There is a saying that you learn something new every day. The idea behind it is that every situation offers the potential to learn something. In other words, you are always learning from those around you, and from what happens in your everyday life and work.
This sounds wonderful. Ongoing learning opportunities at no cost! What’s not to like? But I think this kind of thinking also holds a trap for the unwary, especially in a fast-moving field like analytics and for data scientists, but also more generally. The idea that we are always able to learn something from ordinary situations and those around us can lead to the view that we do not need any other learning experiences. I believe this kind of thinking is a mistake, as does a true advocate of lifelong learning, Oliver Schabenberger, the Executive Vice President, Chief Operating Officer and Chief Technology Officer of SAS.
The importance of keeping up to date
It is a popular conception that any piece of technology is already out of date when you buy it, and will need upgrading almost instantly. It does, however, have a kernel of truth. The same also goes for analytical models: They are only as good as the data used to train them, and that is likely to be at least a few months old. I think the same idea is also sound for knowledge and skills.
Any piece of technology is already out of date when you buy it, and will need upgrading almost instantly. Same for #analytics knowledge and #skills ? #learnanalytics Click To TweetYou need to keep your knowledge and skills up to date, and add new knowledge, if you are to remain at the top of your game. New ideas and new ways of working are constantly emerging, especially in analytics, and it pays to understand them. A refresher can also keep bad habits from creeping into your work, and ensure that you use best practices at all times. There is a reason why doctors are expected to commit to continuous professional development, and other professions – including data scientists – would do well to remember it.
Like doctors and other professionals, data scientists can choose from a wide range of training and learning experiences. These include formal courses, one-to-one learning from experts – shadowing, for example – and online learning. What is right for you? Only you and your managers can tell for sure, but my view is that once you are in work, rather than full-time education, generic courses may not work very well. Something tailored to your specific needs is likely to give a much better return on investment, both personally and for your organisation.
Designing your training
A good example is SAS’ Analytics Value Training program, a 12-month course aimed at business users, IT experts and data scientists. It starts with a needs assessment for companies and individuals, and then has a core of 11 days of mandatory training designed to bring the three groups together and give them common ground. The core content covers programming, statistics, machine learning and visualisation, but also includes communication and business acumen, while optional training allows participants to focus on the additional areas that will be most useful to them, based on their background and business needs. Both online training and classroom sessions are used to maximise time efficiency and fit with a full-time work situation.
The value of cross-role collaboration
Business users and analysts could, of course, do their own separate training. There is, however, huge value in collaborating with colleagues outside the formal work environment, where the precise results are less crucial – which is one reason why hackathons are so popular. There is also enormous corporate value in a shared grounding and understanding. That is why SAS Analytics Value Training aims to bring together data scientists, IT and business users to provide a common language and shared understanding of what is possible.
There is huge value in collaborating with colleagues outside the formal work environment, where the precise results are less crucial – which is one reason why hackathons are so popular.
Emphasising soft skills
For all that IT specialists and data scientists are often thought of as “geeks,” only able to communicate with computers, communication and collaboration are a key part of working with data. When communicating with business users, it is vital to ask the right questions. But you must also help them understand the results emerging from analytics if they are to use them to support decision making. Formal classroom training, exercises, practice and role-playing are used to make sure these skills stick. Many of the participants find this to be the program’s most valuable part.
Case studies
The program includes case studies with a range from a variety of fields. These case studies bridge the gap between business messages and technical code. They cover various business problems and corresponding techniques and methods to resolve them, such as data management, analytical and visualisation techniques.
Analytics lab
SAS has a dedicated analytics lab where the participants can practice with case studies, build proofs of concept on their own business issues, conduct in-depth studies of available tools, and create the basis for decisions using advanced analytics, as well as meet experts and peers.
Mentorship
It is usually fairly easy to define a case study, but there are a number of obstacles that can get in the way when trying to work with it. Each participant therefore has access to a project mentor, someone with appropriate holistic field experience who can help overcome these challenges while not straying from the path. This is rewarding for both mentors and students, and another example of the value of collaboration available in this program.
Ecosystem of skills providers
The program is software independent and is delivered by SAS and an ecosystem of competence partners to ensure that the participants get the best possible training no matter the topic. Patric Hellgren, Principal Architect for Analytics Value Training for Data Science, says:
“We have established an ecosystem of domain experts. About 30 percent of the program content is analytical expertise from SAS, and 70 percent is delivered by the ecosystem, i.e., communication, business value, open source/Hadoop and enterprise architecture – all with their own experiences and angles on analytics, yet with the same commitment to the program outcome. With the program focus on communication and business value, the most critical skill for a successful data analytics professional, this program affords organisations opportunities to embrace and move their analytics maturity forward.”
But don’t take my word for it, ask the participants
Scandinavian Airlines had a challenge with duplicated efforts and inefficient work methods when knowledge silos obstructed the organisation’s analysis capabilities. After attending the program, they have less dependency on consultants and greater analysis insight.
Hi3G wanted to increase the analytics department’s ability to add value to the business. The program participants received a business perspective that enables them to add value when discussing the business challenge to be solved, adding faster and better outcomes compared to before.
Continuous professional development matters for everyone. It does, however, need to fit the business needs as well as the working situation. A tailored program like SAS Analytics Value Training has turned out to be a great way to solve both these issues.
