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Hadn't heard of Analytics Translators before?

That’s possible, it’s a relatively new role in organizations! But it’s fast growing, as companies want to make money out of new, analytics based technologies, such as AI, blockchain and IoT.

Already existing examples of Analytics Translators are Data Scientists with good understanding of your industry and organization, Product Owners and Product Managers of data-driven products or services, Chief Data Officers and many Chief Digital Officers, and Data Visualization Experts and BI Experts.


Big Data, AI, Robotics, IoT, Blockchain Use Cases For Your Industry Or Function


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 scares 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.

  1. You may find a PhD who has it all: excellent analytical skills and excellent people and domain skills.
  2. Or you may hire a PhD with excellent analytical skills and retrain them with respect to people and domain skills.
  3. Or you can split the data science function in two: Data Engineers and Analytics Translators.

The first two are not our favorites. Both 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 you 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.

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.
  • 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.
  • 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.
  • 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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What we believe

At the core, we at Aiandus believe in passionate people. And we believe that technology can help bring prosperity. We believe that, equipped with the latest of tech, the power of passionate people can change the world for the better. We feel this so strongly, that we put it in our name: AI-and-us!

What we do

But technology, especially the latest of tech, can be complicated to many of us, can be difficult to work with and to some it is outright scary. Therefore, we see it as our role to bridge the gap between new technologies and humanity, especially between the latest of tech and people at work.

How we do it

Most of the latest of tech are data-driven and analytics-based technologies, such as AI esp. Machine Learning, Robotics esp. RPA, Blockchain, Internet-of-Things and even CRISPR-cas. Therefore, for companies we deliver three services:

  1. Consulting – We consult companies on how to design and develop a (big) data, analytics and analytics translation capability in their organization of passionate people and the latest technologies to gain a competitive advantage and become a data-driven organization.
  2. Staffing – We deliver passionate temporary, usually project-based, staff who believe in tech to help our customers translate their data and analytics into great products, services and processes.
  3. Recruitment – We help our customers build a passionate workforce that believes in tech and that is capable of translating their data and analytics into a data-driven organization.

For professionals we also deliver three services:

  1. Career advice – We help professionals prepare for or adapt to data-driven roles in the new era of data & analytics.
  2. Skill training – We curate Analytics Translator skill training content and help professionals complete the learning process.
  3. Community – We organize professional communities for Analytics Translators.

Analytics Translators

We are Analytics Translators. At least, that is what McKinsey call us in Harvard Business Review. We bridge the gap between Business and Technology.  We make Analytics based Business solutions work!

We are a team of experienced professionals with good analytics skills and excellent people, change and domain skills. We know how AI, robots and cognitive systems work. And we know how people can live up to their true potential in a changing environment. We help organizations implement AI systems, robotics, and cognitive tools. More specifically, we help people in organizations work with smart systems.

Our professionals

Our professionals have in-depth knowledge of AI systems, robotics, and cognitive tools. Typically, we have educational backgrounds at university level in disciplines like mathematics, physics and software engineering.

We are AI, Robotics and Machine Learning enthousiasts. But we are not just “geeks” (for whom we have deep respect, by the way). All of us have a strong personal interest and tangible experience in human behavior. We have in-depth knowledge of applied behavioral science. We have many years of experience in change management strategies. Needless to say, that we are especially passionate about making people work with AI.

Our leadership

Aiandus was founded in 2016 by Kees Groeneveld (internationally a.k.a. Case Greenfield). Kees is the Managing Director of Aiandus. He has a university degree in Theoretical Physics and Mathematics and a business background in Software, Strategy and Change at big renowned ICT companies and consulting firms, such as PwC, Capgemini and KornFerry HayGroup.


Challenge us

Do you know, that big data & analytics based technologies – like Artificial Intelligence (AI), Robotics, Internet-of-Things, Blockchain – (will) play a big role in your business? And you know that bridging the gap between technology and business is critical for the success of your organization?

Then, maybe we can help. If you want to know how our services may suit your specific business needs, please contact our office and let’s discuss. Won’t cost you more than a cup of coffee. Promise!




INT+31 (0)35 – 628 7197

Visit address

HNK Building Amsterdam Arena, Burgemeester Stramanlaan 105, 1101 AA Amsterdam, NL, Europe


Twitter – twitter.com/aiandus

LinkedIn Company – linkedin.com/company/aiandus

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Github – github.com/aiandus

YouTube – youtube.com/channel


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