Analytics Translators translate Big Data, Analytics and AI technologies to flourishing business solutions.

The AIANDUS Analytics Translators Community is a fast growing network. Why join? For three beautiful reasons.

Does Analytics Translator sound like a dream job to you? Are you one already? Do you want to stay informed on the latest developments of this great profession, already dubbed the “new sexiest job of the 21st Century”? Them join the global Analytics Translators Community! Click here to join!

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It’s the job of the future!!

Everybody knows Data Scientists are scarse and it is a very good career. But in the coming years focus will shift from building Data Science solutions to getting business value out of it. That is exactly where Analytics Translators play a pivotal role. Or as AI guru Andrew Ng said it:

(Source: McKinsey & Co, How artificial intelligence and data add value to business – see the second video in the article)

There is hardly a better case to make for Analytics Translators than the following excerpt from a talk by Cassie Kozyrkov, Chief Decision Scientist at Google, at Strata UK 2019, (since Google is always a few years ahead of the global data science community):

Or, in the words of Chris Y. Zang (Data Analyst at Sustainability Victoria): “This is what you really should master in datascience. When, in a few years, the tools for ML and AI gets so intuitive to use, the true value of a datascientist are the core ‘human’ skills that are not easy to automate. So get good at those.”

Not convinced? Here’s a great video from Silicon Valley based venture capitalist AndreessenHorowitz (a16z) on why all businesses will become data-driven organizations and how all professionals will become operational analysts.

Thank you, Jad and colleagues.

Still not convinced? Here’s what the Data Scientists themselves say.

Thank you, Longhow Lam.

Talking to fellow data scientists at meet-ups, people in my network or at my courses I hear: “We’ve got models in production, we have an API or a process to score every week a batch.” OK, that is nice, but that is just a technical exercise, and that is becoming more and more easy with tools like , Dataiku, Sagemaker, Plumber and many others…. But when I ask how is the business using this model in their day-to-day operations and how much money do they make or save, it often becomes a little bit more quiet 🙂 Are we data scientists following the same destiny as some car manufacturers? Building many cars that no one drives, or the destiny that many BI-developers in BI departments have, building dozens of dashboards that are not really used.


If the activities described below sound like a dream job to you and if you have – or are developing – the skills described, then you may want to join the AIANDUS Analytics Translator Community!

1 – What Analytics Translators Do

As an Analytics Translator, you translate Big Data and Analytics based technologies, such as Artificial Intelligence, Robotics, Internet-of-Things and Blockchain, into viable use cases and ultimately into successful data-driven products and services.

Analytics Translators bridge the gap between the technical Data Scientists and Data Engineers and the non-technical Business community.

You act as a translator, because the business community typically do not speak the technical language of algorithms and programming, and the technical specialists lack knowledge and experience of industry-specifics and people and change related issues.

Analytics Translators have three key business result areas:

  • Making stories and decisions with data   — Facts and data don’t convince. Stories and emotions do! As an Analytics Translator, you translate analytics-derived insights into actionable recommendations for the business community, eg. through visualization.

  • Translating analytics to business — As an Analytics Translator, you work with Data Scientists, Data Engineers and the Business Community to develop successful data-driven products and services.
  • Building a data-driven organization — Data-driven is the 21st Century organization model. As an Analytics Translator, you lead or help develop a data-driven organization in terms of strategy, structure, systems, style, staff, skills, shared values and strategy execution.

The core of the Analytics Translator function is the second result area, Translating analytics to business. We use the 5 step Data Science process as defined by McKinsey:

  1. In identifying and prioritizing business use cases, as an Analytics Translator, you work with business-unit leaders to identify and prioritize problems that analytics is suited to solve.
  2. In collecting and preparing data, as an Analytics Translator, you help identify the business data needed to produce the most useful insights.
  3. In building the analytics engine , as an Analytics Translator, you ensure that the solution solves the business problem in the most efficient and interpretable form for business users.
  4. In validating and deriving business implications, as an Analytics Translator, you synthesize complex analytics-derived insights into easy-to-understand, actionable recommendations that business users can easily extract and execute on.
  5. In implementing the solution and executing on insights, as an Analytics Translator, you drive adoption among business users.

One more thing!

At Aiandus, we believe that, based on our skills, next to our business responsibilities Analytics Translators have a special responsibility towards society:

  • Contributing to society   — We believe, that Analytics Translators have the skills and the social obligation to contribute to a data-driven society. This starts with building trust in data and analytics, eg. by helping citizens manage their personal data. And to provide society with correct, data-driven information and fight fake news: develop the understanding that data-driven information and findings – eg. science – may be different from a person’s individual intuition or opinion.

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2 – What Skills You Need

  • A STEM background — Preferrably a STEM education with substantial mathematics and software programming. You need knowledge and hands-on experience with analytics methods to be accepted as a serious counterpart by your data engineering colleagues, eg. general technical fluency, knowledge of ML algorithms and what type of algorithm is used for what type of problem solving and the ability to understand Python or R code without being an expert coder.

    However … you can also be an Analytics Translator without STEM education or background. You must make sure, though, that you have credibility as a counterpart for the Data & Analytics community. This is why we differentiate between Type A and Type B Analytics Translators. See below for more information!

  • Domain (Industry/Function) skills — You must excel in a domain: either a functional specialization (e.g. marketing, finance, production, logistics, HRM) and/or an industry specialization (e.g. transport, insurance, banking, retail). Especially, you must be familiar with most of the use cases in your domain of technologies like online, AI, robotics, Internet of Things, blockchain and soon quantum computing. You work closely with (or as one of the) Product Owners or Product Managers.

  • People & Change skills — You need to be able to convince your organization to open up for the benefits of data and analysis from the data science team; also the many, many innumerate people, who frankly hate math and rather trust their intuition. The basis for People Skills is emotional intelligence. Examples are expressing yourself, building trust, understanding motives of other people, communication, collaboration, teamwork, coordinating with others, service orientation, convincing others, negotiation, credibility, people management and leadership. Next to that, you need to be able to lead (parts of) your organization through ‘technology driven change’, including program and project management skills, agility and an entrepreneurial spirit. You will play a major role in your organization becoming ‘data driven’: adopting data driven decision taking, developing data driven products and services, operating a data driven organization (‘data democracy’) and business processes.

What others say

Here’s a great video on what the people of Amsterdam based Growth Tribe have to say about Analytics Translators.

Of course, with reference to AIANDUS articles by Kees Groeneveld. Nice!

Thank you, Bernardo and colleagues.

And here is a fun promotional video from Amsterdam based data & analytics consulting boutique GoDataDriven on the crucial role of the Analytics Translator from the perspective of Data Scientists and Data Engineers.

Thank you, Rob and colleagues.

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There are two major groups of Analytics Translator: Type A (for Analytics) and Type B (for Business). The main difference is their background. The Type A Analytics Translator has a background in Data & Analytics and a focus on the Business, the Type B Analytics Translator has a background in the Business and a focus on Data & Analytics. Simply said, they do more or less the same thing but come from a different angle.

The differentiation makes perfect sense. Analytics Translator is a bridge function. And just like in real life, you can enter a bridge from two sides. Analytics Translators bridge the gap between Data & Analytics on one hand and the Business Community on the other hand. So, you either come from the Data & Analytics side (the Type A) or from the Business side (the Type B).

If you are familiar with Berkeley University professor Edward Ashford Lee’s book “Plato and the Nerd“, it’s the diference between a Literate Technologist (Type A) and a Numerate Humanist (Type B).


(A for roots in Analytics)

“Literate Technologist”

Key characteristics

  • Background in data, analytics or technology
  • Has credibility in the Business community
  • Is adopted by the Business community as a serious and knowledgeable counterpart
  • Understands the major business needs and issues without or with limited hands-on business experience
  • Is not an “analytical jerk


  • Computer science
  • STEM education
  • Econometrics education
  • Data & analytics experience
  • Hands-on programming


  • Hands-on data wrangling
  • Hands-on programming
  • Hands-on data engineering
  • Hands-on data science

Added value

Translates analytics to business based on in-depth knowledge of data science & analytics

Typical functions

In leadership roles

  • Chief Data Officer
  • Chief Digital Officer
  • Data & Analytics Team Lead
  • Lead Data Scientist

In professional roles

  • Full Stack/Unicorn Data Scientist
  • Data Product Manager
  • Data Czar/Curator

Also known as

Data Translator


(B for roots in Business)

“Numerate Humanist”

Key characteristics

  • Background in business or product development
  • Has credibility in the Data & Analytics community
  • Is adopted by D&A as a serious and knowledgeable counterpart
  • Understands Data & Analytics without or with limited hands-on data & analytics experience
  • Sincerely respects content-focused (‘nerdy’) people


  • Humanities (or Science) education
  • Business/Law/Economy education
  • Psychology education
  • Limited programming


  • Domain knowledge (is key!)
  • Product management
  • Business experience
  • Organisational insight
  • Change management
  • Project management

Added value

Translates analytics into business based on in-depth knowledge of the business domain

Typical functions

In leadership roles

  • Chief Data Officer
  • Chief Digital Officer
  • Manager Business/Product Development

In professional roles

  • Data Product Manager
  • Data Consultant
  • Data Analyst/Visualizer

Also known as

Business Translator


You hardly see job openings for Analytics Translators or people with Analytics Translator as their job title. Today. And although we expect that to change soon, currently Analytics Translator is more a role than a function.

As stated above, typical functions in the Analytics Translator role are:

  • Chief Data Officer
  • Chief Digital Officer
  • Full Stack (or Unicorn) Data Scientist
  • Data Scientist (with domain knowledge)
  • Business Manager (with datascience knowledge)
  • Business Development Manager
  • (Data) Product Owner/Manager
  • Project Manager
  • (Technology Driven) Change Manager
  • Data Consultant
  • BI Expert / Data Analyst
  • Data Visualizer


We truly believe that Analytics Translator is an excellent career path towards a CEO or COO position in a data-driven organization (which is the organizational model of the 21st Century, anyway).

An Analytics Translator career will typically follow the above mentioned three key business result areas:

  • Making stories and decision with data
  • Translating analytics to business
  • Building a data-driven organization

An example of building-up your career as an Analytics Translator is:

  1. As an INTERN – Clean-up datasets or work as a database administrator, get your hands “data dirty”
  2. As a JUNIOR – Start with making stories and decisions with data, eg. as a Data Analyst, Junior Data Consultant or Data Visualizer.
  3. As a MEDIOR – Start with parts of or small products or services as a Data Product Manager or Data Consultant
  4. As a SENIOR – Move towards groups of data-driven products and services as a Data Product Owner
  5. As a MANAGER – Be a Department Manager with focus on data-driven products and services or be a Data & Analytics Team Lead with focus on business results
  6. As an DIRECTOR – Be a Business (Unit) Manager with focus on data-driven products and sevices or be a Chief Data Officer transforming your company into a data-driven organization
  7. As an EXECUTIVE – Be the COO or CEO of your data-driven organization


Well, no.

There are quite many people who (1) do not qualify for a serious role in a datascience team because of a lack of

  • data, analytics, datascience, AI skills: eg. knowledge of and experience in analytical thinking: logic, understanding the limitations of analytics; mathematics: probability, linear algebra, avoiding Drew Conway’s “danger zone”; algorithms: regression, reinforcement, Bayes, KNN, K-means, ML/DL, training and deployment; coding: Python, R, C++, frameworks and libraries; technology: ETL, pipelines, platforms, etc etc.

and who (2) also do not qualify for a serious business role because of a lack of

  • business, industry, leadership, organizational skills: eg. having interacted with a customer of the organization, hands-on experience with the business’s products, services or sector, economic or financial insight, managerial or team lead experience, experience with organizational dynamics, having a personal network within or outside the organization, understanding organizational politics and skills to deal with it.

And that’s a problem. The problem is, that many people who lack both of these skillsets miraculously believe they can easily fulfil the role of Analytics Translator – in whatever function – assuming that ‘anyone with a bit of common sense can do it’ (I can tell: many of them call me for job opportunities, causing me to spend a lot of time on separating the chaff from the wheat). “I know little of business, and little of datascience, so I can easily act as a bridge between the two.”

No, you cannot. Forget it. Because maybe you do not have to be the best expert in business or the best expert in datascience, does not mean you don’t have to know anything about either! On the contrary. And for many obvious reasons, such as that you simply don’t have the skills required. But the most important reason is, that you will not be accepted as a credible partner by either the business community or the datascience team, and probably both.


How to bridge data science and business? With the right people!


Learn Why It’s The New Sexiest Job Of The 21st Century:


We have entered the era of Big Data. Data is “the new oil”, and Analytics is the way to “extract the oil”, ie. build a profitable Data-Driven business. Disruptive technologies like AI (Artificial Intelligence), Robotics, Internet-of-Things and Blockchain all heavily lean on Data and Analytics.

Not-engaging in Data and Analytics is seriously risking the future of your company. Smart business leaders understand this. Data Scientists are the key professionals who can make Data and Analytics work. So smart business leaders have already started building up a Data Science team.


But there’s a problem! Companies hire scarce PhD’s into prominent Data Science roles, often with excellent analytical skills. Unfortunately, most of them have limited experience in managing people and change, or little expertise in your industry.

For the maturing profession of Data Science to be effective in an organization you need both excellent analytical skills and excellent people & change skills and excellent function or industry skills. You need to “extract the oil and sell the petrol”.

So, today’s data science role is like asking someone to juggle three bowling balls! It’s too broad. The data scientist has become a jack of all trades.

In reality, once dubbed the sexiest job of the 21ste Century, data scientists spend most of their time collecting and processing data rather than finding business insights. Let alone, turning them into profitable products.

Often, the result is a frustrated data science team and frustration with the rest of the organization: data scientists blaming the rest of the organization not to understand the “absolutely brilliant things” they do for the business; and the rest of the organization clueless about how “these high-paid big data wizards” actually contribute to the business at all.


You can tell things go wrong in your organization, when  a data analytics pilot doesn’t get a follow-up. Or when a project simply fizzles out and never translates into a product, service or improved business process. It starts with colleagues not handing over their data to the data science team, for all sorts of reasons. Or compliance issues are raised. Or there is “no time now, no budget, no … etc”. Resistance to change, fear of losing my job …

There are thousands of ways to subtly thwart a data science project. It’s the beginning of the end of data science as a respected function in your organization, unfortunately throwing the baby out with the bathwater — seriously risking the future of your company.


As more and more large organizations now start to implement data science as a strategic capability, this is a growing problem. There seem to be three solutions. Companies may find a PhD who has it all: excellent analytical skills and excellent people and domain skills. Or they may hire a PhD with excellent analytical skills and retrain them with respect to people and domain skills. Or you can split the data science function in two: Data Engineering and Analytics Translators. The first two solutions are not our favorites.

Find the needle in the haystack

Of course, they do exist, data scientists with excellent analytical skills and excellent people and domain skills. And we congratulate you, if you have one. Cherish her! Others will try to hire her away from you. And if you do not have one, you may find one. But they are scarse – and very expensive. And there is always the risk of ‘prima donna’ behavior. Or they may decide to launch their own startup.

Retrain your excellent analytical PhD

Yes, in theory you could. But depending of the degree of people and domain skills of your PhD it may take a long time and a lot of effort. In general, for most people it is easier to learn new cognitive skills than it is to change behavioral patterns. Especially, if those behaviors have led to the success that people praise you for or when those behaviors are deeply engraved in your brain.

Besides, many data scientists don’t feel very comfortable with or even outright hate ‘internal politics’. Yet, that is an important skill to make data science solutions successful.

So, both solutions have significant drawbacks. To be honest, we may simply be asking too much if we want the whole package of excellent analytics skills, excellent people skills and excellent domain skills in one person. And it makes a company vulnerable and reliant. There is a better solution.


Our suggested solution is to let your PhD with excellent analytical skills do what they are good at (data engineering: mathematics & algorithms, software engineering & coding with some people and domain skills) and complement them with professionals who can be a respected counterpart to the PhD in the field of analytics (but no super expert) and who do have excellent people and domain skills: “Analytics Translators”. Together they establish the data scientist role.

In a recent Harvard Business Review article, McKinsey introduced the Analytics Translator role. Later, renowned analytics expert professor Thomas Davenport stated that “you don’t have to hire a PhD to run your Analytics or Data Science”. And they are right!

Data science is no longer a craft that one person can perform, it has become a serious profession with its own specializations

When the oil industry matured, they split their business into upstream (extract the oil) and downstream operations (refine and sell). Now that data science as a profession is maturing, it is time to make a similar split between data engineering (upstream) and analytics translation (downstream). Especially in large organizations, data science simply is no longer a craft that one person can perform, it has become a mature profession with its own specializations.

Here’s how Andrew Ng explains it (the crosses stand for AI projects):

Source: Coursera MOOC “AI for Everyone” by Andrew Ng (btw, highly recommended for newcomers in AI)

So, what is an Analytics Translator?

The role of an Analytics Translator is to bridge the gap between analytics and business. McKinsey have given a great summary of the Analytics Translator role in five steps for data science:

  1. Identifying and prioritizing business use cases — The Analytics Translator works with business-unit leaders to identify and prioritize problems that analytics is suited to solve.
  2. Collecting and preparing data — The Analytics Translator helps identify the business data needed to produce the most useful insights.
  3. Building the analytics engine — The Analytics Translator ensures that the solution solves the business problem in the most efficient and interpretable form for business users.
  4. Validating and deriving business implications — The Analytics Translator synthesizes complex analytics-derived insights into easy-to-understand, actionable recommendations that business users can easily extract and execute on.
  5. Implementing the solution and executing on insights — The Analytics Translator drives adoption among business users.

More about Analytics Translators

Are you an – aspiring – Analytics Translator? Do you want to know more about the role? Responsibilities, skills, etc? How to join our community? Then click here.



Data Scientists have become a jack of all trades. As the profession of Data Scientist is maturing, the need for specialization grows rapidly.

Data Scientist, the sexiest job of the 21st Century lost its sex appeal. The new sexiest job is really two jobs: Data Engineer and Analytics Translator

Many companies today want to become data-driven. But most people still feel uncomfortable with mathematics.


  • You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role, Harvard Business Review This is the landmark HBR/McKinsey article that started the public awareness and popularity of the Analytics Translator function. Success with analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and — perhaps most important — analytics translators.
  • The Fastest Growing Analytics and Data Science Roles Today, Bill Franks, International Institute for Analytics, June 2019 –  Yes, Analytics Translator is now the fastest growing role in Data Science and Analytics. “This is not a junior role that is used as a stepping stone. Rather, the role is a destination that senior business or analytics and data science professionals can strive for as they gain substantial experience, demonstrate leadership abilities, and are ready to try something new.”
  • “Physics Envy” and the Second – Class Status of Translators, Thomas Davenport, IAA – “(…) Some have suggested to me that highly quantitative individuals are less likely to be interested in solving routine business problems with analytics or AI. Others have said that hard-core quants have less interest in learning about the business and its customers. None of these issues are inevitable, but they seem to be pretty common. Another issue with an overemphasis on the highly quantitative is that they are the most likely group to be replaced—or at least heavily augmented—by automated machine learning.”
  • How To Avoid Analytics Failure, Steve Remmington, Minerra (on LinkedIn) – “Data Scientists are too focused on the relatively easy technical aspects of analytics initiatives and tend to ignore the relatively difficult and arguably more important business and people aspects.”
  • Do Your Data Scientists Know The Why Behind Their Work, Harvard Business Review – Without even mentioning the word ‘analytics translator’ this is no doubt one of the most powerful pleas for hiring them: “The biggest [data science] successes stemmed not simply from technical excellence but from softer factors such as a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address dozens of concerns among those impacted. Conversely, otherwise excellent technical work died on the vine when we failed to connect with the right people, at the right times, or in the right ways.”
  • It’s The Era Of The Analytics Translator, This Complex World – Organizations that are looking to be successful with data must not fixate on data scientists only. Analytics translators are essential to get real value from data.
  • Don’t Be An Analytical Jerk, Tom Davenport, IIA – Finally somebody had the nerve to point out the big elephant in the room about “attitudes and behaviors that often make analytics and AI leaders unsuccessful”.
  • Analytics Translator – The Most Important New Role In Analytics, Data Science Central, William Vorhies – Even ‘hardcore’ Data Scientist Bill Vorhies, “generally reticent to create new naming conventions for roles that have been intuitively obvious”, advocates the Analytics Translator role.
  • The New Analytics Translator: From Big Data to Big Ideas, McKinsey & Co – “The translator then needs to know enough about the nuances of various models to ensure that the team solves the client’s problem. (…) Translators then help the client integrate the analytics model and data results into their ongoing processes.”
  • Will Data Scientist Continue to Be the Sexiest Job, International Institute for Analytics, Tom Davenport – Automation of Data Science techniques, such as AutoML, is rapidly causing a shift of the added value of Data Science from pure analytics and coding towards translating analytics into viable business solutions.
  • Is Data Scientists Still the Sexiest Job of the 21st Century, Atos Consulting – How can you organize the Data Analytics function within your organization, while working on maturing your Data Analytics approach? Focus on translating analytics to business.
  • Forget Data Scientists And Hire A Data Translator Instead?, Forbes, Bernard Marr – Big Data guru Bernard Marr confirms that, “the choice is not whether to hire a data scientist or a data translator as you are likely to need both”. Marr has “had the pleasure to work with some fantastic data scientists that had both, the analytical skills and the data translation skills, but those are very rare and as such have sometimes been called unicorns”.
  • Analytics Translator: The New Must-Have Role, McKinsey & Co – The search for vital analytics talent has often focused on data scientists. In this article, McKinsey describe the overlooked analytics role that’s even more critical to fill.
  • Analytics Translator: The Must-Have Job for 2019, Growth Tribe – The future of jobs is changing. The Analytics Translator will be one of the most in-demand roles in 2019 and beyond.But what is it? And why does it matter?
  • The Next Role You Need to Fill—Analytics Translator, Futurum Research – Part peace-maker, part-techy creative, part visionary, the analytics translator’s job is take a good hard look at the company’s objectives and find ways to work with data engineers to get the results they’re looking for.
  • Why Your Company Needs Data Translators, MIT Sloan Review – In many organizations, there remains a consistent disconnect between data scientists and the executive decision makers they support. That’s why it’s time for a new role: the data translator.
  • The Analytics Translator – Turning big data into better business, Freshminds – Businesses that identify individuals with strong data skills and commercial ability to close the gap between human and machine interaction will find themselves staying ahead of the competition.
  • What Is An Analytics Translator and Why Is The Role Important to Your Organization?, Dataversity – As Self-Serve Advanced Analytics and Data Democratization becomes more common across industries and organizations, the role of the Analytics Translator will also become more and more important.
  • In Praise of Light Quants and Analytics Translators Deloitte, Tom Davenport – Organizations need people of all quantitative weights and skills. If you want to have analytics and big data used in decisions, actions, and products and services, you may well benefit from light quants and translators.
  • Making Big Data Deliver, London Business School – You need someone in your business to liaise between those in the existing business organisation and the data science team. This person speaks the language of both and is able to act as a “translator”.
  • The Age of Analytics, McKinsey Global Institute – The translator is the link between analytical talent and practical applications to business questions. McKinsey&Co. estimate demand for approximately 2 million to 4 million business translators in the USA alone.
  • The Changing Talent Landscape: Enter The Data Analytics Translator, Corinium –  One of the earliest publications we could find, mentioning Analytics Translators (June 1, 2017)
  • Why You’re Not Getting Value From Your Data Science, Harvard Business Review – An early warning sign (Dec 7, 2016) that things were not working in Data Science.
  • Data Scientist vs. Decision Scientist, DeZyre – Introducing the (now forgotten) “Decision Scientist”, an Analytics Translator avant la lettre (Sep 14, 2015).
  • The Sexiest Job of the 21st Century Is Tedious, and That Needs to Change, Harvard Business Review – One of the earliest warning signs (April 1, 2014) that Data Science was not working.
  • What Great  Data Analysts Do – And Why Every Organization Needs Them, Harvard Business Review, Dec. 2018 – “A frequent lament among business leaders is, ‘Our data science group is useless.’ And the problem usually lies in an absence of analytics expertise”, not in the absence of ML or statistics expertise.

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