Analytics Translator: The In-Demand Tech Job 20197 min read
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?
What is an Analytics Translator?
Analytics translators bridge the gap between an organisations technical expertise and operational expertise. They help to convey business goals to data professionals while ensuring data solutions provide insights that the business can use to inform decision making.
A Harvard Business Review published this year states that ‘companies are recognising that success with AI requires cross-functional teams and professionals who can translate business problems and customer pains into, for example, an AI solution.’
These ‘professionals’ are known as Analytics Translators. Over the next few years, Analytics translators are going to in high demand.
For example, in the US alone, the potential demand for this new job role may range from two to four million professionals in the coming years.
We’re going to break this down a little more, and look at:
The steps of the analytics initiative that the translator has to perform
The level of knowledge companies should require when hiring or building the skill set of their teams
Don’t you need lots of complicated qualifications to be an Analytics Translator?
To understand AI concepts and applications, you don’t need to be a native data architect, data engineer or a data scientist.
They don’t necessarily need to be dedicated analytics professionals or possess deep technical expertise in programming or modelling.
Their mission is to ensure that the deep insights generated through sophisticated analytics, especially AI, translate into impactful scale within an organisation.
Put simply, being a successful analytics translator requires a combination of:
Project management skills
An entrepreneurial spirit
How do you become an Analytics Translator?
The best people to take on the role of Analytics Translator are often already within your organisation and are prepared to step up and into the role.
So, how do you know which analysts make the best translators?
A Data Product Manager would be the closest match because they serve as the link between the analysis team and the end users.
Alternatively, a well-trained T-shaped growth marketer could be the perfect match for this career path.
In terms of personality traits or emotional skills, the ideal candidate for the Analytics Translator role should be a professional who is:
Open to new experiences, especially intellectual openness
Has a preference for non-routine tasks
Has a high need for cognition
Enjoys solving difficult tasks rather than simply trying to quit them
Ok, so once I’ve got my Analytics Translator, how do I start the implementation process?
Successful business implementation starts with being able to identify and prioritise problems that analytics and AI are suited to solve.
For example, in the case of machine learning, the recently published book Prediction Machines proposes the use of an AI canvas to help business leaders to use their domain knowledge to establish key operational metrics for task success and their comparable impact on profit, revenue and customer retention.
As a consequence, the translator would have to:
Predefine the priority of the project
Identify whether the company should build the model from scratch or whether these are off-the-shelf outsourced solutions already available (such as those provided by Google Cloud or Amazon Web Services, and at what cost)
Ensure implementation and continuous improvements according to customer feedback and new data
How can you get ahead in the artificial intelligence race?
Getting (and staying) ahead in the accelerating artificial intelligence race requires you to make informed business decisions about where and how to employ AI in your business.
A good way to test whether a businessperson has a reasonable understanding of the applications of AI in business is to check this executive guide to AI from McKinsey: grow.ac/aireport
Inspect how they have been matched to a corporate problem or a necessary product improvement with an AI solution – an essential skill for analysts or c-level professionals.
In summary, Analytics Translators bridge the data expertise with business operations, matching the proper AI solution and experimentation process with the correct business question.
So, do you think you’re ready to be your organisation’s next Analytics Translator?
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