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Data Science: America’s Hottest Job

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GI Bill is now accepted by the Omaha Data Science Academy

on May 16

Here at Contemporary Analysis we believe good visualization is the key to understanding data and making data-driven decisions from it. We have worked with multiple companies (including nonprofits) over the years to provide valuable visualizations of their data, both at the macro and micro levels, to help them use their data more strategically. While technologically agnostic, we do recommend Tableau for those users who are either new or non-technical. We offer classes on how to use data visualization through our school, the Omaha Data Science Academy.

Read more…


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Good Visualization Example #5,345: Here’s how America uses its land

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GI Bill is now accepted by the Omaha Data Science Academy

on May 16

Data science has been named America’s hottest job by an article written in Bloomberg.

In recent years, there has been an explosion in the amount of available data and an advancement in tools that can tame and harness it. Companies are counting on data scientists to make discoveries within the data, yet there is a major shortage of people who are skilled in this area. The article recounts how this scarcity is causing companies to pay incredibly high wages to attract these sought-after professionals.

 

Programs for aspiring data scientists are difficult to find within traditional institutions because data science has only sprung up in recent years. Nontraditional educational routes such as the Omaha Data Science Academy has tried to fill this gap. Interested in joining the next cohort? Apply now.


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Good Visualization Example #5,345: Here’s how America uses its land

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on May 23
GI Bill

The GI Bill is now accepted by the Omaha Data Science Academy. Veterans, in partnership with the Interface Web School, can now use their GI Bill® to receive relevant tech training at the DSA

We want to help veterans jumpstart their career transition by preparing them with the necessary skills needed for a successful and profitable job in data science. Learn from practicing data scientists and get a leg up on college grads. 

Cohort 1 starts with Introduction to Data Science classes on July 11. Apply now.


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Good Visualization Example #5,345: Here’s how America uses its land

on October 1

Data Science: America’s Hottest Job

on May 23

Reading the full GDPR is probably not your idea of fun.

Your Data Isn’t Ready, and Your Company Might Not Be Either

As of May 25th, all organizations working with the data of EU citizens will need to be GDPR (General Data Protection Regulation) compliant.

Global Data Protection Regulation (GDPR) is the EU’s new data regulation and it applies to everyone who has customers that are citizens in the EU. That means it applies to almost any internet business.

These new regulations may completely change how your business is required to handle user data and sometimes even how you operate.

Your organization could be fined up to 20 Million pounds ($28M US dollars as of today) or 4% of global turnover (whichever is greater), so pay close attention!

Seven New GDPR Requirements

Here’s a quick summary of seven new regulatory requirements and how they might affect you. Before we get started, here are two important terms you need to understand:

Data Controller: Any entity that “controls” the data by deciding the purpose or manner that the data is or will be used.

Data Processor: Any person or group that “processes” (obtains, records, adapts, or holds) the data on behalf of a controller.

1. Consent

When asking users to consent to your terms, you cannot use indecipherable terms or conditions documents that are filled with legalese. As a user, I’m a big fan of this; from a company’s perspective, this can be a gray area. Read into the official documentation (linked at the end of this post) for details.

On top of clarity, you also need to ensure that it’s just as easy for users to withdraw their consent (after giving it, not just when you present it initially) as it is for them to give their consent.

2. Breach Notification

In the event of a data breach, you have to notify any data controllers and processors within 72 hours. If a data controller determines that the breach “is likely to result in a high risk to the rights and freedoms of individuals” then they also have to notify each individual user that was affected.

These notifications must contain at least:

  1. The nature of the personal data breach (number of categories of subjects and records affected).
  2. The Data Protection Officer’s contact information.
  3. Describe the likely consequences of the personal data breach.
  4. Describe how you’re going to address the breach.

Thankfully you are allowed to provide this information in phases if it isn’t available all at once.

3. Right to Access

Your users (or “data subjects”) have the right to obtain a free copy of their personal data. In addition, they have a right to receive a confirmation of their personal data being used or processed.

If you’re wondering what providing “a free copy of their personal data” looks like, check out how Google does it1.

4. Right to Be Forgotten

Users (data subjects) have the right to have their data erased from the data controller “without undue delay” if:

  • The controller doesn’t need the data anymore.
  • The subject uses their “right to object” to the data processing, or withdraws their previous consent, or was a child when the data was collected.
  • There is a legal requirement for the erasure.
  • The controller or processor is processing the data unlawfully.

As always, there are a lot of exceptions here, be sure to read the detailed resources below if this applies to you.

5. Data Portability

Not only do users need to have access to download their data, you should also offer different tools for portability; such as APIs alongside a direct download. Direct downloads should be offered in multiple formats, again, Google is a great example here1.

This could mean that you need to allow a competitor to be able to directly import your data if the user requests it.

Thankfully, you’re not responsible for protecting the data copy that has been received by the user.

6. Privacy by Design

This means you need to be thinking about data protection all the way down to the design of your internal systems.

Privacy by design calls for data protection in infrastructure too, meaning there may even be non-technical changes you need to make to your company structure. Now is a great time to look for vulnerabilities in your internal practices and even consider getting a security audit.

7. Data Protection Officers

Qualified officers have to be appointed in any public authority or large organization (over 250 employees) that monitor or process personal data.

If your company qualifies, you should dive into the qualifications and start looking for an officer right away. These regulations go into effect May 2018.

Conclusion

If you’re doing business with EU citizens it’s in your best interest to get on top of these new regulations as quickly as possible. Hopefully, this article provided you with enough detail to know where to start and what to expect.

GDPR isn’t the only thing that requires thoughtful implementation, check out our recent guide on Best Practices for Implementing Data Science.

Detailed Resources

Citations

  1. Download Your Data – Google Account Help


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Good Visualization Example #5,345: Here’s how America uses its land

on October 1

Data Science: America’s Hottest Job

on May 23

Contemporary Analysis (CAN) and Cabri Group and have teamed up again to use Machine Learning to predict the 2018 NCAA Men’s Basketball Tournament. This is different than last year as we are picking the entire 2018 bracket instead of just upsets.

Historically, only 26% of tournament’s games end in an upset (this includes games from all rounds). That’s 17 out of 64 games. Last year we did really good. Only failing to predict 3 upsets and getting 50% of our predictions right. We are going to need to improve a bunch to win that 1M/year for life from Berkshire Hathaway–including that wee bit about having to work for Berkshire Hathaway to be eligible. This year we added far more variables and used an ensemble model. Will we be perfect? Probably not. Here is the problem with using Machine Learning to try and predict a perfect bracket:

 

A). Error propagates itself through the bracket. This is why the odds of a perfect bracket are around 1:128 billion. If you pick San Diego State to upset Houston-

Side note: The machine learning is in fact, picking Houston by the slimmest of margins. However, if San Diego State wins, the machine learning is actually picking them to go on to beat Michigan, Providence, and then Ohio State to win the entire region.

 

and then Houston actually wins, you will lose the entire region. Perfection may have to do with a 6/11 game that no one would normally care about except its the tournament, and everyone cares about every game.

B). Machine Learning and Predictive Analytics aren’t about being 100% accurate. You wouldn’t want to pay for that kind of accuracy even if it were possible. We are trying to be less wrong for companies. This is why predicting upsets made sense and the whole 2018 NCAA Bracket is so hard. Figuring out who is most likely to be an outlier (churn) is something we do all the time. And, we can error on the side of being wrong. We would just tell you to call both Houston and San Diego State (in this instance) because calling them to talk to them about staying at your company has no ill effect. (i.e. there is very little cost to being wrong in this example.) There is a huge cost to being wrong in the tournament in the later rounds as you are predicting the next game based on your assumption of correctly predicting the last game.

 Without further ado, here is what the Machine Learning algorithm predicted as the bracket:

 

CAN Bracket-pdf

 

If you have questions on this type of analysis or machine learning in general, (or if we are perfect and you would like to congratulate us), please don’t hesitate to contact:

Gordon Summers of Cabri Group (Gordon.Summers@CabriGroup.com), or

Nate Watson at CAN (nate@canworksmart.com).

 

Now for some disclaimers: 

Understand the technique that finds a group of winners (or losers) in 2018 NCAA bracket can be based on any metric. Our analysis isn’t to support gambling, but to open up people’s minds onto the possibilities of leveraging Machine Learning for their businesses. If we can predict things as seemingly complex as a basketball tournament (Something that has never been correctly predicted), then imagine what we could do with your data that drives your decisions

We will be keeping score using the very traditional 1,2,4,8,16 point process. 

 

**Any handicapping sports odds information contained herein is for entertainment purposes only. Neither CAN nor Cabri Group condone using this information to contravene any law or statute; it’s up to you to determine whether gambling is legal in your jurisdiction. This information is not associated with nor is it endorsed by any professional or collegiate league, association or team. Machine Learning can be done by anyone, but is done best with professional guidance.


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Data Science: America’s Hottest Job

on May 23
Contemporary Analysis Awarded Small Business of the Month

Recently, Contemporary Analysis (CAN) was presented with the Greater Omaha Chamber’s Small Business of the Month award. It means a lot to be recognized for the hard work the team has done over the last year improving how companies start and scale data science internally.

The Problem

On the consulting side, CAN has spent most of its 9+ years implementing Data Science in the traditional format: bidding via proposals and statements of work. While we still make bids and builds via proposal, we realized a lot of companies that need data science have a difficult time formulating their need into a written document. They don’t know where to start, what they need, or how to scope time and materials. Not having a statement of work presents a problem for traditional consulting. Even when they did understand how to build a needs document, an outside vendor wasn’t what companies wanted. Their desire was to own their own analytics team. We couldn’t agree more.

Once they had decided to make data science an internal strategy, companies hired a senior-level data scientist. This was due to the fact that the person needed to be all things for the department for a long time until it showed an ROI and would be granted abudget to hire a team. This required the first data scientist to be a programmer, database manager, mathematician, data visualist, data science strategist, and implementation manager. This came with a whole new set of problems. A person experienced enough to do all things is expensive ($150k+ salary), hard to find (time to hire is 6+ months), implementation requires a philosophical change in problem-solving (reactive to proactive), and scale requires a new management process (Agile is ineffective). It is simply too much for one person to be successful.

We realized we had to change how companies implemented data science. They needed a fully functional team inside their company from day one and for a time requiring an outside vendor, but needed to manage the process inside their company for company buy-in and scalability moving forward. A new way of implementation had to be invented.

The Solution

We came up with something different, a method with immediate results and little risk. Instead of hiring senior-level talent out of the gate, use a full team of consultants to help you stand up your group. Then find, hire, and train someone to run the team once it’s already up. This means you get multiple people (with no recruiting and no time to hire) and expertise (understanding how to implement and manage all aspects of the team) immediately on day one – all at a price similar to hiring one senior-level data scientist.

Additionally, there is a benefit of when it’s time to find, hire, and train someone to run the team. Because some of the heavy lifting is being done by the vendor, a person skilled in data science implementation (a data science strategist) can now be hired to run and scale the department. This person is usually much less expensive than a senior data scientist.

We pioneered this thought process at a local bank in Treynor, IA. TS Bank is one of the fastest-growing banks in our area. They reached out to CAN in late 2015 asking how they could be better at predicting what is likely to happen not only in their portfolio but also in marketing, sales, operations, M&A – almost every function of their business. They already had business intelligence but didn’t know how to make the transition from reactive to proactive. That’s when CAN stepped in.

CAN became their data science team for 18 months, deploying 4 data scientists skilled in NoSql, data visualization, coding, and computational modeling. We served as their team until they were able to stand by themselves. Now, just 2 years later, they have their own team of two data scientists, a data strategist, a business intelligence analyst, and a database engineer. TS Bank now has a better data science team than banks five times their size, and they have plans to hire more. With their team, augmented by ours only when needed, TS Bank can make decisions faster and less expensive than their peers. They know when to buy. They know when to sell. They have better risk analysis. Their business intelligence team, now coupled with their predictive analytics team, is the poster child for how to start, grow, and scale data science in an organization. This pilot allowed CAN to better understand how to implement the “Us then You” strategy.

Today CAN offers three approaches to improving outcomes with data science:

  • Data Science as a Service (to get you started)
  • Training (to make it yours), and
  • Staff Augmentation (to keep your need fulfilled, even if that need is temporary).

Data Science as a Service (DSaaS)

CAN begins the process of serving its clients by initially and temporarily serving as their data science team.  Day one, we show up and provide our client with an established data science team that knows exactly what they’re doing, knows how to dig into their data, and knows how to cut through the red tape.

Different than most consultants is the fact that from our first second on the job, there is a timer running. We establish an agreed-upon milestone and, once that milestone is reached, CAN will give you everything you need to have your own data science capabilities: all the data, all the knowledge, no black box, and nothing secret.

Training

About midway through DSaaS, CAN will identify, hire, train, and place a person to run everything CAN is building. While this person can be and often is from outside the organization, sometimes it is an internal person who just needs a few additional skills. When this happens, there is an additional savings of time and money as this person required no hiring process, no internal training of tools, is already a culture fit, and requires no spin-up time figuring out internal politics.

To formalize this training process, CAN built a training curriculum designed to help individuals already in the workforce gain necessary and valuable skills in the four key parts of data science: coding, database, statistics and computational modeling, and data visualization. We call it the Omaha Data Science Academy. While initially only for individuals hired to manage the data science portfolio after CAN has reached the milestone, CAN has opened enrollment to the community so they too can have data scientists for job openings at companies with established data science teams. The Omaha Data Science Academy’s new goal is to train a data scientist for every company in Omaha.

Staff Augmentation

Once established, teams like those at TS Bank aren’t finished building. It took TS Bank 24 months before they felt they had enough talent to cut CAN loose. And even then, CAN still helps out from time to time, providing talent for project work so the company can continue its lean data science team while retaining the talent and expertise to do the high-level or high-speed need projects. Because CAN offers staff augmentation in addition to DSaaS, companies can hire the senior-level talent much further down the road and not fear not have the senior-level thinking to tackle the hard projects as they arise. CAN also offers entry-level data scientists allowing extra staff for those projects that require hours of work, not level of expertise. In this way, CAN closes the loop of need making sure at all points, a company can run a data science team of any size and make noticeable gains from the insights gathered by having a team.

CAN’s new way of data science team implementation lets a company gain access to the decision making of their ability without the fear or risk of single person dependence. It creates better data science much faster with higher ROI than traditional implementation with the helping hand of a company who has done data science for years.

 

Us then You will revolutionize Data Science.

 

Are you ready? Reach out to see how we can help.

 

CAN provides insight to all teams as they grow and develop. We have completed over 150 projects across 100+ companies, and have been the data science teams for 3 companies in the proof of concept stage, 2 in implementation in past years, and 5 more planned for 2018. We have the experience and wisdom necessary to help companies navigate the new kind of management necessary in data science.


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Good Visualization Example #5,345: Here’s how America uses its land

on October 1

Data Science: America’s Hottest Job

on May 23

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.  

 


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Good Visualization Example #5,345: Here’s how America uses its land

on October 1

Data Science: America’s Hottest Job

on May 23

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 . . .




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