Recently, Contemporary Analysis (CAN) was presented with the Greater Omaha Chamber’s Small Business of the Month award. It means a lot to be recognized for the hard work the team has done over the last year improving how companies start and scale data science internally.
On the consulting side, CAN has spent most of its 9+ years implementing Data Science in the traditional format: bidding via proposals and statements of work. While we still make bids and builds via proposal, we realized a lot of companies that need data science have a difficult time formulating their need into a written document. They don't know where to start, what they need, or how to scope time and materials. Not having a statement of work presents a problem for traditional consulting. Even when they did understand how to build a needs document, an outside vendor wasn't what companies wanted. Their desire was to own their own analytics team. We couldn't agree more.
Once they had decided to make data science an internal strategy, companies hired a senior-level data scientist. This was due to the fact that the person needed to be all things for the department for a long time until it showed an ROI and would be granted abudget to hire a team. This required the first data scientist to be a programmer, database manager, mathematician, data visualist, data science strategist, and implementation manager. This came with a whole new set of problems. A person experienced enough to do all things is expensive ($150k+ salary), hard to find (time to hire is 6+ months), implementation requires a philosophical change in problem-solving (reactive to proactive), and scale requires a new management process (Agile is ineffective). It is simply too much for one person to be successful.
We realized we had to change how companies implemented data science. They needed a fully functional team inside their company from day one and for a time requiring an outside vendor, but needed to manage the process inside their company for company buy-in and scalability moving forward. A new way of implementation had to be invented.
We came up with something different, a method with immediate results and little risk. Instead of hiring senior-level talent out of the gate, use a full team of consultants to help you stand up your group. Then find, hire, and train someone to run the team once it’s already up. This means you get multiple people (with no recruiting and no time to hire) and expertise (understanding how to implement and manage all aspects of the team) immediately on day one - all at a price similar to hiring one senior-level data scientist.
Additionally, there is a benefit of when it's time to find, hire, and train someone to run the team. Because some of the heavy lifting is being done by the vendor, a person skilled in data science implementation (a data science strategist) can now be hired to run and scale the department. This person is usually much less expensive than a senior data scientist.
We pioneered this thought process at a local bank in Treynor, IA. TS Bank is one of the fastest-growing banks in our area. They reached out to CAN in late 2015 asking how they could be better at predicting what is likely to happen not only in their portfolio but also in marketing, sales, operations, M&A - almost every function of their business. They already had business intelligence but didn't know how to make the transition from reactive to proactive. That’s when CAN stepped in.
CAN became their data science team for 18 months, deploying 4 data scientists skilled in NoSql, data visualization, coding, and computational modeling. We served as their team until they were able to stand by themselves. Now, just 2 years later, they have their own team of two data scientists, a data strategist, a business intelligence analyst, and a database engineer. TS Bank now has a better data science team than banks five times their size, and they have plans to hire more. With their team, augmented by ours only when needed, TS Bank can make decisions faster and less expensive than their peers. They know when to buy. They know when to sell. They have better risk analysis. Their business intelligence team, now coupled with their predictive analytics team, is the poster child for how to start, grow, and scale data science in an organization. This pilot allowed CAN to better understand how to implement the “Us then You” strategy.
Today CAN offers three approaches to improving outcomes with data science:
- Data Science as a Service (to get you started)
- Training (to make it yours), and
- Staff Augmentation (to keep your need fulfilled, even if that need is temporary).
Data Science as a Service (DSaaS)
CAN begins the process of serving its clients by initially and temporarily serving as their data science team. Day one, we show up and provide our client with an established data science team that knows exactly what they’re doing, knows how to dig into their data, and knows how to cut through the red tape.
Different than most consultants is the fact that from our first second on the job, there is a timer running. We establish an agreed-upon milestone and, once that milestone is reached, CAN will give you everything you need to have your own data science capabilities: all the data, all the knowledge, no black box, and nothing secret.
About midway through DSaaS, CAN will identify, hire, train, and place a person to run everything CAN is building. While this person can be and often is from outside the organization, sometimes it is an internal person who just needs a few additional skills. When this happens, there is an additional savings of time and money as this person required no hiring process, no internal training of tools, is already a culture fit, and requires no spin-up time figuring out internal politics.
To formalize this training process, CAN built a training curriculum designed to help individuals already in the workforce gain necessary and valuable skills in the four key parts of data science: coding, database, statistics and computational modeling, and data visualization. We call it the Omaha Data Science Academy. While initially only for individuals hired to manage the data science portfolio after CAN has reached the milestone, CAN has opened enrollment to the community so they too can have data scientists for job openings at companies with established data science teams. The Omaha Data Science Academy’s new goal is to train a data scientist for every company in Omaha.
Once established, teams like those at TS Bank aren't finished building. It took TS Bank 24 months before they felt they had enough talent to cut CAN loose. And even then, CAN still helps out from time to time, providing talent for project work so the company can continue its lean data science team while retaining the talent and expertise to do the high-level or high-speed need projects. Because CAN offers staff augmentation in addition to DSaaS, companies can hire the senior-level talent much further down the road and not fear not have the senior-level thinking to tackle the hard projects as they arise. CAN also offers entry-level data scientists allowing extra staff for those projects that require hours of work, not level of expertise. In this way, CAN closes the loop of need making sure at all points, a company can run a data science team of any size and make noticeable gains from the insights gathered by having a team.
CAN’s new way of data science team implementation lets a company gain access to the decision making of their ability without the fear or risk of single person dependence. It creates better data science much faster with higher ROI than traditional implementation with the helping hand of a company who has done data science for years.
Us then You will revolutionize Data Science.
Are you ready? Reach out to see how we can help.
CAN provides insight to all teams as they grow and develop. We have completed over 150 projects across 100+ companies, and have been the data science teams for 3 companies in the proof of concept stage, 2 in implementation in past years, and 5 more planned for 2018. We have the experience and wisdom necessary to help companies navigate the new kind of management necessary in data science.