CAN recently got back from Tableau’s 2017 customer conference, affectionally shortened “TC17”. I brought back several things from the week in Las Vegas: a couple Tableau tips and tricks, several new connections within the Tableau community, and of course, some swag.

In no particular order, here are the top 5 swag (swags?) I picked up at Tableau Conference 2017.

 

① Interworks Viz Socks

In a world of suit and ties, socks can be a great way to show off some pizazz while remaining professional. I wish I had grabbed 6 more pairs because I can see myself wearing these every day of the week!

 Pluralsight Fidget Cube

That didn’t take long. Within a year of invention, fidget cubes are now officially branded swag. Pluralsight’s fidget cubes are perfect for mindlessly fidgeting around while thinking through your next great visualization.

③ Data Sleep Mask

This was a surprise from the Tableau Partner Summit on Monday. Tableau was kind enough to provide its partners with a TC17 Rally Pack that contained cool, yet practical swag like this to help make sure they were able to fully recover each night of the conference.

④ VizItPhilly Koozie

Shoutout to the VizItPhilly crew (specifically Corey Jones) for supplying me with this neat koozie! Without it, my hands surely would have frozen from holding craft beer cans during Data Night Out.

⑤ “We Are Data People” Pennants

These small felt pennants are perfect for selfies and are a great way of reminding your coworkers that you are in fact a real data person.

 

*Bonus – Mini Speech Bubble Whiteboard

Another item in the Tableau Partner swag bag – a miniature whiteboard version of the large speech bubbles with fun data phrases that are seen in about 80% of Tableau users’ Twitter avatars.

 

Did we miss any top swag from the conference? Let us know in the comments below.

 


Related

Game of Throne Meets Data Science

on July 26

Happy Fourth of July from CAN

on July 4

Nebraska is not California. Omaha is not San Francisco. This sounds glaringly obvious but sometimes entrepreneurs in the Midwest get caught up in proving that we’re the same as any other startup scene in the country. But we’re not… and that’s okay! It doesn’t mean we don’t have big ideas here. It doesn’t mean there’s no tech leadership here. And it certainly doesn’t mean you can’t build a disruptive tech company here. The Silicon Prairie may not have the same quantity of startups as Silicon Valley, but we have just as much quality. You just have to know where to look.

Nebraska has actually been pretty good at creating a startup and building it into a billion dollar company. Did you know that the Omaha company ACI invented the ATM’s software that you still see each time you use an ATM? Despite its spectacular demise, many people forget that Enron was created when the CEO of Northern Natural Gas (an Omaha company) decided to merge his startup company with Houston Natural Gas. Inacom was a Fortune 500 company (albeit briefly) meaning Omaha has had 6 Fortune 500 companies. West, Kiewit, Mutual of Omaha, Solutionary, TD Ameritrade, Sojern–all got their start in Omaha. We just haven’t started one in while. Well, that is changing.  

 

We here at CAN sometimes take for granted all the cool companies and people we work with every day. In our building alone, there is a collaborative incubator filled with fast growing tech startups, multiple small companies, and not one, not two, but three code schools. While we know of this amazing ecosystem, sometimes we forget that a lot of people in Omaha and Nebraska don’t even realize this startup niche even exists.

We view that as an issue.

 

Our community needs to know that Nebraska is a place to brag about. While certainly not the hotbed of entrepreneurialism as Boston, New York, and San Francisco, we do have success stories that we can be proud of. And we think we’re about to have a few more. Because we are always looking for great ideas, we decided to compile a list of Nebraska companies we have come across that have a good chance at fundamentally changing their niche and becoming a huge success.

Why Startups?

First, we need to address “Why Startups?” A startup by definition is a small entrepreneurial business that sort of “pops up” to fit a need in a particular market — often times technologically based. If successful it tends to grow rapidly and is usually founded by forward-thinking and innovative people. Stereotypically, these people are young college grads who are trying to apply their education in a new way, but the truth is that a lot of startups begin by professionals who have worked decades in a field. This means that by definition, a startup is a new idea, built to change the world (or at least their part of it).

The Big Four

Any list of the top startup companies in Nebraska must include the Big Four. They are successful and well known outside of the startup ecosystem. At this point, they are all established in their respective industries and have begun scaling their products. They are (in no particular order):

 

FlywheelFlywheel got started back in 2012 by Dusty Davidson – and is now a premier WordPress hosting company for designers and artists.

 

HudlThe leading software company for coaches and athletes (and doing some amazing data science work), Hudl was founded by David Graff, Brian Kaiser, and John Wirtz in 2006.

 

BuilderTrendBuilderTrend is a residential construction cloud system founded in 2006 by Dan Houghton, Jeff Dugger, and Steve Dugger and is one of the largest non-VC funded software companies in the US.

 

Bulu BoxBulu Box provides you weekly box, filled with cool new samples to try, delivered to your doorstep with an easy online setup. It was founded in 2012 by two UNL grads, Paul Jarrett and Stephanie Jarrett.

 

It’s easy to forget but these companies were all much smaller startups a few years back. They show that with the right product and people you can build a successful tech company in Nebraska. But you didn’t read this far to hear about the companies you already know. Without further ado, here are:

Eleven Nebraska startups that could be the next big thing 

 

KojubaKojuba was the brainchild of James Rolfsen back in 2015. What is Kojuba and what makes it a startup to keep tabs on? We let Rolfsen answer this:

 

“Have you ever worked in a group of some kind and at the end of the project, the wrong people got the credit for doing the work? We all have. This represents what we call an “inefficiency of performance assessment.” The consequence is often that hard working people don’t get recognized for their contributions and that toxic individuals who undermine the team evade detection and sometimes even get promoted. Specifically, the “social contributions” that individuals make to a team are extremely difficult to delineate and almost impossible to quantify. Kojuba solves this problem. We analyze the behaviors and relationships of employees in organizations in order to paint a precise picture of how work gets done. Sound like magic? Fortunately, it is not. (I’m not sure if magic is scalable.) It is a proprietary combination of network science and machine learning that gives us the power to see inside the soul of organizations – and to deliver actionable guidance to our corporate customers.”

 

Ulytic Ulytic was founded in 2015 by Billy Martin, who has an impressive background in marketing. Ulytic is a video processing platform that “allows traffic engineering and data collection firms to quickly upload footage and receive highly active traffic count reports with lightening fast turn around.” No more trucks taking traffic counts by clicker for three hours, one day a year and extrapolating out that info to be the traffic pattern for that corner every day. Now you can capture real numbers for all times of days, for all days–Including things like concerts, football games, school plays, and carnivals. You now can collect real data on events without extrapolation of anything. The funny thing is, they already have the cameras, we are just providing a way to use them instead of a person.

 

LifeLoop “Keeping you in the loop of your elderly family member”. The idea for LifeLoop came from a personal situation of Amy Johnson, who founded the startup in 2015. Their mission is as follows “Our number one goal is to improve the care and overall experience of residents living in senior living communities. they believe this involves creating and fostering great relationships between community staff, residents, and the residents’ families, which results in personalized care and attention.” With calendar management, resident tracking, and a family portal, LifeLoop is certainly living out its mission.

 

Bric Bric is quantifying work. Through capturing data on work, projects, skills, and financials, Bric knows what issues companies are having, the true abilities of their people, and which teams work best together. Today they are using this data to help teams maximize their time and talent; however, in the future, they will use this information to recommend products, services, and provide clients with coaching. They are creating a digital business consultant that knows everything about your business, your industry, and can use this to recommend solutions that work. No longer will you have to rely on a consultant’s personal experience or education — but can learn from your own company and industry.

 

Decision Logic  Ryan Mack is the current CEO of Decision Logic, which began in 1998. Originally designed for Applebee’s, today this restaurant management software serves over 50 brands. The software itself is special because it gives managers a much-needed, one-stop-shop to go see where their money goes, manage the outliers, uncover trends in sales and preparation, and manage staff and ordering. To date, the software has saved its restaurants over $37.5 million in food and labor costs. And that’s just the appetizer (see what I did there?). Over the next few years, they are adding prescriptive analytics and data visualizations that no one currently in the industry has or offers.

 

Quantified Ag Quantified Ag is a little different than our traditional tech startup, but perfect for our eco-system. They are in a new field called Ag-Tech–something Omaha does very well.  We talked directly to the CEO, Vishal Singh. Here’s what he told us about his startup, and the field he works in:

 

“Quantified Ag is a precision livestock analytics company located in Lincoln, NE. Think FitBit for cattle! This makes the beef supply better by providing feedlot workers with the tools to identify sick animals sooner and more accurately. Through early detection, Quantified Ag’s technology helps dramatically reduce costs by lowering re-treatment rate and death loss and getting animals back to peak performance faster.

 

“One area that often gets overlooked [in tech] is agriculture. Which is ironic because this industry is one of the most important things that our state does and does very well.  On that note, I feel fortunate that my company is working on a transformative idea in beef livestock production.  According to the NE Beef Council, “it’s the state’s largest industry and the engine that powers the state’s economy.” – http://www.nebeef.org/the-beef-story/state-national-facts.  Our state also ranks as one of the top beef cattle producers in our country and exports beef beyond our borders.”

 

Dynamo At the peak of their careers with a Fortune 500 company, Michelle Wingard and Brody Deren left their careers as regular recruiters to develop something completely different–a new way to recruit and place that focused on quality over quantity. Their mission is to not fill a role with a body that matches a check box, but instead to match openings with people that match each other. Their goal is to get it right the first time. One placement, done. Dynamo is succeeding in discovering news ways of expanding the tech field with a focus on better instead of more.

 

Gazella Wifi Eric Burns started Gazella Wifi in 2015 as an automated marketing tool for restaurants and businesses–think fishbowl filled with cards, but without the cards, and without the fishbowl. When customers log in to use the store’s wifi, Gazella is able to capture valuable customer information and provide it back to the owner for use in marketing and sales. Easy to use and customize, Gazella has helped countless restaurants grow their social media reach and customer marketing lists. Their next trick is to provide feedback to the business owner as to which customer would react best to which offer–rewarding behavior and driving sales in areas the store owner wants.

 

SOLVEStephanie Sands of SOLVE spoke to us about her startup’s unique platform. She says: “SOLVE is changing the way companies develop and implement their “people strategy.” The most successful companies recognize that investing in their employees will boost their bottom line. Handing out gift cards, buying a ping pong table, or changing the dress code won’t suffice anymore and especially with the new generation of workers. (Centennials, not Millennials). The key is understanding your workforce, and SOLVE can help you get there. With a background in Organizational Psychology, our team helps companies understand human behavior in the workplace using theory, research, and data to inform best practices. We partner with companies to increase the accuracy of hiring decisions, develop effective leaders, and create great cultures to engage and retain top performers. We also help ensure that those strategies are aligned, consistent, and connected to their culture. Our team provides results-focused services/tools and ongoing, customized support to ensure people problems are SOLVED.”

 

Median – Median is the newest company on the list, founded only last month (June 2017), but its two founders, Ben Stevinson and Derek Homann are excited about their new venture. Median is a customer service platform specifically designed to make real time support chat as fast as possible. It has built-in custom screen sharing technology that doesn’t require end users to install any special or clunky software. It makes service easy for agents as they can immediately get on the same page as the people they’re helping, instead of wasting time either trying to explain a screen to a customer or a customer downloading a piece of software so they can see their screen. It fundamentally changes the conversation of research and trying to understand what someone is telling you to one of diagnosis and problem-solving. As a beta tester, this one is truly helpful and a much-needed addition to anyone’s toolbox.

 

Retail Aware – Retail Aware is a joint collaboration of three already successful entrepreneurs Preston Badeer and Keith Fix. They are capitalizing on the newly forming IOT space by providing a new way to collect and use data in the retail space. Especially designed for owners of multiple locations (think franchisees), it gives them a way to see and understand previously untrackable data. Using sensors owners can test marketing effectiveness of store layout, product placement, and new store experiences. They can even A/B test in different stores. The results are given by the minute and displayed in well-designed dashboards.

 

As you might have guessed, based on what we do, these companies have a common thread. They are all using data to change their niche. It comes in many forms: BI, IOT, Predictive Analytics, Prescriptive Analytics, Machine Learning, and AI. These are just the tip of the iceberg. There are others, many others. Companies like: ScoutSheet, SkyVu, Kiai, and Rodeo Analytics are all on the brink of this list. (perhaps we should have another list next year) All of them are companies that are revolutionizing their niche and are run by bleeding edge leaders focused on using data and tech to update how we do business. In the future, these will be the companies to watch out for and our eleven will be the big companies everyone will have heard of. But, until then, you heard their names here first.

 

Now go and make sure you brag about these companies to all of your non-Nebraskan friends.  

 


Related

Eleven Nebraska startups that could be the next big thing

on July 28

Happy Fourth of July from CAN

on July 4

Sometimes obsession breads genius. Fans of Game of Thrones have dedicated much time to tracking the deaths, births, twists, and turns of the previous seasons. Now that season 7 has arrived, there are some amazing maps of the story out there. We found one we particularly liked on Tableau Public.

Check it out “Games of Thrones Interactive Death Viz” by David Murphy. Select a character and see how they died, who killed them, and what the circumstances were. Turn it into a game and test your friend’s knowledge too. There may be a few more to add before the season is over . . .



Our staff augmentation model proves that CAN believes in building a data analytics team from within. Our goal is to get a data scientists in every company in Omaha. We want to add value to every business team by training a data scientist with the latest tools of the industry and on the ground field experience.

We found an article called “How to Build a Data-Driven Culture: Develop From Within” on Data Science Central and thought that it reflected a lot of what CAN believes. Check it out and let us know what you think in the comments below.


Related

A Celebration of CAN’s Best Ideas

on February 11

The Advantage of Hiring an Hourly Data Scientist

on January 18

Did you know CAN’s blog is full of sound data science related advice dating back to the beginning of CAN? In case you didn’t, we make it a habit to regularly re-post our favorites. What follows are reasons why you should consider becoming a data scientist. If it grabs you – check out the Omaha Data Science Academy. It might be the first step in your data science career.

Why Become a Data Scientist?

Too few of today’s college students realize they want to be data scientists when they graduate. We believe that data scientists are the future, and that we are on edge of a data science revolution. Therefore, we decided to explain why to become a Data Scientist.

1. As a data scientist, you have incredible access across the business.

Your job of modeling specific business strategies and forecasts requires you to have broad access across your company. People look to you to bridge the gap between business theory and relevant data.

This is a tough role because it requires you to develop and implement a strategy to create consensus in order to implement the results of your work. Since the days of the English Luddites (the anti-technology loom weavers) there have been people who are against technological progress and the efficiency it brings to the economy. The best data scientists will be able to manage the political and social change that comes from their work. Data Science success isn’t only about making work more productive, it is also about helping other people adjust and succeed.

2. Being a Data Scientist is a specialized field.

The requirements to be a data scientist are long, because the decisions they make impact thousands of people. Data scientists usually have a 3.5 GPA or higher. They must have the ability to learn and share several different forms of knowledge, including principles of computer science, high business acumen, and complex math. Learn more about how to become a data scientist.

3. You have the opportunity to work with top level management extremely early in your career.

While this sounds great, it is also challenging. You need to be comfortable giving board room presentations to people who don’t understand what you’re talking about. A specific aspect of your position is to clearly articulate why your results are useful and valid — and do it without math speak. Learn more about presenting business intelligence.  

4. The best data scientists never settle, and question everything.

Whereas a statistician starts with a data set and a problem, a data scientist has a more difficult task. A really great data scientist will constantly ask, “are we solving the right problem?” Often the perceived problem won’t match your data, requiring you to look at everything from a new perspective.

A data scientist spends his energy asking machines questions and then trying to validate the answers, instead of spending energy trying to address the question directly. This requires a different work process, one that requires humility and understanding. Data scientist know while they are the ‘go to’ person in the organization, they don’t have all the answers. However, at the end of the day they are still responsible for finding the answers, which is why they get paid the big bucks. Data Science is a complex science as opposed to a simple science. 

5. You use artificial intelligence to automate the most routine, frustrating jobs known to mankind.

Instead of doing routine tasks, you can be responsible for automating the most tedious aspects of business, while saving your customers money and making enterprise more efficient.

If you are interested in learning more about Data Science and Predictive Analytics, download our free eBook — Predictive Analytics: The Future of Business Intelligence.




We at CAN have a pretty impressive staff of data scientists. They’ve all got their quirks, that’s for sure. Some like to run in their spare time, some bird watch, some binge watch House of Cards. When it comes to work, however, they have something in common. They work hard, around the clock if they have to, to accomplish projects and put their best foot forward for clients.

Something they don’t agree on when it comes to work is the age old Python v. R debate. Yes, in our free time, besides healthy normal hobbies, we also have discussions about computer programming languages.

So, today, we put it into words. Python v. R — which one is for you?

If you’re completely new to the computer programming discussion

Webopedia defines computer programming language as “A vocabulary and set of grammatical rules for instructing a computer to perform specific tasks.” How does one talk to computers? In code. It’s gets tricky, however, because there are a lot of different codes that computers can understand. There’s not just 10, 20, or 30 different computer languages that exist. There are hundreds and hundreds of languages. You can browse a full list here. Python and R are just two of the most popular.

For some additional help, we’ve compiled a list of terms that will help you understand the background of this topic (inspired by LinkedIn).

Programmatic thinking. It’s exactly what it sounds like. It’s a way of thinking that you have to turn on when you learn computer programming. It means seeing the large problem as a series of smaller steps. It also requires being able to transcribe ideas into a code that computers understand.

Compiled and interpreted languages. Compiled languages require the user to compile and build code before it can run. Interpreted languages can read code directly without compiling.

API. API stands for application programming interface. Basically, it’s instructions put out by the program designers for accessing the full functions of the language and softwares.

Pseudocode. It’s like code, but not. It’s shorthand for standard code and helps programmers with outlining before they dig into bigger coding tasks.

Armed with a few definitions, let’s jump into the debate.

Python v. R: Where to Start

First, we’re going to hit at the hard truth. In order to succeed in the data science world, you need to be familiar with both languages (or at least good at one and familiar with the other). Particularly in Omaha, where CAN is headquartered and data analyst jobs are highly competitive, knowledge of both languages gives you a leg up on the competition.

But that’s not what you want to hear, we know that. So we’re still going to break the two down and tear them apart in comparison.

Both Python and R are good at . . .

Python and R are both free to download, and the learning curve is about the same once you’ve already mastered some basic programming skills. They’re both impressive to master, so in that way you can’t go wrong. No one will shame you for mastering one and not the other.

Python Positives

Python is know for data munging, data wrangling, website scraping, web app building, and data engineering.

Let’s say you’re tackling a project with a lot of disparate data. Maybe you’re collecting sales data from the past 5 years for a company to help them predict new trends. The problem is that the company has had several turns in management, and that data is stored in multiple locations. Python would be more helpful in this situation. It succeeds as a software for gathering data from many databases and making it one.

If you already know Java or C, Python is going to come more naturally for you. The similarities coincide for your benefit.

It is an object-oriented programming language (see above), so it’s easy to write large scale and robust code. And, some people say there is data to prove that more business owners are looking for those proficient in Python over other languages.

Positives of R

R has better visualization tools than Python. It’s also been around a lot longer, which means there are more online support communities than Python (think: APIs). There are over 5,000 softwares you can find on the internet to run alongside R to boost its capabilities.

R is known for being great at statistical modeling, graphing, and converting math to code.

Perhaps you’re working on a project for a company that has a nice and neat database. The problem is, it’s difficult for most people to look at a bunch of numbers and understand trends. R is the most helpful for these situations, as it can successfully take data and make it into graphs and pictures for others to understand it.

Let’s talk to CAN

In attempt to settle this debate, we’ve brought in some professional opinions.

Matt Hoover, Director of Data Visualization: Matt sees R used as a more efficient math language, emphasis on the word “math”. It can achieve in one line of code what Python needs several lines to accomplish. R’s speciality is research, statistics, and data analysis, so it’s more efficient on the stats side. He continues, “Python is way more flexible as a language overall and can be used to do a wider range of things.” Matt sees R used in more learning settings than on the field, and sees Python used for more high level data science.

Essentially, R is easier to learn and better on the math/statistics side, but overall Python has more capabilities.

Gordon Summers, Senior Data Scientist: Gordon’s advice is a bit more far reaching. He says, “The hardest thing about picking between Python and R isn’t choosing which one to start learning, it is in choosing when it is time to stop learning it”. Basically, Gordon’s advice is to not focus so much on which language to master, but instead realize that something new could come along at any time, so don’t invest too much time in one.

In summation

If you work consistently with clean data, and your goal is to dissect the data and creative visualizations from it, go with R. If you have messy data that you need to “wrangle,” Python is more helpful.

Still stuck? Answer the following questions to help you navigate the Python v. R world.

 

  • What are your teammates using? Maybe you just got a job in data science and can’t decide which one to learn. Look around – what are you friends and fellow employees using? Are they successful in their work?
  • What are the data trends of you job market? It wouldn’t be inappropriate for you to call up a company who just posted a data science job and ask what they would prefer. Get a feel more the market, decide from there.
  • Whose data are you working with? Is the data messy and needs to be gathered? Python is your answer. Is your data clean and needs to be visualized? Go with R.

 

You can’t go wrong

Neither Python nor R is perfect. Both will have downfalls, but but there are packages that exist to help alleviate those pains. Examples of libraries that can help alleviate problems can be found at https://elitedatascience.com/r-vs-python-for-data-science.

To summarize more thoughts by Gordon Summers, the IT world is changing. He says, “To do development is to use the application and to use the application is to do development. There is no IT person and no business user. The person is both developer and business user. One of the reasons that larger organization have struggled to embrace Python and R is that frequently there is an organizational barrier between IT and Business.” When you enter the programming language, data science, or IT world, be ready to to be flexible. Businesses are still struggling to figure out where IT fits in their company. The best advice is to be adaptable.

Oh, and not to complicate the entire argument, but about the time we get the R v Python debate settled, Scala might just come from the back of the pack to win the whole thing. After all, Twitter is in part written in Scala and Hadoop choose to write Spark in Scala.  Social Media Speed and Big Data Prowess? Perhaps this dark horse isn’t the long shot after all.


Related

Eleven Nebraska startups that could be the next big thing

on July 28

Game of Throne Meets Data Science

on July 26

Tableau is a data visualization software that CAN uses daily with our customers. We even have our own Tableau expert on staff: Matt Hoover.

But Tableau isn’t just for those who pursue predictive analytics, like us. Tableau is a really awesome tool for anyone who has interesting data: bird watchers, marathon runners, dog walkers, etc. It takes data and makes it look pretty, so anyone can understand it.

It’s fascinating what people can do with Tableau. Are you hooked on the Tableau world? We found a cool article on Data Science Central by Kenneth Black called “Top 10 Bloggers Writing About Tableau”. We’ve re-published that top 10 list below for the Tableau lovers who read our blog.

Tableau bloggers to check out:

Companies:

Individuals:

If you find some helpful or interesting information on one of these Tableau blogs, comment below! We’d love to hear it.


Related

Eleven Nebraska startups that could be the next big thing

on July 28

Game of Throne Meets Data Science

on July 26

History is a fascination for us at CAN for two reasons. The first is that we find our own history pretty fascinating. Did you know that CAN has been around for 9 years? Pretty cool.

The second reason is that we want the world to know that predictive analytics isn’t a new field of the 21st century. It’s been around for a long, long time in some form or another. Intrigued? Check out a piece we wrote back in 2013: “The History of Predictive Analytics: Since 1689”.

This post is part of our “From the CAN Vault” series that highlights some of the gems of our blog from the past 9 years. These articles are written by current staff but also members of our alumni network. This week’s throwback was written by Tadd Wood, who was a data scientist at CAN for 7 years and now lives in Silicon Valley.


Related

Eleven Nebraska startups that could be the next big thing

on July 28

Game of Throne Meets Data Science

on July 26

The iPhone versus Galaxy debate. There just doesn’t seem to be a clear way to compare them. Until now.

We found a data visualization on Tableau Public by Sarah Lewin that breaks down the two smartphones so buyers can make an educated choice, or just finally understand their different capabilities. Check out “The Smartphone Breakdown” here. Scroll over blocks for more details.

Tableau is a data visualization software that data scientists at CAN use daily. In fact, we even have a Tableau expert on our team — Matt Hoover. If you have any questions or ideas about Tableau, talk to Matt at matt@canworksmart.com.

Tableau Public is an extension of Tableau, where the public posts their projects for all to see, be amazed at, and enjoy.



Looking for something?