How to do machine learning without data scientists

Many data and analytics leaders face a chicken and egg situation. Without experienced data scientists, venturing into machine learning and data science is difficult. Without any successful pilots, getting serious business commitment to fund data science projects and hire data scientists is equally challenging.

Most organisations are still in the early phases of their data science journey and struggle to understand what machine learning and data science can do for them. They don't exactly know which skills are needed and hiring data scientists seems really difficult.

According to a recent Gartner survey, more than 40 percent of organisations practicing advanced analytics say a lack of adequate skills is a challenge. Hiring experienced candidates can be difficult for a number of reasons:

  • Experienced data scientists will want to avoid being the first to join a company.
  • The amount of energy needed to just get access to data, integrate it and have the first machine-learning models deployed into the business can be staggering.
  • There’s a limited pool of available candidates.

Retaining quality data scientists is also a struggle. They currently favour job hopping as this gives them more exposure to a broader range of tasks — ideally in different industries.

What options do you have?

You don’t have to have a large data science lab to be able to take advantage of machine learning. Start small and evolve the following competencies:

  • Train existing staff into (citizen) data scientists
  • Partner with academia
  • Hire third-party professionals
  • Use packaged applications

Many organisations have mathematically skilled employees without knowing it. They might have been math geeks since high school or are using their quantitative skills in other roles.

The right employees just need to possess the following important characteristics:

  • The right mindset — curiosity and entrepreneurial drive.
  • A holistic attitude — the whole data science pipeline, from data collection to delivery of the analytics results, must be questioned and analysed.
  • The right dose of mathematical affinity — data is often noisy and messy, and the situations data scientists deal with are loaded with uncertainty and high dimensionality.

Many universities and colleges now offer data science related degrees. Using universities for specific projects serves the dual purpose of an organisation getting skilled resources, while also providing students with real-world learning experiences.

The relationship can take four main forms: internships, class projects, innovation labs or hackathons. Coca-Cola, for example, partners with Georgia-Tech in the U.S. for innovation in machine learning and robotics.

It’s best to utilise senior academics that know the business processes and quantitative methods in your area really well. Get them to advise you. They will benefit from the practical traction with you, and in turn, you will benefit from their knowledge and their students.

In this time of immense machine learning skills shortage, third-party professionals can accelerate and kickstart the success of data science programs. There are hundreds of consultancies that can provide a spectrum of assistance, from creating project ideas, early piloting, coaching and teaching of junior staff, to the fully fledged creation of managed services.

Analytics service suppliers should provide the following:

  • Upskilling of existing staff - Suppliers can educate existing math literate staff and even further assist in hiring full-time data scientists.
  • A range of value-adding knowledge assets and knowledge – frameworks, collateral and knowledge artefacts that improve solution quality, repeatability, project delivery effectiveness and time to value.
  • Critical thinking, imagination and creativity – They shouldn’t be solely focused on project execution.
  • A learning experience and knowledge transfer – Coaching and mentoring your in-house team will ensure that the data science capability is sustainable beyond the term of engagement.

It’s important to perform a thorough assessment of your internal advanced analytics capabilities to determine which service provider engagement and pricing model is the most suitable for your overall advanced analytics program in relation to skills, funding and bandwidth.

Machine learning capabilities are often packaged as targeted software applications to solve specific problems. In fact, there’s already an enormous wealth of prefabricated solutions available, and it’s growing all the time.

These solutions often provide superb cost-time-risk trade-offs, significantly lower the skills barrier and can provide a solution much faster than creating one from scratch. Even for organisations with larger data science teams, packaged applications are an important consideration, especially as a productivity gain.

Alan Duncan is a research director in Gartner's Data and Analytics research group. He focuses on the business value of data and analytics, data-driven culture and analytics ethics. Alan is speaking at the upcoming Gartner Data Analytics Summit 2017, taking place 20-21 February in Sydney.

Copyright © 2017 IDG Communications, Inc.

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