"Analytics translator": a new job to link data scientists with the rest of the firm.

Data scientists get paid really well. But what if you can’t quite hack the … hacking? And maybe your skills and passions are more business-focused? Well, here’s a new type of job that is crucial for successful analytics projects.

Data science is moving so fast and it is difficult to keep up. The maths and hacking subsets in Drew Conway’s data science Venn diagram are expanding like crazy. There are tonnes of new data analysis technologies like AI. The types of data to analyse are growing alarmingly as we record ever more of our lives with different Internet of Things devices. And it seems like every day a new start-up thinks of some novel applications.

I’ve often wondered what it would be like to be a hard core data scientist. And I love what new analytics technologies are doing with data and how they are changing the world.

But after my MBA I found that my real passion is with what data can do for the business, not the detail of how the data is manipulated.

Maybe you are the same? Maybe you have a degree in science, engineering or maths. Or maybe an MBA?

You actually get the maths and coding in a data scientist job. But there are only so many hours in the day and your career has taken a more managerial route. Something less technical and more strategic. More commercial and less about the tech?

If you love technology. But you also want to change how things get done in your firm or your industry. And maybe help to change the word while you’re at it.

Then this is your chance. A job as an Analytics Translator could put you right in the middle of all the amazing new AI and analytics technologies that firms are just starting to work with.

What is an Analytics Translator?

An Analytics Translator is the bridge between the hard core data scientists and the rest of the business. What Tom Davenport also compares to a ‘light quant’ and SAS talk about a “translation layer”.

Analytics Translators have the business understanding to see where the tools of data science can be used in a firm and the communications ability to explain it to all the relevant people.

Successful analytics projects are made up of lots of different skillsets and business functions. The wider the impact of any project them the more people you need to get engaged. Mckinsey see the need for a role which bridges data scientist and business.

Imagine a customer journey personalisation project to look for events in customers lives that no one has a name for. It will need people from marketing to finance, people from operations to customer service – all the usual cross functional input.

An analytics project could revolutionise how things are decided in some part of the firm. But it needs to include someone who understand that part of the firm. First to see an opportunity and then to nurture and deliver the outcomes of the project. Which is a rocky road all by itself.

7 key tasks of an Analytics Translator – the analytical vs the business sides

1.   Spot where data analytics could help the business – analytical capabilities vs business needs

2.   Prioritise ideas for data science projects – project ideas vs the firm’s situation

3.   Write the business case – technical and other benefits vs the costs and business capabilities required

4.   Explain the project to data scientists – technical requirements vs the business requirements

5.   Keep the project moving – navigating unforeseen blind alleys and unexpected opportunists vs some business benefit

6.   Selling the project outputs – digital insights vs better different business decisions and appropriate actions 

7.   Implementing to realise benefits – all the usual change management skills, from a formal business case to delivering the implementation project on time and on budget

These tasks need someone who has spent a lot of time understanding the firm. Someone who knows how data generates value through better business decisions. Someone who knows the right people to convince and to get involved. Someone who can explain the technical side and the business side.

Finding a data scientist with all these communications skills, a deep business knowledge and an entrepreneurial imagination is very rare. Because they are specialists.

Why do data scientists need their hands holding? Because we all specialise.

I’m not saying that data scientists are unworldly nerds. In fact I think a lot of data scientists should get themselves an MBA and start running the company.

Many are doing even better and starting their own firms. We have only just started to see how analytics technologies are changing industries and society, and data scientists are leading this change.

What I’m saying is that business is complex and so is the unfolding mixture of technologies and data which we call data science.

Data science technologies are new and quickly developing. New data sources and applications are dynamic and untried. It is very difficult to be a specialist in more than one specialism.

However, all analytics projects do need a person with one eye on the data science and one eye on the “big picture”. The “big picture” means the firms’ situation, the context of different stakeholders, the market’s direction, technological change the legal and ethical background. Or some other indirect hazard which might bite you if you don’t watch out.

This is the Analytics Translator – the bridge between data science and everything else.

What can you do to become an Analytics Translator?

If you are a functional specialist with a deep understanding of marketing, finance or some other business area then try some of these online courses on data science and get a feel for it. It will help if many years ago you spent your undergraduate years doing something sciencey or mathsy, in between “socialising”.

But that’s not critical. Most firms desperately need to develop the skills of an Analytics Translator. So have a chat with HR and see if the firm will pay for some Continuing Professional Development (CPD).

If you are a grandmaster arch data scientist with two out of three of Drew Conway’s data scientist skill areas. Then get out into the business more. Your colleagues will love you. To be frank, right now they need you more than you need them.

They might seem a bit weird at first but that’s because they are nervous of you. They know that AI and Big Data is changing everything in the industry. And maybe you can save them?

So be modest, ask more than you tell and learn as much as you can about your business.  

How can firms get some Analytics Translators?

If you are a senior manager then you have realised that you must get some Analytics Translators. Especially, if you usually borrow expensive data scientists from a consultancy or if you are considering renting some AI.

So why not train them up using some current staff? Analytics Translators have to understand your business and your staff already do.

Have a chat with HR, get them to look for functional specialists. Maybe with something sciencey or mathsy in their past. And certainly with an investigative itch that would make Sherlock Holmes appear quite unambitious.

Duncan is a lecturer at Nottingham University Business School. He also advises organisations on creating value with digital data and he writes his own blog here.