Currently browsing: News

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). 

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.

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://can2013.wpengine.com/case-study/.

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.

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/