Related

The Second Class of the Oma-DSA has SOLD OUT!

on January 11

The Omaha Data Science Academy

on June 22, 2016
unnamed-2

We are now accepting applications for the June 2017 cohort of the Omaha Data Science Academy!

Apply at Interface Web School’s website.

Are you interested in predictive analytics? Are you applying for jobs involving machine learning? Would you like to learn how to design and create algorithms? If so, the Oma-DSA may be a perfect fit. The Oma-DSA is designed for people who want to add to their data science knowledge for marketable skills. We use hands on teaching from leading data scientists in the Omaha area to craft courses that will boost your knowledge exponentially. More details at canworksmart.com


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The Advantage of Hiring an Hourly Data Scientist

on January 18

Insurance Premiums Relative to Health by State

on May 15, 2014

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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Why you should invest in your employees

on August 8, 2016

Delivering Impactful Customer Research

on February 25, 2014
Screen Shot 2017-01-25 at 4.33.28 PM

As a predictive analytics team, we at CAN take the science behind Big Data very seriously, but that doesn’t mean that our whole process is centered around the software we create. In fact, we prioritize our relationships with our customers on a human level, and do our best to educate them about what we do best: data. The following article is an educational piece for our customers to learn more about CAN and CAN’s process. 

 

With technology developing so quickly, new ways to implement marketing strategies and more effectively reach consumers are popping up all the time. Predictive analysis is one such technique. Praised for its ability to inform companies of future trends and reveal important information, predictive analysis is growing in popularity, with 87 percent of B2B marketing leaders saying they had already implemented or were planning to implement predictive analytics in the coming 12 months. So what is predictive analysis and how can it benefit you? Let’s check out the details of this new process sweeping its way through the business world.

 

What Is Predictive Analysis?

 

Before fleshing out its benefits, it’s probably best to first explain what predictive analysis is: through data mining, statistics, modeling, machine learning and artificial intelligence, predictive analysis is a process for collecting and analyzing current data. To learn more about how CAN uses predictive analysis, check out our blog post here.

As a result, brands are able to interpret big data and uncover patterns and relationship regarding consumer behavior. For example, the latest mobile technology, such as the Samsung Galaxy S7, has developed sophisticated and compressive methods to retrieve such data from app behavior and mobile activity. With mobile being such a popular device choice for consumers, this is beneficial for retrieving fast and relevant information.

 

How Can Predictive Analysis Benefit Marketing and Sales?

 

  1. More Efficient Customer Acquisition

By providing your sales team with specific data, predictive analysis can allow them to acquire new customers and keep old ones more efficiently and with less cost. What journey do they take to purchase a product? What advertising do they respond to? What is it about your product/service that they enjoy the most? All these questions can be answered by analyzing previous data and drawing conclusions about future activity. This information can then be used to determine which customers to reach out and how best to appeal to them, saving time and money.

 

  1. Determine Up-sell Opportunities

Predictive analysis also assists in drawing conclusions about other aspects of your customers’ buying behavior. Through analysis, brands can better understand what their customers’ needs are and what exactly they’re looking for. This can then be used to tailor the sales and marketing strategy to specific customers.

For example, if you are a fashion brand and have customers who are in need of shoes, it would be inefficient and wasteful to send them an advertisement for a new shoe promotion. Instead, it would be better to send this to customers in need of footwear to maximize on profit.

 

  1. Optimize Marketing Strategy

Not only can predictive analysis benefit brands by helping to find information on customers, it can also help in regards to the market environment. You can learn what time of the year spending peaks, how much people are spending and what they’re spending their money on. This information can assist in the successful execution of marketing strategies by ensuring you are targeting the right people at the right time.

Or you can figure out where to score the most candy on Halloween, like CAN did here. See, predictive analysis can be fun too.

 

Predictive analysis is an increasingly popular method for brands to more effectively initiate sales and marketing strategies. By providing detailed information about market trends and buying behavior, brands can cut costs, boost profit and increase overall efficiency.

 

Hooked on predictive analysis? We’d love to chat with you! Contact Nate Watson via e-mail at nate@canworksmart.com.



CAN’s product roadmap is driven by:

  • Who adopts new technology
  • Why they adopt new technology
  • The hurdles they encounter

 

The adoption of new technology starts with play. Play is inquisitive and experimental. Try something, if you don’t like it: no worries, on to the next thing. The goal is to have a good time.

 

Work is about producing. Doing what you say you will, when you say. Work is about being dependable, known, dedicated. There is nothing inquisitive or experimental with work. Work is about doing what is known to produce value.

 

Value vs. Known

The most common fallacy is that value is what drives business adoption. It doesn’t, don’t act like it does. Known is more important than value, especially if value requires change. Anyone who is currently comfortable will take a bird in the hand vs. venture for two in the bush.

 

The Goal.

How can CAN build a product the allows play, but once familiar transforms work. This is one of the reasons that Twitter — Yammer: a similar service — has been able to gain substantial transaction among professionals for sharing knowledge.

  • Open Source: In the technology community your credibility comes from what you have built. To stay current developers have to build outside of work. They use free open source software — with enterprise support, and once they have gain familiarity it often ends up in their work lives. Play becomes work.
    • Examples: Hadoop, R, Ruby on Rails, AngularJS, Backbone.js.

 

  • That Next-Level: Take technology that people love to use in their personal lives to the next level. No new technology, but technology that is work ready. Yammer is a work ready version of Twitter and Facebook. Microsoft Lync is a work ready version of Microsoft Skype. Windows is a work ready version of Mac OSX.
    • Linux, Apache, Personal Computer, Drones, 3D Printing, iPhone

 

  • Academic Bump: Professors provide advice to students as teachers and professionals as consultants. When possible providing software to professors — free or discounted — can spur adoption in businesses as students get jobs and consulting results are operationalized.
    • Qualtrics, SAS, SPSS,

 

MVP to Maturity:
Technology tends to mature from general to specific applications. Flint Scrapers evolved into a specific application of using sharp edges to process materials, e.g. knives, spears, axes, and cleavers.

 

  • Cutting: Flint Scrapers evolved into a specific application of using sharp edges to process materials, e.g. knives, spears, axes, and cleavers.
  • Digital Screen: A modern example is the splintering of digital screens from a lab tool into the variety of digital screens we have today. Matrix of light bulbs, CRT’s, Plasma, LED, Liquid Crystal.
  • Personal Computer: Even the PC has evolved into a spectrum of diversity. Starting with desktop PCs, and moving to servers, laptops, mobile phones, smart phones, tablets, netbooks, cloud computing, and Internet of Things.

It is impossible to fight the extropy — the trend towards order — of the nature of technium — technology as a biological Kingdom. Technology will always fracture from general to specialized. CAN’s product roadmap leverages the nature of technology instead of fights it.


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A Celebration of CAN’s Best Ideas

on February 11

Insurance Premiums Relative to Health by State

on May 15, 2014

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/.


Related

Guest post: Grant Stanley of Bric

on December 29, 2016

Successful First Run of Omaha Data Science Academy

on December 20, 2016

The first run of the Omaha Data Science Academy proved successful. Already 4 of the 6 graduates have found jobs in a related field. Silicon Prairie News found this noteworthy and published an article about it here.

Silicon Prairie News is a newsroom and community forum focused on start-ups in the Great Plains/MidWest region. Silicon Prairie is a venture of AIM, a non-profit organization centered around building community through technology.

For some information on the Oma-DSA, contact nate@canworksmart.com.


Related

Now Accepting Applications for Oma-DSA

on February 21

The Omaha Data Science Academy

on June 22, 2016
canstockphoto25278440

The next Omaha Data Science Academy cohort starts June 13th. Beat the competition and apply early! Applications open on Monday the 16th.

Contact Nate Watson at nate@canworksmart.com or see Interface‘s webpage for more details.

Are you interested in predictive analytics? Are you applying for jobs involving machine learning? Would you like to learn how to design and create algorithms? If so, the Oma-DSA may be a perfect fit. The Oma-DSA is designed for people who want to add to their data science knowledge for marketable skills. We use hands on teaching from leading data scientists in the Omaha area to craft courses that will boost your knowledge exponentially. More details at canworksmart.com


Related

Silicon Prairie Publishes Article on Omaha Data Science Academy

on January 12

Successful First Run of Omaha Data Science Academy

on December 20, 2016

Every week CAN will highlight a past or present CAN employee as part of a CAN alumni network series. This week we feature Grant Stanley.

Grant Stanley founded Contemporary Analysis in 2008. For 6 years he served as CEO and president before handing off the company to Nate Watson to pursue new ventures. In 2014, Stanley launched Bric. Bric is a managing software system designed specifically for creative agencies. Today we highlight a post on Bric’s blog about the art of time tracking and the importance of the data it collects:

https://getbric.com/time-tracking-needs-new-purpose/


Related

Silicon Prairie Publishes Article on Omaha Data Science Academy

on January 12

Guest post: Grant Stanley of Bric

on December 29, 2016

The Omaha Data Science Academy: What is it?

In 2008, Contemporary Analysis (CAN) began helping companies build predictive analytics capabilities, mostly through project based work. Last year, CAN recognized a rising need in companies: more and more, businesses needed to bring PA capabilities in house but lacked the staff to do so.  So, in mid-2015, CAN switched from project based work to a staff augmentation model. This last year, the need has grown exponentially as CAN has been asked to train staff for more companies, sometimes two at a time. CAN decided it needed a better, standardized way to train individuals to be part of data science team.

In July of 2016 Contemporary Analysis (CAN) announced the open enrollment for the Omaha Data Science Academy (Oma-DSA), the ultimate goal of which is to train a data scientist for every company in Omaha. With the help of Interface Web School and through the CONNECT re-education grant, the Oma-DSA was born.

Nebraska’s role in the development of the Oma-DSA:

 

Coursework in the Oma-DSA is designed to provide training to those who already have business acumen and don’t need another degree just to qualify for an entry-level data science job. They really only need skill-based training. This goal led CAN to partner with the CONNECT Grant in Nebraska. This federal grant provides Nebraska’s underemployed workforce with skill training and financial support to begin careers in IT with companies throughout the state. The partnership was perfect as both CONNECT and CAN seek to bolster Nebraska’s professional workforce with more highly trained individuals.

 

Interface’s role in development and administration:

 

In search for an example of how to teach an academy, CAN connected with Shonna Dorsey of the Interface Web School. Interface offers courses to bolster skills and knowledge of technology and online softwares to help strengthen the workforce. It appeals the most to people who may already have degrees and careers, but are looking for new opportunities. Class schedules are flexible for busy lives.

 

CAN was excited because Interface is both a platform for learning and a platform for teaching. They offer students an immersive learning program lead by industry experts and a professional network that connects students and businesses throughout the Midwest.

 

“We understand,” commented Shonna, “that first and foremost it takes talented people to build talented people.

 

This was in complete agreement with how CAN thought and wanted to run the data science, and the partnership was set.

 

“Interface is helping us setup the platform and teaching us the very detailed structure that goes into running an Academy such as the DSA”, commented Nate Watson, president of CAN and administrator of the Oma-DSA, “without them, we would still be back at step one.”

Who teaches the Oma-DSA?

 

The answer to this question sets CAN apart from many other data science courses. The Oma-DSA is taught by the data scientists who work at CAN. Each day the professors spend their time solving a problem for a client and then teach the students those same techniques and solutions. With the DSA, there are no textbooks, students are taught scenarios that are sometimes only hours old.  

 

What was the outcome of the first iteration?

 

In December of 2016, the DSA graduated 6 entry level data scientists. Four have already been hired  by local companies looking to implement data science into their daily managerial tasks. Multiple others companies have shown interest in the graduates and many others are excited to see what the next group of graduates will have to offer.

 

What did CAN learn from the first run of the academy?

 

Although the first run was successful, CAN is building improvements for  second run of the Oma-DSA starting in January. The eighteen week course will be divided into 4 modules: Python programming, statistics and mathematical modeling, database design, and data visualization using Tableau. These can be taken individually in any order. When all four are completed, the graduate receives a Fundamentals of Data Science Certificate.

 

The modular system is also significant because it allows students or company to enroll their employee in just one module. If a person were to only want Tableau and not the entire certificate, the module format allows them to enroll in only one module. This also allows a student to test out of a module as well. A Data Base Administrator, for example, won’t have to take a database design class anymore. They can enroll in the other three and receive a certificate.

 

What is the future for the Oma-DSA?

 

In the second half of 2017, CAN hopes to offer masters-level classes in Tableau and machine learning to continue education after the Fundamentals certificate. CAN is also researching customized classes in vertical-specific problems and solutions.  

The next class begins January 23, 2017. You can apply here.

There is nothing else like the Oma-DSA in the Omaha, NE and great plains area. This means that Omaha has the potential to be known internationally as a hub for budding data scientists. Not only that, but it also means that companies in Omaha have an enormous advantage by their proximity to highly educated and expertly trained data scientists.


Related

Silicon Prairie Publishes Article on Omaha Data Science Academy

on January 12

Guest post: Grant Stanley of Bric

on December 29, 2016

Every week CAN will highlight a past or present CAN employee as part of a CAN alumni network series. First up to bat is Eric Burns

Eric Burns is a former employee of Contemporary Analysis. In 2011, he brought on CAN’s first international clients. Today he is the CEO and founder of Gazella Wifi Marketing, which turns restaurant guest information into a marketing tool. He continues to be an active member of CAN’s alumni network.

Here are his thoughts on analyzing wifi marketing:

http://blog.gazellawifi.com/10000-visits-to-a-coffee-shop-wifi-marketing-data



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