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.

Rethinking Business Intelligence: Information or Decisions

Traditional business intelligence leaves executives with the same amount of work, but with even more information to sort through. The number of decisions, the unit of work, is not diminished.
Traditional Business Intelligence asks, “What information do you need to make better decisions?” The outcome is hopefully beautiful well designed reports and dashboard that support decisions.  The problem is that you still have to make decisions.
Decisions are work.  Having more information doesn’t reduce the amount of work required to make decisions. In fact, it makes decisions more work.  More information does not create less work.
The flaw is thinking that the business decisions are calculations. (more…)

When should you Update Predictive Models?

New clients often have questions about why and how frequently CAN needs to update their predictive models.  Predictive models need to be updated because everyday new data is being created.  For example, your customers are buying more, subscribing or unsubscribing.  The environment is constantly changing.  While predictive models can handle a lot of new new data, overtime environmental changes build up causing predictive models to lose their effectiveness.  After a month, quarter, or a year it is necessary to update predictive models with new data.
As these new patterns emerge its important to periodically take time to investigate your data, update your models, and challenge your assumptions about your business. But how often should you do this?
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The Predictive Analytics Revolution – Are you sitting on the sidelines?

Predictive analytics (or Big Data) is here to stay. You may not understand it. You may not believe that it really works. But the reality is this: your competitors (and it may be just one or two of them) are using predictive analytics to chew up market space as you remain on the sidelines.
Don’t believe me? (more…)

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.
Statistics
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.
 

Data Science in the NFL: Finding the Right Players and Strategies

Who are the best NFL players and why?  This is a question that NFL teams want to answer, or perhaps they just want confirmation.  NFL teams spend a lot of money on scouts to find the best future players, and on staff to determine if their current players are up to par.  Teams already have sources to tell them who are the best players and why.  So why are some teams beginning to hire data scientists to analyze a players stats to determine his value?  How does data science help determine whether a player is good or not?
With football being perhaps the most popular, talked about, and drama filled sport in America, NFL teams invest a lot of money to become the team that everyone talks about, pays to watch, pays to be endorsed by, and generally just pays.  The best way to do this is to win a Super Bowl (or have Tim Tebow on your team).

How do you win a Super Bowl?

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Diapers VS Beer image

Diapers, Beer, and Data Science in Retail

When asked for white papers or case studies on how predictive analytics works, I often give a few stories on how different industries use analytics to find patterns in their data and then apply that knowledge to their existing data to predict what future trends are going to happen. Learn about how we applied predictive analytics to politics. 

I get asked specifically about legends that roam the retail world:  the study that found that milk is the most purchased item so it is always in the back of the store, making you walk by everything thing else they have before you get there, the fact that women’s shoes are always on the way to men’s clothes, and the fact that bananas are at the front of stores because they are found to be an impulse buy.  The one that seems to get the most requests though is the one that men who buy diapers for their kids are most likely to have beer also in their carts.

It doesn’t seem that far-fetched. (more…)

CAN Navigators Help You Work Smart

CAN’s goal is to use predictive analytics and data science to help our client’s work smart.  While predictive analytics and data science can help a company generate more profit, the technology can be very complicated and the process very cumbersome.  CAN is committed to making predictive analytics and data science simple.  Simple systems get added, and our systems are only valuable when they are implemented.
CAN’s Navigator program is key to making predictive analytics simple.  We developed the Navigator program, because in addition to great software, data visualization and reports, wanted to provide our clients with great customer support.  Navigator’s are here to help introduce you to CAN, predictive analytics and data science, understand your business, create a plan so that you can work smart, and then work with you and CAN data scientists to implement the best solution for you. (more…)

Using Tableau Reference Lines to Explore Data

At CAN, as needed we use the visualization software Tableau to create reports and dashboards for our clients.   Also, because Tableau is capable of handling large amounts of data very quickly, we’ve started using it to explore data visually during the data discovery stage of each project.  We use Tableau to check the quality of data, find outliers, and get a sense of the properties of a data set, such as dispersion, central tendency, clustering, etc., before we apply statistical analysis or build predictive models.  A Tableau feature, especially useful for exploring data, are Reference Lines.
This blog post explains a few ways that CAN uses Tableau to explore a data set.
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Why Jefferson Decided to Join CAN

Jefferson joined CAN before we had this blog, our website, our products, or our office at 1209 Harney St.  This video is about how and why he decided to join Contemporary Analysis.  He knew we had potential and decided to become apart of CAN’s future and the future of data science.  CAN specializes is predictive analytics.  Predictive analytics involves collecting data about your business and customers, and then applying theory and math to build simple systems to help you work more effectively and efficiently.
Our systems are tailored to fit your company no matter how big or small or what industry you are in. We have built simple systems for fast-growing technology companies, Fortune 500 companies as well as small companies in a variety of industries including community colleges, insurance companies, software companies and engineering firms

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