6 Things You Need To Be Successful At Data Science

What thing can you take back to your business to implement that will help you? There isn’t one and that is the point. They were successful because they implemented them all. All.

Why you should invest in your employees

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 (ODSA), 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 ODSA will inspire even more businesses to invest in their employees.

–The ODSA’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 ODSA, 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 ODSA. That Lyconic. That’s all companies committed to individual achievement. That’s the beauty of investing in employees.

How to become a data scientist

“How to become a data scientist?” is an interesting question, because there’s no real formal training as of yet to become one. Some universities are combining mathematics, computer science, and humanities classes together, but nothing formal has been decided in terms of a major or full concentration of study. Berkeley, Stanford, and the other greats have classes related to data science, but most classes are nestled within existing information technology or math departments. This is perhaps due to the idea that the position still isn’t properly defined, and “data scientist” is usually a catch all term for people with a variety of skills – some that even tend to conflict with each other. Most hard math or science majors are 1+1=2, end of story. Humanities tends to look at the world more abstractly and realize that there is leeway and not everything adds up. Data science requires much from both of these.
The requirements of people with these skills are also somewhat across the board, with specializations reaching from simple large scale data management and storage, to those who can apply analytics and machine learning or artificial intelligence to make predictions of the future or better apply recommendations to consumers ála Netflix, Amazon, Facebook, and Google.
Nonetheless, there are a specific set of skills you can work to develop and fields of study you can dabble in if you’re interested in working with data. While still somewhat vague, the ultimate purpose of today’s data science is to manage, make sense of, and ask questions of data sets.
Applied data science is all about measurement, so work on increasing your statistical chops. In addition to being a general good life skill (probability and common statistics can be used in the media to manipulate human behavior or use to fear monger those into believing false or loosely defined relationships. Knowing even elementary statistics helps you spot bad science.)
Computer Science
Depending on the type of data science you’re into (management vs analytics, for example) a good understanding of computers is a strong skill to develop. Even if you’re interested in only mathematical applications, elementary programming classes can familiarize you with a certain logic and problem solving mindset useful in this space. Being familiar with database languages like MySQL, and the statistical language R, and even web technologies like HTML and PHP can help you write applications to gather data and make life much easier.
Economics / Biology / Bio – Informatics / Physics …
I’ve got a soft spot for my own field of study, economics. But any simple or complex science in which you model reality and try to describe it is useful for data science. Economics itself is the study of efficient allocation of limited resources, so many economic models are built to use data to describe processes and how firms and consumers interact, among many other things. Physics and Biology are also concerned with modeling their “ecosystem” and finding relationships between all of its actors. Being fascinated with how changing inputs changes the outputs is a good mindset to have, all while being able to approach it with a scientific method style of hypothesis testing.
Beyond University, there are a multitude of resources out there for learning how to play with data. MIT OpenCourseWare has a lot of free courses, many dealing with computer science, math, and other sciences. LinkedIn has lots of groups devoted to those who work in data. Try connecting with those people.

Forbes: The Age of Big Data / A Looming Talent Gap For Data Scientists / Why Companies Are Spending More on Analytics

The Age of Big Data: “…Big Data has the potential to utterly transform the relationship that individuals have with institutions, customers with companies, patients with the healthcare system, students with universities, and voters with government. And that means once it has fully penetrated society and industry, the Big Data revolution may very well prove a turning point in our economic – and ultimately, cultural – history as great as the electronics revolution. . . perhaps even as great as the first and second Industrial Revolutions.”
–“Why? Because once the relationship of individuals to institutions transforms, the benefits to the individual consumer, citizen, patient and student will be profound.” (Forbes)
How a Looming Talent Gap Will Crush Enterprise Hopes for Big Data: “’A lot of companies don’t know how to find data scientists, and don’t understand data science,’ … ‘These enterprise companies can’t implement a proper data analytical solution because they have no data talent.'”
— “Part of the problem is an overall lack of big data skills in the United States. In May 2011, the McKinsey Global Institute laid out the numbers: ‘By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.’” (ReadWriteWeb)
Big Data Security Is Inevitable: “There’s been a fair amount of discussion about the fact that security analytics is becoming a big data problem. … If you think that enterprises recognize these trends, boning up on Hadoop, Cassandra, and NoSQL, and hiring data scientists to tag along with security analysts, think again.  There’s a growing security skills shortage that will preclude these activities before they even start.(Network Worlds)
Big Data Holds Big Promise for Government: “Big data has the potential to transform the work of government agencies, unlocking advancements in efficiency, the speed and accuracy of decisions and the capability to forecast, according to a separate report from MeriTalk.”
–“…the Centre for Advanced Spatial Analysis (CASA) at University College London is combining data from London’s Oyster cards – used to pay for public transport – and Twitter messages. Tube-travel patterns are regular: people who enter the system at one station tend to leave it at a particular other one. Twitter messages reveal a city’s structure and its activity.” (Smart Data Collective)
Why are companies spending more on analytics despite cutbacks elsewhere? “Analyst Dan Vesset, author of IDC’s “Worldwide Business Analytics Software” report, credits ‘attention-grabbing headlines’ about big data, rather than the data stockpiles themselves, with helping to put business analytics on the agenda of senior executives. Goodnight seems equally dubious, saying big data is the hot new topic ‘because people got tired of talking about the cloud.'” (InformationWeek)

Contemporary Analysis Job Board: Data Scientist

Contemporary Analysis (CAN) is a global data science company based in Omaha, NE that provide predictive analytics to multiple Fortune 500 companies and small businesses in the United States, Europe and Asia.  CAN is focused on making analytics accessible to companies of all sizes and industries, and offers standard products and professional services.
The purpose of this position to help expand our professional services team.  CAN’s professional services team is responsible for developing solutions for CAN’s largest and most unique clients including Fortune 500 and Global Fortune 50 companies.  The by-products from the team’s professional services are used to create new and enhance existing CAN products.
Each Data Scientist is responsible for working with a CAN Sales Executive to understand each client’s business, define projects to help clients achieve their business objectives, use data science to develop solutions, and present results as a written report and presentation.  Data Scientists must be familiar enough with statistics and computer science to develop creative solutions, and have the written and verbal skills to develop compelling reports and presentations.
The Data Scientist will be responsible for:

  1. Working with the Sales Executive, the Data Scientist will work at all executive levels to help design solutions that will meet the needs of the client.  To be able to design creative solutions that go beyond simple client feature requests will require Data Scientists to have an advanced familiarity with modeling, mathematics and statistics.  Also, during the discovery phase the Data Scientist will coordinate with the COO and Sales Executive to develop project budgets.
  2. During the implementation phase, the Data Scientist will work with other CAN Data Scientists and vendors to implement the Analytical Blueprint, and monitor client results, and adjust the Analytical Blueprint to optimize the client results and experience.  Since CAN offers data science solutions as a service, implementation can last from a month to several years.  This creates a unique project management scenario that requires continuous monitoring to ensure that the project does not fall behind.
  3. The Data Scientist working with the Sales Executive will maintain a positive relationship with the client, ensure ongoing deliverables are met, and assess any future need for CAN’s services.  In some cases the Data Scientist will need to record best practices from the project, or write specific business issue case studies.


  1. Minimum Education:  Bachelor’s Degree from an accredited institution.
  2. Able to maintain focus in highly-charged environments and manage competing priorities.  This includes experience managing multiple projects simultaneously against tight deadlines
  3. Experience solving business issues with the consultative application of advanced analytics and/or information technology
  4. Strong presentation and client management skills – up to the highest executive level.  This includes being able to explain highly detailed and technical subject matter to non-technical audience, and being able to present and sell analytical concepts to clients
  5. Experience delivering insight to internal or external clients by building on a technical foundation that includes a conceptual understanding of modeling techniques and a basic grasp of statistics.   Ability to use analytical applications to solve a practical problem, in an on the spot high-pressure situation
  6. Experience in project management and managing a team to meet a deadline, manage client expectations, and maximize client satisfaction relative to solution profitability
  7. Functional experience in one or more of the following areas, selling analytic services, project management, data science product development, pre-sales, technology implementation, and/or account management
  8. Technical foundation including one or more of the following areas, Bayesian statistics, multiple regression analysis, and/or econometric modeling is preferred but not required.
If you are interested in learning more and applying contact Grant Stanley by phone at 866-963-6941 #801 or connect with him on LinkedIn.  Please have your LinkedIn profile up to date before applying.
Featured Posts – Click the Brain
CAN Jewels