Analytics Translators

Mckinsey has called analytics translator the ‘new must-have role’ and has predicted we’ll need around 5 million of them. ⁽¹⁾

FIVE MILLION.

That’s more than the entire population of Los Angeles.

The problem is that no one really knows what the term ‘analytics translator’ means. Mckinsey seems to have slipped this term into usage in an article they published in the Harvard Business Review in 2018 ⁽¹⁾.

What’s in a job title? For the past ten years, we’ve struggled with the ambiguous title ‘data scientist’, then ‘citizen data scientist’. Now it’s ‘analytics translator’.

I largely ignore attempts to introduce new buzz words, but it seems this new term has taken root (unlike ‘citizen data scientist’). People seem truly excited by it. Within 18 months of publication of the HBR article, the University of Amsterdam had asked me to help design and teach a new ‘analytics translator training’ for professionals. The time had come for me to start formalizing the skill set for ‘analytics translator’.

Since Mckinsey was guilty of introducing the term, I wanted to understand what they might have meant by it. After a bit of digging, I found they were recycling content from their 2016 article, which discussed a shortage of ‘business analysts’⁽²⁾. So it seemed that during the 14 months between publication of these two Mckinsey articles, the term ‘business analyst’ had evolved into ‘analytics translator’. They must have decided it was time to introduce a new job title, never mind that we still haven’t clearly defined what a ‘data scientist’ is, despite the fact that half the planet is, was, or will soon be ‘data scientists’.

What Is An Analytics Translator?

Quite simply, then, an analytics translator, is someone who can understand business requirements and ‘data science’ possibilities. This skill set is actually extremely important. In a recent survey by O’reilly, 47% of respondents indicated this as one of the biggest challenges holding back adoption of AI / ML ⁽³⁾.

An analytics translator bridges the gap between business goals and data science possibilities. As per the Mckinsey article, “translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved”.

In this sense, ‘analytics translator’ is a skill set and not necessarily a role or a job title.  The concept is a bit like Ranger qualification in the Army. Some companies may have roles dedicated to gather requirements, and in this case the role itself could rightly be title ‘analytics translator’.  It is not, however, an equivalent label for a ‘data scientist’, ‘machine learning engineer’, ‘statistician’ or ‘product owner’ role.  At best, the term ‘analytics translator’ could substitute for the traditionally nebulous title ‘business analyst’.

Even if your company doesn’t use this job title, the skills related to the analytics translator concept are crucial, and you should make an effort to ensure that a large number of your staff are ‘fluent in analytics translator’

What Skills Does An Analytic Translator Need?

Facilitating the execution of data science projects within a business contact generally requires an understanding of business goals and processes; a high-level understanding of analytic vocabulary, techniques, technologies, and processes; and the ability to communicate cross-functionally.

In particular, the analytic translator skill set would generally include

Foundational Technical Understanding

1. Basics of classical statistics (regression, exploratory data analysis, hypothesis testing, correlation, etc)
2. Overview of common machine learning techniques (deep learning, SVMs, decision trees, adaptive boosting, clustering algorithms, etc)
3. Overview of technologies commonly used (programming languages, database concepts, deployment tools (docker, cloud), etc)
4. Understanding of techniques for model building & training

Foundations for Collaboration and Communication

5. Understanding of frameworks and tooling used by data science teams (scrum, kanban, Jira, git, etc)
6. Stakeholder management: setting expectations, building trust, change management
7. Techniques of communicating analytic results (as per Stephen Few, Cole Knaflic, etc)

Foundational Business Understanding

8. Understanding the goals and priorities of the diverse horizontal and vertical elements of organizations in which data science is only one team or department
9. Choosing the analytics projects most likely to deliver business value in the current economic and corporate environment

 

Regarding skills 1-4, a large number of data science teams are run by non-technical managers ⁽⁴⁾. These managers may not need to understand the technical details of team projects, but it is critical that they understand the difference between a high-risk, high-effort technique and a low-risk technique. They should understand the background terminology of project reports so they can focus on the elements that are unique to their business case. They should be able to ask basic questions to assure that the team is not taking unnecessary shortcuts related to model training or validation, selection of training data, etc. Unfortunately, it’s very common for data scientists to push hard to use cutting-edge techniques that are completely unnecessary, and the data science manager must be able to push back on this when necessary.

Regarding skills 5-7, it’s important to understand that much of the common tooling and methodologies (such as scrum) in use today were developed over the past 20 years by and for software developers, rather than data scientists. Data science teams must adopt and adapt the elements which will make them most effective in their unique tasks. Likewise, visual design and communication skills require special considerations when the subject matter is quantitative and especially when the audience is non-technical.  I’ve seen very few data scientists communicate well to non-technical audiences without special training.

I sometimes add 2 additional items to this list of skills, as part of a soft-skills training for data scientists, these are

10. Working in a multi-cultural environment
11. Dealing with office politics

 

Why Are Analytics Translator Skills So Important?

Analytics translator skills are crucial for two roles in particular: data science product owner and data science team manager. Without analytics translator skills, neither of these two roles will be able to bring to bear the full potential of data science within a business context. Analytics translator skills are also extremely valuable for data scientists (individuals specialized in advanced analytics), both to steer them in producing business value and in helping them work more effectively with business counterparts.

 

How Do We Fill Analytics Translator Roles?

I’d agree with the Mckinsey article that the best solution is to train existing staff, rather than hire into an analytics translator role. Existing staff will already have a deep, proprietary knowledge of your business and have already formed relationships with key stakeholders.  To illustrate, consider an AI healthcare company here in Amsterdam, who employees licensed medical doctors as product owners for their data science teams.  In such cases it’s especially clear that adding analytics translator skills to the hiring conditions is not reasonable, and that providing trainings is the appropriate solution.

Also, analytics translator skills are sufficiently general that most staff with a healthy dose of curiosity can learn them fairly quickly, and my experience is that a large number of experienced professionals are indeed eager to learn, given the opportunity.

Many companies are setting up internal analytics translator trainings.  For those without such trainings, part of the skill set can be learned through online courses, but there are relatively few open enrollment programs that teach the combination of business, communication and stakeholder management skills required.  I do teach some of these skills at the University of Amsterdam’s Business school, and I often give in-house trainings for analytics translator skills.  Feel free to reach out to me if you’d like more information and advice on how to set up internal trainings, or if you’d like information on my open-enrollment courses.  You can reach me at trainings@dsianalytics.com.

 

1. You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role. Nicolaus Henke , Jordan Levine and Paul McInerney. Harvard Business Review. Feb 2018
2. The Age of Analytics. Competing in a Data Driven World, Mckisey & Company. 2016
3. AI Adoption in the Enterprise, How Companies Are Planning and Prioritizing AI Projects in Practice. Oreilly 2019 
4. The State of Machine Learning Adoption in the Enterprise, Oreilly 2018