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The Omaha Data Science Academy

on June 22

Spreading the Good Word about Predictive Analytics

on April 1
https://jjmarketing.co.uk/wp-content/uploads/2015/07/Dilemma_755x350-wide-3.jpg

Investing in employees means more than just treating them well by giving them benefits and a flexible schedule. It means putting time and resources into individuals who have potential for greatness, but may need a little guidance.

 

More and more companies find it strategic to invest in their employees, even if it means allowing them to move on to greener pastures when the time comes. These companies care about the personal and career growth of their individual employees. They don’t back away from new talent who may need a little training. They see opportunity in new hires, unlike most who are afraid to waste money on training.

 

One such company is Contemporary Analysis (CAN). CAN provides predictive analytics to businesses with needs to unlock patterns in their data. President Nate Watson committed to investing in employees from the beginning. The result of his investments is the substantive alumni network of CAN.

 

Alumni networks are the metaphorical badges of honor for companies committed to their employees’ growth. Employees may move on to other businesses or start their own companies, but they continue to maintain positive relationships with companies like CAN who invested so much in their future. Past employees reach out to the network for advice, giving them a makeshift peer network even when they are the only data scientist in their company.

 

Watson comments, “By investing in our employees’ future, we get people not only willing to go the extra mile for us, but access to employees who have the tenacity to figure out how to solve problems. We lose really good talent, but a lot of the contracts we have right now are from companies that have our former employees in them.”

 

The opening comic portrays the decision to invest in employees perfectly: CFO: “What if we train them and they leave?” CEO: “What if we don’t…and they stay?” Companies like CAN know that whether employees stay or leave, resources are not wasted.

 

CAN doesn’t wait for the most highly trained analysts to walk through the door. CAN nurtures potential with patience for greatness.

 

Some companies make the switch to investing in their employees years after they begin operations. For CAN, commitment to training employees is embedded in their DNA.

 

Nine years ago, the same people who rely on data analysis to keep their companies moving today had never even thought of hiring a data analyst. This was during the recession. As a result, a lot of creative people with experience in data science were out of work.

 

Grant Stanley is the former CEO of CAN. Stanley saw the small pool of highly driven, intelligent, but under employed people as something special–something with which he could start a company.  

 

He then built CAN, the now 9 year old predictive analytics company. Stanley and his non-traditional co-workers would approach companies like Mutual of Omaha or West and say, “Give us the hardest problem you have and let us have a crack at solving it.” Many times they would get an opportunity because a solution was already tried by the “regulars” and failed. Giving CAN a chance provided no risk and very little outlay of cash.

 

Stanley is now CEO of Bric, a software company originally designed for small companies that now helps Fortune 500 companies plan and project manage using predictive analytics.

 

CAN approached the solution differently even from day one. Many times it felt like crash a course in learning the models necessary to succeed. However, CAN’s employees were already good at learning. They looked for solutions, sometimes in other verticals and industries, and applied that knowledge back to the original problem.

 

They found success even though they were younger and less funded than some of their well known competition. This problem solving ideology has become a cornerstone of how CAN does business, even to this day.

 

After years of solving difficult problem after difficult problem, the young data scientists were well trained. They now had resumes to qualify for more prestigious positions, even CEO or management positions. CAN learned to cope with employees moving on. They started an alumni network to capture the excitement of the “graduation” of the employees. The alumni network now boasts 15 former employees in 13 companies.

 

Nate Watson maintains the same mindset of investing in employees for CAN today.

 

If you look at CAN now, Watson has changed very little. Yes, CAN today has more resources and more consistent work, but their motto still reflects their passion to make businesses better: “Empower the great to build something greater.” This is not only true for how they work with clients, but also how they treat their employees. They aren’t afraid to smile and wave goodbye as their best employees seek other opportunities. That’s why they have such a strong alumni network.

 

In fact, last month CAN announced the start of the Omaha Data Science Academy (Oma-DSA), the ultimate goal of which is to train and place a data scientists in every company in Omaha. Their goal is not to replace the four year degree, but provide training for those who need an extra push before they become entry-level data scientists.The DSA’s motto of “Building Smarter Talent” likens back to Watson and CAN’s original mantra. 

 

Further on down the road, of course, there will be an alumni network for the Data Science Academy. Watson comments, “We want each cohort to be able to connect every cohort as they move between companies and up in each company. Having that peer network is going to be key to the success of graduates.”

 

Watson hopes that the Oma-DSA will inspire even more businesses to invest in their employees.

 

–The Oma-DSA’s Alpha class starts September 19th–the graduation party, already RSVP’d by some of the most forward thinking companies in the surrounding area, will be on December 8th.

 

CAN isn’t the only business with an impressive alumni network. Strong alumni networks like CAN’s, however, do seem unique to the tech world.

 

Aron Filbert at Lyconic is proof of this. Lyconic provides software designed to improve security guard management. Lyconic’s products are proven to increase accountability and decrease turnover rates among guards. 

 

Filbert needed a talented software developer to create Lyconic. However, he knew that he could not compete with the corporate world in pay. He gave his employees other perks to make up for this, like casual dress, time off, a lax work schedule, fluidity in moving up at Lyconic, and so on.

 

These other perks, in particular Filbert’s commitment to train and grow with his employees, helped Lyconic build a strong alumni network. Filbert’s greatest success story is a man named Carl Zulauf. He worked for Lyconic for a little over one year, gaining valuable experience alongside Filbert.

 

He moved on to a start-up company in New York City where he was compensated very well.

 

Filbert speaks of Zulauf with pride, not resentment. This is what makes Lyconic and CAN’s alumni network unique to growing tech world: they believe in each other, and that leads to success for everyone. Most businesses only care about the success of their company, not the growth of individuals.

 

Three significant results of having an alumni network.

 

Companies use their alumni networks in different ways. Since the beginning, CAN noted three positive and long-lasting results for their alumni network.

 

The physical result: CAN’s alumni network includes some of the biggest names in Omaha as well as some of the most promising startups. Names like HDR, Avantas, TD Ameritrade, Kiewit, Flywheel, and even Ebay are sporting former CAN employees. As well as 4 founders of data science software companies: Eric Burns at GazellaWifi, Luis Lopez at Crumb, Grant Stanley at Bric, and James Rolfsen at Kojuba. With each movement out, a vacancy occurs that can be filled is filled by new employees that need only training and an opportunity.

 

The social impact: The alumni network is an active and working peer connection hub. Former employees of CAN left on positive terms which means they still keep in touch and occasionally ask for help on projects or advice on business moves. The reverse is true as well: CAN doesn’t hesitate to reach out to the alumni network with questions and advice.

 

The emotional fulfillment: As Watson continues to invest in his employees and sees other companies like Lyconic do the same, he feels a deep sense of pride for the community CAN helped build.

 

As more and more companies make the switch to investing in employees, what does the future of business look like?

 

Investing in employees allows companies to retain the value of each individual, even after they are gone. As more businesses decide that money spent on staff training is money well spent, their pool of resources grows beyond their own company. Their former employees continue to have value in an alumni network. In a sense, by investing in employees you never really have to cut the cord when they move on from your business. This doesn’t get rid of competitiveness between companies completely, but it does allow different businesses to act as support for each other’s growth.

 

Watson of CAN ends with this note, “Investing in people will always be my personal mantra. I hope it continues to permeates the atmosphere of CAN, the Oma-DSA, and the Data Science Community long after I’m gone.”

 

You learn. You move up. You let go. You come back and talk shop. You train someone else. You maintain connections and continue to encourage each other. That’s CAN. That’s the Oma-DSA. That Lyconic. That’s all companies committed to individual achievement. That’s the beauty of investing in employees.


Related

Why you should invest in your employees

on August 8

Spreading the Good Word about Predictive Analytics

on April 1

CAN is excited to announce the Omaha Data Science Academy. We are teaming up with Interface Web School to offer Omaha’s first Data Science Academy. This is something we have been working on for a long time. It is actually a continuation of a service we currently offer to clients where we train a company’s first data scientist. We feel this is a unique person, trained in both data science and business problem solving, and is needed by their company to help implement the ideology more than produce mathematical models or produce visualizations.

In the past we heard that while companies know how to find and hire a data scientist, they fear not being able to utilize this person or even know how to correctly scope how to use predictive analytics in their business. This caused them to not execute or to execute poorly and leave a bad taste in the organizations mouth.

CAN has discovered that having a data science advocate (instead of just a data scientist) usually fixes the hangup with implementation in most companies trying to use data science for the first time. The realization there was a considerable lack of talent when looking to fill this need, led us to develop a school that teaches not only entry level data science, but also how to address the political red tape prevalent in changing how an operation thinks and makes decisions.

This academy will help CAN reach its goal of putting a data science advocate in every company in Omaha. While audacious, we feel this is a must to keep Omaha companies relevant in an economy where we are not just in competition from a company down the street but from every other company doing similar work around the world.

 

So what are the details?

This certificate will teach some of the most important techniques and tools necessary to introduce data science into company culture, get necessary political buy-in, find, manipulate, and analyze the data present inside your company’s database, make predictions of outcomes, and create visualizations that can help non-technical users understand and see the identified trends and patterns inside the data.  

The Data Science Academy is designed to help set a company down the road of data discovery and data-driven decision making. While not the heavy mathematician or economist created by four year degrees, the graduate will leave the Academy with the confidence and the skills of an entry level data scientist and be able to have conversations with business units, build predictive analytical MVPs, and be able to know and manage the skill sets needed for future data scientist projects.

Below is a high level overview of what you’ll learn and some fast facts about the course.

 

Data Science Skills Covered:

  • Entry level predictive analytics
  • Entry level business implementation skills
  • Basic computer programming in Python
  • Entry level computer programming
  • R for statistical analysis
  • Practical machine learning techniques
  • Unix and Git
  • SQL and JSON
  • Introduction to Algorithms
  • Introduction to Data Visualization
  • Tableau Certification
  • Capstone project for a local small business or nonprofit

 

Fast facts:

 

  • Cost – $5000
  • Duration – 12 week course–consisting of online assignments and 2 onsite evening classes per week
  • Starts 9/19 through 12/8
  • Meets weekly on Monday and Wednesday nights from 5pm-9pm

 

Please let us know if you or someone you know is interested in the class. You can contact the Nate Watson, the Class Coordinator at: nate@canworksmart.com or submit your info below: 

 


Related

Why you should invest in your employees

on August 8

The Omaha Data Science Academy

on June 22

Contemporary Analysis (CAN) is recognized nationally as a leader in the data science field and is regularly asked to “Spread the Good Word of Predictive Analytics” by presenting on various topics at conferences around the US.  In fact, CAN has presented at six conferences in the past 14 months, including:

 

    • InfoTech– Omaha, NE- “Politics and Big Data”
    • 2015 Predictive Analytics World– Chicago, IL- “How Predictive Analytics Fundamentally Changes Marketing”
    • Internet of Things Summit– Overland Park, KS- “The Implementation of Data Science into Production”
    • Big Data Summit– Kansas City, MO- “Finding and Managing Data Science Talent”
    • Vistage Sales Seminar– Omaha, NE- “Improving Sales and Customer Service using Predictive Analytics
  • 2016 Predictive Analytics WorldSan Francisco, CA- “How to implement Predictive Cross-Sales” 

 

CAN is thrilled to spread the word about the data revolution that the world is undergoing, and about the business advantages that can be exploited from understanding that data.  Because data science is an emerging field, many firms have questions about:

How do companies implement data science?  

How should data scientists be managed?  

 

Here are some important things to consider:

Every current data scientist comes from another field

Because data science is a new field, there is very little formal, university training available.  Although data science programs are under development at UC-Berkeley, Northwestern, and UN-Omaha (among others), current data scientists have all made the transition from some other area of expertise.  Some of the most common fields producing data scientists are Mathematics, Economics, and Political Science, and other scientific professions that measure and use data.

Data Scientists are not your average employee

Data scientists feel an innate need to solve problems.  This causes them to be creative thinkers who can think outside the box and operate when there is no box.  They tend to get deeply invested in problems, and use their creativity to find or simulate the right data.  Data scientists are tenacious, and because they place such a high value on finding answers, it is paramount that their solutions be utilized.

Managing a Data Scientist can be tricky

Data scientists are not necessarily businesspeople.  It’s a manager’s job to understand what a data scientist is trying to say, and to help them explain what their solutions mean to the rest of the company.  Additionally, data scientists are not to be managed agilely – the time it will take to find the answer to a hard problem cannot be predicted or scheduled.  Lastly, it is imperative that data scientists not be moved from projects or given menial tasks: they will get bored and leave.

Implementing Data Science is also tricky

There’s an old saying that “it’s hard to teach an old dog new tricks”, and this idea translates to business practices.  It is often difficult for firms to embrace new, proactive methods when they’ve been doing things the same way for years.  Occasionally, resistance to the implementation of data science is borne out of a fear of what will be found – data scientists are known for shining a light in places where light has never been shone before.  Another challenge is being patient once data science has been implemented.  Data science is very difficult, and predictive models require considerable fine-tuning before their true potential can be realized.  Confidence and complete company “buy in” is crucial to the implementation of predictive analytics, particularly in the earliest stages.  

The rewards are immense

When properly implemented, predictive analytics will take a firm to previously unattainable heights.  We live in an age where information is king, and firms who learn to obtain more accurate information in a shorter amount of time will have a distinct advantage over those who do not.  Generally, the first step down this road involves implementing data science. There exists a staggering amount of information in your company’s data… all you need is the key to unlock that knowledge!

 

Let us know how we can help you build predictive analytics into your company. We would be glad to help.

For more information or to gain knowledge as to who and how we have helped implement predictive analytics, go to our website at:

www.canworksmart.com

or connect with the president on LinkedIn at: http://www.linkedin.com/in/natewatson

or send us an email at:

support@canworksmart.com

Download our Predictive Analytics ebooks:


Related

The Omaha Data Science Academy

on June 22

Recently, Contemporary Analysis (CAN) was asked by the Indianapolis Business Journal to weigh in on how Predictive Analytics is changing the marketing space. We believe by combining predictive analytics and marketing, called contextualized marketing, marketing can move closer to the holy grail of one person, one sale.  Most companies do this by purchasing a software–it’s dactyl, most companies have a line item in their budget, and it gives a third-party marketing company reoccurring revenue. While CAN itself doesn’t have a piece of software to sell, we believe that contextualized marketing is the right move for companies and that those with the edge are “the ones using data scientists to predict who inside of a group of people have the propensity to buy.”

Because CAN doesn’t have the software solution, we approached the solution from a slightly different angle. We provide modeling and results in way that can be easily added to your current tracking software. This way, a company can begin the transition from shotgun marketing to tactical marketing with a low cost of entry. Once implemented, the case can be made for the software using predictive analytics to be purchased and used if needed.

Additionally, because software companies provide a tool and very little in the way on why the tool is important, most software implementations fall flat. We believe our value is helping companies manage the change management necessary to implement the software and to understand how to use them effectively–which greatly increases both the adoption and the ROI from the adoption.

Let us know how we can help you build data science into your marketing. We would be glad to help.

For more information or to gain knowledge as to who and how we have helped implement contextual marketing, go to our website at:

www.canworksmart.com

or connect with the president on LinkedIn at: http://www.linkedin.com/in/natewatson

or send us an email at:

support@canworksmart.com

 

Full article IBJ article:  http://www.ibj.com/articles/54753-smarterhq-gobbles-up-venture-funds


Related

Voting with Facebook Likes

on August 22, 2014

Omaha Public Schools Pre-Bond Analysis Featured on KETV NewsWatch 7

on August 7, 2014

Contemporary Analysis (CAN)–A new president of CAN was announced earlier this month. Nate Watson, long time employee, Sr. Project Manager, and Head of Sales will take over for Grant Stanley, in early June. Grant said the lead change was a long time coming, “I am staying on as the Chairman of the Board so I can provide vision and strategy, but I am relinquishing the day-to-day operations to Nate.”

This change comes as Grant takes over as CEO of a new startup, Yield. Yield provides a tool for design and marketing companies to better project staff capacity for a given scope of work. It allows management to know how much work a designer has left and alerts the manager to when the designer is running out of work. Yield and CAN will remain close as the two are set to do work for each other for the rest of the year. “It will be a great predictive project for CAN”, says president-elect Nate, “Yield gets the leadership of Grant, we (CAN) gets to keep the strategist and visionary our company is known for, and we (CAN) get to build predictive analytics into a new product slated to change a whole vertical.”

New Leader

Nate has been steadily taking on more and more of the operations since mid-2014 when he began managing the projects he was selling. It was an important step for the company because it no longer meant there was a drop off in knowledge between setting up the project, and the implementation of the project. “We will operate like we always have. We will help companies use their data to understand and get a better handle on how to make decisions faster. When you let data do some of the heavy lifting, it’s amazing some of the insights a leader can get. They still have to make the final decision, but predictive analytics gives them access to relevant data to make decisions in seconds instead of spending hours combing through a pile of reports.”

New Verticals

Nate also brings new energy and ideas to the business. After adding political campaigns to CAN’s capabilities in 2014, Nate managed 2 governor campaigns, 2 local campaigns, and 2 bond issues. In fact, politics became 33% of the total business CAN secured in 2014. “We faired pretty well getting 3 of 6 through the primary and going 3 for 3 in the general.” In fact, CAN predicted the turnout of the primary election to within .27% or 876 votes out of 324,227, and the final vote count to within 2.8% or 1,577 out of 56,324–all 3 weeks before the election. These numbers catapulted them into the spotlight for regional and national campaigns and many took notice. To date this year, CAN has taken on a Governor campaign, two ballot initiatives, and looking to add a presidential candidate later this year.

New Ideas

CAN is working on a number of new ideas as well. Later this year, they are going to release their first piece of software. CAN’s analytical software is designed to help non-mathematical leadership interact with and learn from their data without the need to employ data scientists and includes the ability to run scenarios on live data. This will accelerate political buy-in and implementation time of analytics into a company. Their software will give CAN an entirely new revenue stream and will allow CAN to sell to much smaller companies. “We are hoping this product allows all companies to use their data to create better marketing, sales, customer retention, HR, and forecasts,” says Nate.  The system is slated to come out in the fall.

CAN is also being asked to develop auditing capabilities as well as a predictive analytics and a data science recruitment arm. “Finding, hiring, and training data scientists is a real problem for companies. The lack of data scientists is the bottleneck we think we can solve. We understand how to attract and vet data scientists better than traditional HR and hope we can partner with companies to lend them our knowledge.”

With these new ideas and growth, CAN is also going to need more staff. Currently open are positions for two new data scientists and a sales person. “We are looking for those individuals who are gritty, and can solve a problem when the solution isn’t easily found. This goes for both data scientists and salespeople.” “After all,” says Nate “finding solutions to problems–really hard problems–is how CAN has been known for the last 8 years.”

 

More data on Contemporary Analysis can be found on their website at: www.canworksmart.com or by connect with Nate Watson on LinkedIn at: http://www.linkedin.com/in/natewatson

 


Related

Nate Watson named new President of CAN.

on May 15, 2015

Omaha Public Schools Pre-Bond Analysis Featured on KETV NewsWatch 7

on August 7, 2014
Voting with Facebook Likes

As campaigns this year gear up their marketing efforts on all the social platforms, it begs the question, how can campaigns measure the success of their efforts on each of these platforms and translate that success to the state of the race?

Let’s take Facebook as an example. On Facebook, a campaign is limited to a few metrics to track performance. These metrics include total “Likes”, average post likes, average post shares, and total number of people talking about the page. Marketing efforts are best measured by looking at the reach of each post, but it seems that the campaign as a whole tends to race towards getting as many “Likes” as possible. For example, campaigns frequently post about milestones they’ve reached for Facebook likes and promote it as a metric for success for the performance of the campaign. There’s certainly nothing wrong with this. Campaigns should be doing everything they can to increase their reach across their network of constituents by getting more of them to like or follow their page.

However, does it actually translate as a predictor for a winning campaign? We decided to embark on an experiment to find out the effect of voting with Facebook likes.

Our Sample:

For our initial experiment we decided to focus on races in the 2012 election cycle at the national level for U.S. Senate and House seats, and at the local level with Gubernatorial races. We didn’t focus on smaller races because the Facebook data tended to be sparse. We also couldn’t analyze races farther back than 2012 because the time series data through Facebook only goes back so far. Initially we gathered information on approximately 106 races for our sample. After eliminating races where Facebook data was sparse or non existent, we were left with 76 cases for our analysis. We also excluded cases where Independent seats were the incumbents, any new seats that opened up for that election (this would be caused by redistricting most likely on the House side), and any cases where less than 100 likes were found on someone’s Facebook page.

Testing:

Next, we wanted to isolate those who won their race in 2012 and also had the most Facebook likes as the group we were trying to predict. We assume that Facebook likes don’t translate to wins explicitly and that there are other factors or dimensions within races that might also be good predictors. The predictors we decided to test in this experiment were Race Type, Incumbent Status, and spread of Facebook likes between the competing campaigns.

Race Type: Senate, House, or Governor

Race Type indicates the type of race at the national or local level. We wanted to test the different races to see if one type was more predictable than another.

Incumbent Status: Democrat, Democrat OPEN, Republican, Republican OPEN

Incumbent status indicates whether a current party has a seat or if they are leaving the seat because of term limits. Our reason for testing incumbent status was that it would give us another dimension around estimating the impact of an established incumbency or the impact of fresh new candidates running in an open seat race.

Facebook spread

Facebook spread is the numeric difference of likes between competing campaigns. Our theory here was that maybe closer numbers of Facebook likes would be more likely to be inconclusive for prediction purposes.

Our Results:

After testing the variables mentioned previously, we found none of them to be significant predictors of winning. So what does that leave was with? Well although we might not have any good predictors for a winning campaign based on Facebook likes, mathematically we’d still estimate that a campaign leading in Facebook likes would have approximately a 63.2% chance of winning the election. With better and more extensive data we estimate the percent change of winning to be closer to 70%.

Would you like to learn more about using Predictive Analytics in Politics? Download our Top 10 Reasons to Make Predictive Analytics Part of Your Campaign Strategy:


Related

Nate Watson named new President of CAN.

on May 15, 2015

Voting with Facebook Likes

on August 22, 2014
CAN_OPS-Pre-Bond-Analysis-2

Recently we have been working, in conjunction with NorthStar Campaign Systems, with Omaha Public Schools to determine likely support and public opinion leading up to a possible bond referendum in the November election. The results of our analysis, along with the recommendations of other consultants working on the project, were featured last night on KETV NewsWatch 7. Watch the clip here.

Our analysis of the public opinion poll showed the 67% of voters likely to vote in the upcoming election were likely to support an OPS Bond. We were also able to determine the demographic makeup of those most like to support a bond issue.

Those most likely to support the issue have a median income between $36,876 and $48,000, and a median home price between $122,401 and $147,600. Likely supporters are most likely Democrats, and those between the ages 50 to 59 have a higher likelihood of support.

In addition we were able to determine the support for the most needed improvements to the Omaha Public School system. The strongest support being for safety and security upgrades, a high school with a career focus, and air-conditioning in all OPS locations.


Related

Nate Watson named new President of CAN.

on May 15, 2015

Voting with Facebook Likes

on August 22, 2014
Why to 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.

Read more…


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Why I work at Contemporary Analysis

on January 16, 2012

Why Corporate Hierarchy is Important

on December 4, 2012

On Entrepreneurship, Risk and Uncertainty

on January 8, 2013
Flexible-Work-Trends_20140813

Today’s flexible work trends favor the clever, well educated and self–motivated. Trends such as BYOD, MOOC’s, results-only workplace, and Holocracies such as Valve and Spotify emphasis the importance of creative, well executed ideas developed by self-motivated employees.

Flexible work trends have emerged because “scalability” allows organizations to realize large gains from ideas, instead of only operational efficiencies. For example, one clever Tweet can reach millions of people, while thousands of mediocre Tweets can fail to ever be read. Today the value is in creativity — efficiency and even automation are just prerequisites.

Today’s flexible work trends are the opposite of the trends of the 80’s and 90’s that emphasized efficiency and cost cutting: six-sigma, just–in–time, out–sourcing, the great moderation, and leverage buyouts. All of these strategies were about extracting more value from what was already being produced. While, today’s trends and technology place a premium on quality and cleverness over efficiency: typically by creating flexible work environments.

However, today’s work trends are not without problems. We have created a flexible work environment at CAN and here are several of the challenges we have experienced.

Read more…


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An Analytical Dashboard: Nebraska’s Workforce

on December 16, 2013

Data Driven Decision Making & Camping

on November 10, 2013

The theory of insurance states that the healthier a population of people, the less they should pay in insurance premiums. Right? We built this dashboard to investigate: do healthier states pay less in insurance premiums? What we found is that average weighted monthly premiums depends less on how healthy a state is, and probably more on average cost of living, geography and political affiliation. What do you think?  Read more…



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