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This summer, long time employee Nate Watson took over as president and owner of Contemporary Analysis. Situated in his new role, Watson has impressive plans for CAN’s future.

Since 2008 Contemporary Analysis (CAN) helped over 100 companies use predictive analytics to find patterns in their business data. CAN uses data businesses already collect, then explores those patterns to figure out what will likely happen. CAN has worked with some of the largest companies in the midwest including Kiewit, Gavilon, Mutual of Omaha, Blue Cross/Blue Shield, and West. CAN holds the reputation of solving the hardest problems in the Omaha data science field.

In mid-2014, after 150+ completed projects, co-founder and CEO, Grant Stanley decided it was time for a new leader to run the company. Stanley appointed then Senior Project Manager Nate Watson to run daily operations while he worked on a new project implementing machine learning into project planning and time management. Stanley’s new company, Bric, launched in late 2014.

Over the next year, Watson kept his eyes open for new ideas on how to make the culture and ideology of CAN work in today’s world. The idea for a new staff-augmentation model (see below) came as a cross between the need to provide a solution to companies that didn’t require massive political buy-in and budgets to build a POC. This idea struck a cord with two friends of Watson who decided to invest in the new ideology and buyout Stanley.

CAN’s motto is “Empower the great to build something greater.” Watson chose two investors who believe in empowering CAN to be something greater.

 

Through the transition, the mission of the company remained unaltered, albeit expanded. Watson partnered with two investors to help him with the buyout.

CAN’s new investors are Nick and Carrie Rosenberry. Both Nebraska natives, the Rosenberrys recently moved back to Nebraska after a stint in Minnesota. They bought into the business because they see a promising future in the data science industry.

“We were looking for a company poised to be on the bleeding edge of a bleeding edge industry. CAN completely fit the bill,” said Carrie Rosenberry.

Carrie is from Tekamah, Nebraska. She received her BS in Mechanical Engineering from UNL while also participating in the Raikes School. She then attended University of Minnesota Law School, where she graduated Magna Cum Laude. She will serve as General Counsel for CAN.

Watson remarked, “Having a lawyer on your team means we can build the ideology behind both the investment group and the agri-tech incubator (scheduled for development next year) using someone who understands the ultimate goal of CAN.”

Nick Rosenberry hails from Scottsbluff, Nebraska. He graduated from UNO with a Bachelors and Masters in Architectural Engineering before getting his MBA from the Carlson School of Management at the University of Minnesota. He serves as Chairman of the Board as well as general wisdom of business management for CAN.

With an on-team lawyer as well as a MBA on the board, Watson believes he has the team built to bring data science to every company regardless of vertical or size.

With the Rosenberrys on his side, Watson unveils a new business plan.

 

While not drastically changing their core business, CAN wants to change how companies interact with data science consultants. CAN aims to shift its main business model from a project model to a staff augmentation model. Previously, when a company needed a project done, they hired CAN, CAN did the job, the company paid CAN, and CAN moved onto a different project.

A staff augmentation model, on the other hand, means that CAN actually provides a data scientist to work directly for the client. By giving businesses the option of hiring a part-time data-scientist, companies no longer need to sift through projects and create extra budgets. It instead allows a company to test out how a data scientist would work in their culture, figure out how to implement ideology, and create the necessary roadmap for success long after CAN’s data scientist has been replaced with their own.

This however, has created a new problem: how and where to recruit the talent necessary to continue data science initiatives after CAN as a consultant has left?

CAN believes the answer lie in one of its new creations, the Omaha Data Science Academy (Oma-DSA). The Oma-DSA is a twelve week course designed to train entry-level data scientists who have business acumen but lack a few of the key skills needed before they take on corporate projects.

The Oma-DSA is designed to augment a person’s existing degree with advice and training from real data science experts in the field. This should provide talent, trained in entry level data science for companies to hire to run their new capabilities.

The first run of the Oma-DSA is this September.

Nate Watson and everyone involved with Contemporary Analysis is ecstatic about these new ventures.

 

CAN has always empowered the great. Under Nate Watson’s new ownership, CAN now has the time and resources to empower greatness within itself.

For more information on the Oma-DSA, or anything you liked about this article, contact Nate Watson below. 



As a data scientist at CAN you will have the unique experience of working at a startup and also consulting with Fortune 500 companies. You get to solve critical problems that other people have not been able to solve. Your work environment will be fast paced, and you be expected to work independently.

Data Scientists have to embrace complexity. They have to understand math, computer science, and business. They have to be able to work with data to find patterns, and use those patterns to create value for the business.

As a data scientist you have to figure out how to solve the problem, find the tools you need, and build the solution. This takes a level of determination that is rare, but required. As a data scientist you will have to be willing to get dirty sifting, sorting, structuring, categorizing, analyzing and presenting data.
Read more…


Winning Political Campaigns with Predictive Analytics

 

Elections are won based on the individual decisions of thousands of individual voters. With Predictive Analytics, even small campaigns are now able to micro-target the voters they need, talk about the issues voters care about, and excite the voters enough to turnout and vote, all without excited those voters who would turnout and vote against the candidate.

 

Download our eBook to learn how Contemporary Analysis (CAN) is helping campaigns implement Predictive Analytics and how this slight change is giving campaigns a distinct competitive advantage to win!

 


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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

 


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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:


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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.


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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.

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Predictive Analytics. You’ve heard the term, but do you know how it can actually impact your organization?

Learn how Predictive Analytics can help in all aspects of your business — sales, marketing, customer service, management, and strategic planning.

Predictive Analytics is the future of Business Intelligence. Using historical data, data you already have, you are able to go beyond just reporting past events (Reporting) and showing what is currently happening (Dashboards). Predictive Analytics gives insights into what is most likely to happen next — it is the closest thing you have to being able to predict the future.

Download our eBook to learn more about the history of Predictive Analytics, how it works, and how you can implement it within your organization.


Information is only valuable if you can make sense of it — and put it to work.

Dashboards provide a window into data. They transform your data from non-descript rows and columns of numbers into visualizations that make patterns and trends obvious — they organize data and make finding meaning easier.

Download this ebook for an introduction to dashboards and the principles of data visualization.


To succeed with business intelligence, there can only be one version of the truth.

Smart companies are data driven. They use spreadsheets to analyze budgets and performance, to calculate return on investment (ROI), and to develop plans. However, this business intelligence is difficult to coordinate, usually resulting in everyone creating separate reports.

With different reports, there are different answers — and slightly different conclusions — to the same questions. These multiple versions of the truth lead to confusion and a lack of trust in business intelligence.

Download our case study to learn how to start making data-driven decisions with a Business Intelligence Data Warehouse.



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