Business team analyzing data information of market.

What is an Analytic Translator? Why your organization needs (at least) one.

Nov 28, 2022

Investments in advanced analytics and Big Data are accelerating in almost every industry.

Over 90% of companies intend to increase their investments in analytics going forward.

Even more impressive, a whopping 96% of companies report using Artificial Intelligence (26%) or an intention to pilot AI projects (70%) this year. 

Accordingly, data science professionals continue to be in high demand, with a projected growth of 30% in positions by 2030. 

With all that growth and investment, we must be seeing incredible benefits, right? 

Not exactly.

Despite growing investments, signs indicate that Big Data efforts are falling short of expectations.

Reviews indicate that over 80% of analytic projects fail to deliver business value. 

Fewer than 20% of companies believe they are succeeding in building a data-driven culture.

So, what is the problem?

Studies of analytic performance come to the same conclusion:  the success or failure of AI initiatives has more to do with people than with technology.

An overwhelming 91% of company leaders say people and processes are to blame, more than inadequate technology or skills. 


It’s actually not surprising. Executives hire highly trained data scientists who use complex tools that most non-analysts do not understand.

At the same time, most data scientists are unfamiliar with business operations.

Too often there is a disconnect between analytic and business teams, resulting in costly rework and waste.

Introducing Analytic Translators

There is a straightforward solution: staffing teams with analytic translators.

First mentioned by McKinsey in 2018, and sometimes referred to as "data translators” or “analytics translators,” analytic translators have the responsibility of bridging the communication gap.  

An Analytic Translator is an advisor trusted by data analysts and business leaders to crystalize, explain, and shepherd complex analytic projects efficiently and collaboratively from initial concept to a relevant, insightful decision, or application, in ways that recognize and elevate the contribution of everyone involved (from Become an Analytic Translator).

Like language translators, analytic translators work between teams to make sure communication is clear and goals are aligned.

Analytic translators are acutely aware of the potential for misunderstanding and are equipped with combination of a working knowledge of analytic methods, a solid grounding in business priorities, and advanced communications skills. 

The goals of an analytic translator are to:

1. Recognize, highlight, and promote opportunities where data and analytics can explain or solve key business challenges. 

Top executives may not have a deep understanding of all data sources, analytic tools, or expertise available to investigate important business questions.

Similarly, data teams may not grasp the subtleties of evolving business priorities. 

Translators operate in between.

They become familiar with both environments and stay mindful of the priorities, advantages and limitations of each.

They identify opportunities others may not see.

2. Facilitate analytic projects that meet business needs efficiently and effectively by understanding both business and analytic domains.

Too often, business professionals make a rushed or cryptic request before it has been thoroughly considered.

What is the underlying business question, and how can we best answer it?

Or business professionals are unfamiliar with the sorts of data or methods that might be available.  

Conversely, data scientists may focus more on the technical aspects of data manipulation and modeling than the nuances of what the business needs to accomplish.

Because business teams and analytic teams operate in separate silos, misunderstandings are common. 

Whether in the initial request, during technical updates, or when results get delivered, there are many instances where miscommunication is likely.

While business leaders want quick, definitive answers, analysts want to apply appropriate methods and perform data checks.

Where analysts want to explain detailed limitations associated with data and models, business leaders would prefer straightforward conclusions.

Translators act as the go-between, making sure both teams follow what is happening, adjustments are understood, and results are clear.

In this way, companies avoid the rework caused by miscommunication.

3. Deliver analytic results in optimal language and formats for the audience.

Expertise is a double-edged sword. The more advanced our knowledge, the more specialized and unique terminology becomes.

When CFOs, pilots, archeologists, or data scientists speak with each other, they necessarily rely on their own unique vocabulary.

Within a profession, a precise lexicon helps teams build collective understanding.

When used between two professions, complex, technical language has the opposite impact: it divides and confuses.

Business leaders and data scientists can find themselves lost in each other’s jargon.

Analytic translators are trained to extract key findings that are necessary and relevant, converting them into clear, meaningful implications for a specific audience. 

4. Build strong relationships between business and analytic teams based on mutual respect.

Analytic translators understand that trust builds over time and appreciation develops from a consistent understanding of each other’s contributions.

As such, translators take deliberate steps to acknowledge team members’ roles and achievements in a project, educating both teams. 

By facilitating clear communication, avoiding misunderstandings, and delivering clear results, translators support positive collaboration.  

Why analytic translators will become essential.

The gap between analytic and business teams will only widen.

Computing and analytics get increasingly complicated and specialized every year (or month, for that matter).

Technologically, we are generating, collecting, storing, and processing more and more data, which require talent to digest and interpret.

Machine learning and AI techniques will help us handle the complexity, but the tradeoff is less straightforward: “black box” results that are harder and harder to explain.

Business leaders will be required to make critical decisions based on results they may not totally understand, created from methods they can’t comprehend.

To be successful, someone—trusted by both teams—will need to translate from one team to another.

There is a critical need for people dedicated to useful pathways that connect data capabilities to business problems.  

Data literacy won’t be enough. While efforts to improve general “data literacy” across all employees can promote acceptance of and appreciation for the value of data and analytics, it will not suddenly make complex statistical models simple to grasp. We still need skilled translators.



Ready or not, companies are launching themselves into a future that relies heavily on data and analytics, powered by new AI and machine learning capabilities.

So far, companies have largely failed to capitalize on this opportunity due to their inability to convert analytic insights into business advantage. 

A new type of hybrid professional—an analytic translator—will be a necessary team member to optimize the value of advanced analytics in business.

Unsure if you need an analytic translator or want to know more about what analytic translation is? Don't hesitate to get in touch with me.

Wendy D. Lynch's headshot photo.

Wendy D. Lynch, PhD

Wendy Lynch is an experienced sense-maker and data scientist with over 35 years of research experience, primarily in business settings. She has played the role of Analytic Translator for hundreds of companies, from start-ups to Fortune 100 corporations. Her expertise is both in data analytics and effective communication, combining the two into a framework for optimizing the value of analytics in a business setting.  Connect with her through LinkedIn or email.

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