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Contemporary Analysis 2017 ebook coming soon!

In celebration of CAN’s forthcoming 10th birthday, we’ve decided to bring out the best ideas from our blog and combine them into an educational ebook. The posts were originally written by some of CAN’s most notable alumni, many of whom have gone on to start their own businesses in data science.

At CAN, we are all about education. We believe in educating our employees through hands on experience and through courses offered at the Data Science Academy. We also believe in educating our clients about who we are and what we do. We want our clients to understand the systems we put in place. We’re proud of our work. What follows is a six step model on how to implement data, taken from our new ebook to be released soon. We hope you enjoy it, and learn something too.

 

CAN’s Best Practices for Implementing Data Science

  1. Define a company’s mission, vision, and values. We want to know how they do business; what values they have that are unique and permanent even when the strategy changes. This understanding set the priorities and filters that guide future discussions.
  2. Define a company’s goals. Goals have clear beginnings and ends and typically are accomplished in less than a year. Goals should be in alignment with the company’s vision for the future, and should be accomplished in a way that adheres to the company’s values.
  3. Define the business question to be answered. The business question is about business process improvement, and should not involve technology or research questions. When answered a business questions should have a noticeable impact on at least one of the three parts of a business; sales, operations and administrative support.
  4. Determine what resources are available. This includes political approval, availability of necessary data, and determining research methodology.
  5. Determine how the models will be implemented. Formal Reports help our clients understand the nuances and details of our research. Marketing Summaries provide our clients with colorful and easy to understand summaries of our research.Visual Dashboards help our clients quickly get the up to date information that need to run their operations. Workflow Integration provides our clients with the ability to use our research to impact the activities and operations of large number of people through the systems they are currently using.
  6. Evaluate the model. Does the model answer the intended business question? Does the model produce results that reflect reality? Does the model produce the expected results?

 

Keep your eyes out for our new ebook. For more information and great ideas, contact Nate Watson (nate@canworksmart.com) or Bridget Lillethorup (bridget@canworksmart.com). 


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Any person you hire for your team is an investment. You take careful steps to ensure their fit in the company. You go the extra mile to ensure their skills translate as perfectly as possible to the position you seek to be filled.

Most companies, unfortunately, do not understand the complications of hiring a data scientist. No two data scientists have the same skill set. And there’s not a specific “attitude” associated with data scientists. Personalities range quite drastically. Therefore, you can’t simply choose a “data scientist” and trust that he or she will fit with your company. It’s different than hiring a salesperson or HR representative.

 

So, before you invest in hiring a full time data scientist for your medium to large sized business, there are more than a few things you need to consider.

 

Contemporary Analysis (CAN) offers another option to fill your data analytics needs. We offer a full analysis of your company to determine the projects that will improve your areas of need. We lend you one of our data scientists to use hourly until the projects are over. Less strings attached, less money, higher ROI.

 

Let’s first explore the scenario of hiring a data scientist blindly, and see where it takes this hypothetical company.

 

Scenario 1: Hiring a data scientist full time

 

Perhaps you are the manager of a local bank. You’ve grown significantly in the past 10 years, and you know you have enough data to start analyzing trends with your clients. You’ve noticed that at least three customers drop their services every month, and you wonder if a data scientist could provide an answer to stop this trend.

 

The first step, you believe, is to hire a full time data analyst as part of your team. You write up a short job description and send it out to the hiring sites.

 

Someone with a Master’s degree in data science doesn’t accept a position for less than $100,000/year. Along with $25,000 in benefits, this is quite the price tag. You may understand this and accept it begrudgingly as the only option.

 

What you may not realize, however, is that it takes you about 6 months to find someone with a Master’s in data science and relevant work experience. Then it takes your bank about 3 months to train him in. And finally, 3 months after he is trained in, he builds a software useful to your company.

 

That’s a year from the time you posted the job before you see any kind of ROI, and by then you’ve spent half of a year’s salary on this person.

 

The expenses for hiring and training a data scientist are immense, but there are more than just monetary issues with hiring a data scientist full time. Here is the short list of things you need to consider before you hire full time:

 

  • Not all data scientists will like your company.  Just because someone is qualified on paper doesn’t mean they fit well with your company’s community. At $125,000/year and with 6 months needed before ROI, do you want to take that risk? Personality clash is a real threat.
  • No two data scientists have the same sets of skills. That’s right. “Data science” is a loose term meaning an interdisciplinary field about scientific processes and systems to extract knowledge or insights from data in various forms.” Interdisciplinary is the key word here. Some data scientists may be trained in specific softwares, others may have not even heard of it. When you lock yourself into one person, you may be losing out on knowledges beyond his education.
  • How do you know you need a data scientist? Without experts to examine your needs, are you sure the hire is worth the investment? Perhaps figuring out why your bank is dropping clients every month will only take about a month, but you’ve hired someone indefinitely. What other work will you find for him?

 

There are other options. Consider hiring an hourly data scientist through CAN.  Let’s explore the benefits below.

 

Scenario 2: Work with Contemporary Analysis

 

A Case Study from CAN

CAN has worked with several Fortune 500 companies. In one instance, one of these companies needed assistance creating a software that could predict the failure of telecommunication huts. Loss of several huts slows service to customers, which ends up a nightmare for the finance team.

 

The scope of the project was large: 2,500 telecommunication huts over the Western United States. Over 500 of these locations were in fairly remote areas, making them hard to reach. The scope of this project may have been enough to convince the company that they needed to hire a full-time data scientist, but instead, they saw the benefits of working with CAN.

 

The result? CAN set up a weekly survey process for employees at each station, covering 12 potential problem areas.  

 

The data collected from these services was used to create a “survival model” for each roof. CAN set up a system for predictive analytics with this Fortune 500 company over an established period of time, then made sure the system was self sufficient and did not rely on CAN’s constant attention.

 

With work complete, the company paid CAN and CAN moved on to new projects. Always available for advice, CAN remains a tool for that company, but not at a cost of $125,000/year.

The benefits of using Contemporary Analysis to hire an hourly data scientist

 

To save time and money, and increase productivity, consider these benefits of using CAN for your data science needs.

 

  • With CAN, you can hire a data scientist that fits the skill level needed to attain your goals. If you need someone trained in Tableau, then you get someone with that training. With CAN, there is no risk of a learning curve with your hire.
  • Hiring a part time data scientist means you’re not locked into one set of skills. In a similar vein, when your needs change, instead of paying to train your full time hire, CAN simply assigns a new person to the job. This gives you a bigger skill set advantage.
  • No 6 month hiring process. No training. No wallet-bursting budget. No issues with HR. CAN can write up a proposal for your needs in as little as two weeks, and work starts immediately upon signing the contract. Work starts on Monday, not 6 months from Monday.
  • You will see an ROI in 30 days or less. We at CAN works fast and effectively. It’s our job. Once our systems are in place, you see immediate results. Before you would have a job description for a full time position on your site, CAN will have created analytic software.
  • You don’t need to worry about keeping your full time data scientist busy. Once the project is over, it’s over. You don’t need to worry about filling someone’s time or wasting money on little work.
  • You data scientist won’t quit! A part-time data scientist will do his work and fulfill his duties. You won’t have any surprise 2 week notices in your office.

 

Perhaps you believe your data science needs are great, and you still believe full time is the way to go. Before you plummet down this expensive road, give us a call. If anything, we can give you an analysis of how great your needs are, and you can make a decision from there.

 

For more success stories, see CAN’s website for more case studies at http://canworksmart.com/case-study/.


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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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Creativity and innovation are the key for companies and countries to remain competitive. Technology has flattened access to resources and geography. Access to capital, equipment, and raw materials are no longer a competitive advantage. Geography offers few protections. The only true competitive advantage is in people—their connections and creativity.

The future of Nebraska’s economy is dependent on the future of Nebraska’s workforce. Given the importance, Contemporary Analysis decided to create an analytical dashboard of Nebraska’s workforce. Learn more: download our Dashboard eBook. The dashboard allows you to explore Nebraska’s Workforce from 1999 to 2012 by race, education and job types. It shows the distribution and trends for education and job types, and the correlation between job type and education.

 


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Data Driven Decision Making & Camping

I was recently reminded of the importance of data driven decision making. I spent 6 days kayaking and backpacking in the wilderness on the US and Canada border. After living as a hyper connected technologist, disappearing into the backcountry was amazing and it lead to an unexpected realization. Read more…


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Once again it’s that time of the year when kids and parents prepare to hit the streets of Omaha in their ghoulish best looking for the most candy they can find.

For a kid, Halloween is one of the most (if not the most) exciting times of the year. They get to dress up, act like their favorite characters, and eat lots of candy. What could be better?

For a parent, Halloween can be a stressful event. Having to travel up and down several blocks to find enough candy while managing hyped up kids, and on top of that cold weather, can make for an awful mix on Halloween night. But what if you had a way to scout out the best neighborhoods around you to find candy before hand?

This year for Halloween, the data scientists at CAN took on this challenge to help parents and kids find neighborhoods with the most and best candy.

Using Block demographic and spatial data from the 2010 Census and Halloween spending data from the 2012 National Retail Federation, CAN’s data scientists developed a predictive analytics dashboard that allows parents to get a high level view of the expected spend on candy in their neighborhood as well as surrounding neighborhoods. Alongside spending, an index of household density is also included to show which neighborhoods may hand out the most candy on Halloween night.

“Last year we realized we could use the data we were gathering on household demographics and combine it with estimated spending data on Halloween candy for each neighborhood,” Tadd, COO and head of data scientist says. “I worked with another data scientist, Matt Dickinson, who built the concept of the dashboard last year for the Greater Omaha Tableau Users Group, to publish this fun dashboard and give parents the upper hand when planning their Halloween night.”

The tool is interactive and lets the user choose which part of town they are in or which neighborhoods they want to compare. “This is very similar to some of the visualizations we develop for companies to understand their customers and help guide strategies for developing better marketing campaigns,” continues Tadd. “I am happy we get to use our expertise to showcase some fun analytics as well.”

So if you want to take a proactive approach to getting your little ghouls’ bags filled with candy, use this predictive analytics dashboard to make your Halloween night boo-untiful!


CAN helped a regional healthcare system prepare for the 2009 H1N1 Swine Flu Pandemic. From April 2009 to April 2010 the demand for healthcare spiked putting pressure on nursing staff.

Using predictive analytics CAN developed a staffing plan for a large regional healthcare system with 10 hospitals. Hospitals across the country were hiring as many nurses as they could. A shortage of nurses was quickly developing. Our manager needed to know how many nurses to hirer. He need to optimize his budget to meet the demand without purchasing and holding excess capacity.

Learn how CAN used data from previous flu seasons, nurse schedules, hourly requirements per nurse, training and orientation requirements, and number of active nurse recruiters to forecast demand.

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The Future is Now

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At CAN we explore new frontiers with data science. Most of us think of our world as having already been explored. After all, the days of Magellan and Columbus are literally history, and today we can pull up Google Maps to view satellite and street-level images of every square mile of our planet within seconds. The generations before us sailed new seas, crossed continents and mapped lands that were completely foreign to them. Future generations will be exploring the cosmos and travelling to distant planets. And so it seems as if there aren’t any bold new frontiers for the explorers of our time, but that’s not true.

We live in the digital age, discovering new frontiers using computers, data and the Internet. This world is growing in complexity and we are venturing out to map it and settle it. According to Google’s Eric Schmidt, we now create as much new data in 2 days, as we did from the dawn of civilization up to 2003. We produce 5 exabytes of data every 2 days. (1 exabyte = 1000 petabytes = 1,000,000 terabytes)

This new landscape of data science can be as foreign and complex to many of us as the Great Plains were to the early settlers. Where do we begin? Where are we going and how do we get there? What resources do we have to gain from this bold, new world? Read more…


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I have listened to a lot of conversations about how we are worse off than our parents, how the United State’s Economy is doomed, and how United State’s position as a World Power is doomed. This month I had more conversations than usual about the Great Stagnation.

It is easy to get discouraged about the Great Stagnation. Is the sky falling? It might be, how would I know? Also, after a hard day it is attractive to think that the entire system is broke and we are all doomed to stagnation

However, In the back of my mind I knew things couldn’t be stagnating. Infact, things are changing faster than ever. Confusion, ambiguity, fear and uncertainty are not the result of stagnation. They are emotions caused by rapid growth and new paradigms.

We have failed to realize that we now live in a world that looks like a 1970’s Science Fiction Dystopia. Some people take advantage of the changes, others choose to ignore the change. The result is that we feel anxious, but are not sure why.

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CAN has been fortunate to work with several cities on urban planning. In this case study we highlight our work researching public transportation decisions. We were approached by the Transportation Authority Director of one of the 10 largest cities in the United States. The city had experience rapid social and demographic changes and was struggling to keep up.

From 1960 to 2000, the population declined 20%. The city was supporting a metropolitan area of 5.5M people, while only 500,000 people were living inside the city. In addition, urban renewal projects had changed the income and racial mix of the city. Young urban professionals are moving into the city center, while lower income residents are moving to the cheaper suburbs. Also, the city is losing Black residents while gaining White, Asian and Latino residents.

The Transportation Authority was receiving complaints that the system was under-serving specific demographic groups, and needed to determine whether this was true, and how routes, rails and road could be redesigned to ensure access without discrimination.

Learn how we helped the city make smart public transportation decisions.

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