employee nobody knows about. AI, when done right, will recommend well thought out data supported options for human evaluation and action.

Instead of HAL, from 2001 a Space Odyssey, asking (ominously) “What are you doing Dave?”. HAL says “Dave, you should buy blue teddy bears in bulk in October. Red teddy bears should never be bought in bulk and only in January. If you do those two things you’ll earn an extra $127,438.43 this year.”

The real kicker with AI is how important the preceding Data Hierarchy elements are to have a smart, successful recommender. If you skip steps, If your data isn’t accurate, if you haven’t had multiple iterations of your team correcting interpretations by your Machine Learning, your AI will be worthless. It will simply learn “bad habits” and suggest things that don’t make sense or work for that matter. The morale of the story is, you probably could skip steps–but don’t. There is no shortcut to AI. All the more important to start now.

If you wait until your competitor has AI, and your not already at ML, your doomed. You are just the next bit of dirt the Roomba is coming to pick up. Make your decision to start up the hierarchy now. The Roomba is on its way and it won’t slow down just because there is a table leg in its way…


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I didn’t want to say it…but using the last levels Predictive Data analysis, we were able to predict you’d want to read this one too.  Obviously that’s a joke, but prediction is pretty powerful when its correct.

So you can now predict the future with your data because you’ve followed the Data Hierarchy steps.  Congratulations, you’re still not the coolest kid on the block.

Predictions are more accurate “what if” type of scenarios.  You as a person have to make the decision on how to get there.  This is actually the difference between levels 6 and 7 which we will get to in a second.

Prescriptive Data is the “what should we do about it” phase.  (for example) Your Predictive model spit out 5 highly likely outcomes.  Your team all agree they would be great if you could make them a reality.  Prescriptive Data takes the same data set and looks for adjustments and/or variables to help achieve those outcomes.  Prescriptive Data gives you the actionable items that increase the likelihood of your predictions even more.

Prime example of a moment gone wrong for Judge Smails.

Anybody who’s ever hit a golf ball knows there are dozens of split seconds in a golf swing that equate to hitting the ball correctly.  Get them all right, and you’ll beat Tiger Woods. He’s good, but he’s not perfect. Get 2 or 3 of those moments wrong, and you’ll probably end up in the woods at the worst possible time.

In business your “golf swing” is how you execute decisions.  Lets say your biggest competitor is the Tiger Woods of your industry.  They get 15 out of 20 of the little things right almost all the time. Pretty good, can’t compete with them right?  Wrong! They’re still missing 20% of the things that make them good.

Prescriptive Data looks at your swing.  Because you’ve used the previous steps you know you are doing 13 out of the 20 things right.  Prescriptive Data runs hypothetical “tweaks” to your swing at every point with every variable until it can get 17 out of 20.  (for example)

If you’ve ever hit this shot, that inch feels like miles.

Your data will tell you at any given point in your “swing” if you do X then Y will happen more often.  And its proven by the historic data you have on 1000’s of swings before. Tiger Woods won the 2019 Masters by 1 stroke…1 stroke.  Xander Schauffele, the guy who LOST by 1 stroke, wasn’t beaten by a vastly superior golfer…he was beaten by a detail (or more) being wrong in 2 swings.  A foot that was ¼ inch out of place, a shoulder that dipped ⅛ of an inch too low, and worst of all…he probably hit those shots and could feel it wasn’t perfect.

Hundreds of people leave cable companies every year. For what? A myriad of reasons. Better price, free HBO, wireless boxes, faster internet, Game of Thrones is finally over, and more. The ironic thing is that most people leave a cable company to get a feature the current company could have sold them if they

(1) Knew they were going to leave (predictive analytics)

(2) knew what they were leaving for most likely (descriptive analytics)

(3) knew what to sell them to keep them–free HBO on a phone call to see how they liked their cable (prescriptive data).

It’s only with Prescriptive Analytics that the whole hierarchy starts to make sense (and pay off).

**Next weeks post will be on July 3rd to accommodate the holiday**


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If you’ve enjoyed learning about what your data can do for you looking backward, you might want to sit down for this one.  You probably are already, so lets just say you were standing then.

Level 4 is Predictive Data.  Predictive Data takes the information learned in the previous steps and applies a “what if?” thought process.  If Levels 1-3 were reactive, Levels 4-7 are Proactive. Proactive is knowing what’s going to happen before it happens and it takes a completely new way of thinking to apply it to business. If you’ve ever played with a magic 8 ball as a child, this isn’t that kind of prediction

Prediction is looking at the variables that produce an outcome.  If X then Y then Z kind of relationships. The difference is prediction runs through the entire alphabet and double letters for every variable If A then Y then Z?  If B then Y then R? Then runs through it again in near infinite combinations of if AB then Y then Z? What about if both A and T both happen, then Z? The human equivalent of “throwing it at the wall and seeing what sticks” but thousands of times over and over.

Lets Review:

Your Reporting is giving the machine the raw data.  

Business Intelligence is setting a baseline of “known outcomes”.  

Descriptive Data is helping set the framework of what kinds of trends you should expect, and  Predictive Data is taking what is already known and testing “what if” beyond what you could imagine

Why don’t they make customizable Magic 8 Balls?

Back to the teddy bears for a second…because who doesn’t like this analogy?  So you know there is a spike around Christmas and February for teddy bears. Great, now you also know there is an initial rush followed by lesser activity until the panic buyers kick in.  You’ve proven this with your data and have 4 years of historic data to support it

Using Predictive Data you begin to look at other variables connected to the data.  Is there a higher number of men vs women buying the bears, and is it tied to one or the other holidays?  Is there an age group that is more likely to buy a bear? Why did we pick teddy bears for this example? Does the color of the bear make any difference? Does it even have to be a bear?

The machine looks at the data, takes into account all of the Descriptive Data, and begins to tie that data to trends and patterns that are happening currently. This gives you the ability to predict the sales volume and inventory requirements for next week (for staffing), next quarter (for sales quota setting), and next year (for inventory forecasting). So your Descriptive Data provides your Predictive Data meaningful information for the example below.   (totally fictitious numbers


Men between the ages of 20-35 are 75% more likely to buy blue bears in December.  


Having 10% more blue bears in November than the rest of the year will cover demand.


Women between the ages of 35-45 are 45% more likely to buy a red bear in February.


Having 5% more blue bears in January than the rest of the year will cover this demand.

Now we know how many to order, when, and how many people are needed to staff selling them.

With Descriptive Data you were able to make market adjustments for when to market and to whom. With Predictive Data you can laser focus your marketing to exactly who you want to sell your goods to and know how many you are going to sell. When you’re that accurate you have a higher return on investment (ROI) than you would if you just “guessed” with your marketing efforts


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How would you describe your data?  That is a phrase you’ll never hear outside of a Data Scientist cocktail party.  But it’s an important question.

Descriptive Data, or “Why did that happen?” is where the past 2 levels really starts to sing.  Now that your reporting supports business intelligence you can start to dig even deeper

If only drilling down on data were this easy! The good news is there is less mud to deal with when working with data.

Digging deeper with data sounds intimidating but it doesn’t have to be.  Your BI level information works well for short term understanding and is almost a real time indicator of your business.  Learning WHY it happened factors in variables that would never cross your mind.

So we used the teddy bear example of business intelligence.  You can correlate data points to see beyond the obvious. Descriptive Data takes that even further by looking at the data correlations you might miss and brings them to your attention

“I Wuv you as much as I wuv Big Data”

For example:  Using our Teddy Bear company, your reporting and BI indicate there is a spike in sales around February and then again around December.  It shouldn’t come as a shock that these are connected to the holidays. If that is a shock, you might want to get out of the teddy bear business.

Descriptive Data looks deeper into the data and notices things that seem obvious but aren’t always clear.  In this case it tells you that dads and husbands buy the bears in February and that mothers buy bears in December.

Not the most groundbreaking discovery either, that is pretty common sense for those holidays.  However, it also tells you that its dads under 40 but over 27 who are buying the bears. Its moms 20-35 who are buying the bears later in the year.  This insight translates to better accuracy with sales and marketing efforts.

Machines don’t find this kind of work tedious or boring, like most of us would qualify it.  Because its a machine you can run these kinds of reports all day every day. To do this by hand would take weeks or months.  A machine can do it in minutes.


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Grille mounted heat seeking missiles weren’t covered under the warranty from Q Branch.

“Business Intelligence” as a term always has some interesting visuals in people’s minds.  If you instantly flashed to James Bond or Maxwell Smart…you’re our kind of people. *high five*

Business Intelligence is looking at your viable data and deciphering “what is happening right now?”.  Knowing what is happening helps you repeat success and avoid failure in the future

A great analogy for Business Intelligence (BI for the cool kids) is the car.  Your business is your car, its constantly generating data. The miles per gallon calculation, the miles to your next oil change, tire pressure and so on.  The BI part of your car is the dashboard. You get real time feedback from the car on things like RPM and your speed. You also get neat little lights when you’ve broken something.  Tire gets low? There’s a light. Oil needs a change? There’s a light. Engine detonated at 7000 RPM? There’s a lot of smoke, maybe some fire, and then a light.

Ok, so you understand what BI is on a broad spectrum.  That should be easy to spot with your reporting right?  …not so fast. Somethings are easy to spot in data and others are buried deep in the bowels of your data set.

He just wuv’s data so much…yes he does.

For example:  If you’re a toy company selling teddy bears you would understand why you see spikes in your data around Valentines Day and Christmas.  People buy more teddy bears around that time of year. Mystery solved. Business Intelligence takes that a step further and shows you the real time data.  Its being broken down and put into a format you can make decisions with. Because you can see your business running to plan there are no “lights” on your dash.  If something is out of the ordinary then your BI solution would let you know instantly you need to fix something.

So now you know you’ve got a correlation in your data that wasn’t what you thought.  Thank you BI for proving I don’t understand my business right? Wrong. Using BI you can now formulate marketing efforts to lengthen that time frame for purchase, hold off on pricing reductions until after your busy time to keep your margin’s high, increase your inventory a week earlier to prevent early outages, and more

You can now see deeper into your business than ever before because you know what just happened.  The more accurate the data you input into your reporting now, the more impressive the results will be as you get further down the line in Data Hierarchy


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Reporting is the first step on this journey with Data Hierarchy for several reasons.  Reporting is the “what happened?” part of looking at your business.

If a company doesn’t track and understand “what happened” then how would they implement Artificial Intelligence to give them suggestions on data?  Better yet…how do you file your taxes without some kind of basic reporting? If you’re going to start making data driven decisions, you need to have data…good data.

Reporting takes on all kinds of forms.  The key is to have it in a format that is viable.  Notice the word “viable”? That was intentional, the definition of Viable is as follows:

Viable:  vi·a·ble /ˈvīəb(ə)l / adjective “capable of working successfully; feasible.”

If your reporting is a color coded spreadsheet in excel with abbreviations for fields that only make sense to you and the other person who uses it…you barely have reporting.  Arguably color coding is as effective as a wall covered in handwritten post it notes.  (please don’t be “that guy”)

Sure, we’ve got reporting! Let me go grab it off my desk. ~ “That guy”

Remember, the long term goal is to have your data work for you, not you work for your data.  So your reporting data needs to be in a format that is easily understood by others and computers alike.

There is no shortage of software to make reporting work.  The number one problem people run into is cross platform reporting.  Which can be easier to fix than you think.

If you’ve got one platform for sales, another for orders, and yet another for payments…oh and the one for accounting.  You may have given up trying to find a single system that does it all.  Don’t worry, you’re not alone.

You’re actually in the majority with that problem.  So much so other companies saw a need to fix that, and they did!  These platforms take some skill to connect them but that’s part of the setup process.

Once you have connected all your data inputs into a Viable data set you can do amazing things.

Batman doesn’t like gibberish in your data.

Also is your data accurate?  Do you have a system that requires a data field to be entered every single time?  Do you have a (lazy) sales person who is putting “asjkdjkldfshjkfadsjkhasdfjklhsdf” in a vital field out of frustration and lack of understanding?

Educating your team(s) that data, good information or bad information, being accurate is vital to being able to fix problems for everyone.  If your data is all smoke and mirrors you’ll never be able to do anything of value with it.

Going forward you’ll be able to quickly jump back to the previous segments by following the hyperlinks below.


Related

CAN Spring Sale

on April 1

Data Science: America’s Hottest Job

on May 23, 2018

At Contemporary Analysis we do Data Science Consulting.  A large number of the people reading this just said “huh?” and that’s fine…that’s what this is for.

Data Science is in integral part of everyday life at this point and you just don’t know it.  As a society we’re generating more data than ever before. Smart businesses are tapping into that data to do things that were previously unheard of.

Take Facebook for example.  20 years ago Facebook didn’t exist, now people are addicted to it and seemingly can’t live without it.  But even then, people are still weary of the dreaded “Facebook algorithm” that cuts 50% of the posts you might want to see.  That algorithm is data science at work

That’s right, you’ve generated enough data that Facebook wrote some code to cut 50% of your friends out of your life.  You didn’t interact with them enough, they didn’t post enough, there are hundreds of reasons why that system feels like your college roommates buddy from down the hall with the cat doesn’t need to be at the top of your feed.  It also looks at what you read on a regular basis and then tries to predict what you would want to read next.

So to help people truly understand what we do as a company, and to help you hire us.  (let’s be honest) We put together a series on the sophistication of data usage as businesses mature that we call the Business Data Hierarchy.  The goal of this series is to help people and companies understand where they are now, and where they could go with data driven decision making.

We’ve written the series to be informative and insightful, with a splash of humor mixed in to keep you awake through the whole process.  If you like it or if you feel like someone needs to read this…we ask that you share the info or…better yet…get them in touch with us and we’ll bring the show to you!  The pyrotechnic guys tell us we’ll need a 25’ ceiling for the fire and lasers…Hey, it’s a good show.

…this will also be the longest post of the entire series, don’t worry!

When you look at Data, and what it can do for you and your company, there are six different levels of Data Hierarchy.  It’s a hierarchy because each level is codependent on another.

These levels are important to understand because jumping from one to another, without a long term goal, can be cost prohibitive.  This is even more devastating when you finally get your executive level to believe in the power of data, and it breaks the bank in the execution.

“Skipping” leads to “Skippers”

There are consultants with lovely summer and winter homes who have paid for them “skipping” to the end and then back billing/building the solutions.

To insulate against catastrophic failure of a data-driven initiative we at Contemporary Analysis (CAN) have created a Data Hierarchy to help companies understand where they are and more importantly, where they are going. This understanding helps drive the strategy and vision needed to be successful.  These levels are

(CAN) have created a Data Hierarchy to help companies understand where they are and more importantly, where they are going. This understanding helps drive the strategy and vision needed to be successful.  These levels are:

  1. Reporting:  Tracking and “What happened?”
  2. Business Intelligence:  “What just happened?”
  3. Descriptive Data:  “Why did that happen?”
  4. Predictive Data:  “What is going to happen next?”
  5. Prescriptive Data:  “What should we do to make it happen?”
  6. Artificial Intelligence:  “Automated recommendations”
  7. Omnipotent AI (Skynet): “Automated Doing of its own recommendations” a.k.a. “Terminator Movies”

Every business is trying to move “forward”.  If you work for a company whose response is anything but “forward” or “more” start polishing up your resume, you’ll need it sooner than later.

Most companies are so focused on today’s business they don’t know what the path to the future looks like.  

Imagine you tell a CEO you’re going to walk a mile to get another 1 million in sales.  Most CEO’s would look at the distance and agree that a short distance is worth the time and effort to get the additional revenue.  

The sprint to 1 million

You and your team(s) work feverishly to get from point A to point B as quickly as possible.  You cross the finish line and there’s your 1 million. The CEO checks the box and there it is, project complete.

Now imagine if you told a CEO you’re going to get 20 million in sales.  After the confused look and possible laughing subsides you tell them how.  Instead of a mile, you have to walk 15 miles. But you’re not going to do them all in 1 year.  Instead you’re going to walk that distance over 5-6 years. You’ll measure success with each mile you pass and each mile will result in ROI for the company.

The road to 20 million or more.

You also let them know that you can cover the ground when and how you want to.  If one mile is too tough to work in the time and effort this year, you postpone it to the next.  If, as you’re walking, a business need changes and you need to walk a completely different direction you can.  The steps remain the same but the road you use to get there is slightly different.

Understanding the long term goal allows you and your team(s) the ability to work smarter not harder.  You’re building toward the vision at every turn so you have little to no wasted effort. And, because you’re building over time, you can staff accordingly for each mile and access the right talent at the right time

Part of CAN’s role is being that “Data Visionary” that helps you see over the horizon with possibilities.  The hardest part of this whole process is getting the decision makers in an organization to embrace the culture of change.

“We’ve done it this way for __X__ years and it works just fine.”  Is becoming the leading indicator of a dying business. If you’re 40 years old the technology available today wasn’t even conceptualized when you were in grade school.  “We’ve done it this way for 50 years…” means you’re already behind the curve.

The posts that will follow will walk you through each level of the Business Data Hierarchy concept.  We’ll be sure to include examples that are relatable. The subject matter can be a bit dry, so we’ll also make sure we include some humor along the way to keep things lively.  We’re a Data Science Consulting firm..not monsters after all.

Posts will be made to our blog and to our LinkedIn page. We encourage you to share, re-post, forward, email, and contact us if you have questions at any time.



Nate Watson, president of Contemporary Analysis (CAN), talks about the Omaha Data Science Academy (ODSA). Gain some insights into why the ODSA exists and challenges companies face with Data Science

Nate Watson talking about Omaha Data Science Academ
Space Nate wants you in our next Cohort!

Want to learn more about the ODSA? No problem, just click the button below.



As some of you know, last week Nate presented at InfoTec. The subject was “How to hire and manage Data Scientists”. Since then we’ve had multiple requests for copies of his presentation.

Nate talking to “Spaceman Tom”

Ask and we deliver. Below you’ll find the PDF version of his slide deck.

Don’t forget we run the Omaha Data Science Academy and can help train Data Scientists! We’re an advocate for the Data Science community and love to be a resource for so many great people.

Let us know what we can do to help you and your organization today!

He couldn’t help himself…could you?

Joe Sova, President of Ideal Payroll Service, hosts a podcast that helps businesses make more informed decisions.

Recently Joe interviewed our own Nate Watson about making data driven decisions. Listen to the full podcast through the link below:

Get more great content from Joe from their site. www.joesova.biz/



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