To develop an effective multichannel marketing strategy, brands have to recruit people who can do magical things with data.
But is it data analysts they want, or data scientists? And how many really know the difference?
Our new Multichannel Customer Intelligence report sheds light on some of the key differences between these two roles, which one you need, and when.
In the report, Fitness First’s Group Marketing Director David Langridge says he is beginning to engage more with data scientists than analysts, and that this prompts an interesting question for many marketing leaders…
Who do I need and what do I need them for?
While both roles are extremely important in their own way, understanding the distinction between each one is important if you want to develop a strong data-led approach to multichannel marketing.
As with multichannel and omnichannel, the terms ‘data scientist’ and ‘data analyst’ are often used interchangeably by marketers as if they are one and the same.
One example cited in the report is a 2013 CNBC article titled, ‘The sexiest job of the 21st century: Data Analyst’, which then goes on to describe a role that is much more reflective of a data scientist.
Let’s take a look at each of the roles in more depth, according to the report.
The data analyst
Arguably the most important role of a data analyst is collecting, sorting and studying different sets of information.
This process looks different depending on the organisation, but usually the goal is to pin down a fixed value to some process or function so it can be assessed and compared over time.
The data has to be regulated, normalised and calibrated so that it can be taken out of context and used as standalone information or paired with other data without losing its integrity.
Analysts are generally tasked with drawing conclusions from the data and educating other in the business on how to use it.
They are often the ones with the best sense of why the numbers are what they are.
The data scientist
Data scientists, on the other hand, represent a kind of evolution from the traditional data or business analyst role.
While the formal training is similar, the thing that sets data scientists apart is strong business acumen coupled with the ability to communicate findings to senior leaders in a way that can influence how the organisation approaches a business challenge.
Talented data scientists don’t simply address business problems. They pick the specific problems that will have the most value to the organisation once solved.
Anjul Bhambhri, VP of Big Data Products at IBM, describes the role of a data scientist as part analyst, part artist.
A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a renaissance individual who really wants to learn and bring change to an organisation.
A traditional data analyst might look at data from a single source such as a CRM system. But a data scientist will most likely explore and examine data from multiple disconnected sources.
The ultimate goal is to discover a previously hidden insight that can provide a competitive advantage or help solve a business problem.
Ed Kamm, Chief Customer Officer at First Utility, says:
We have analysts for the more day-to-day stuff, but then also the scientists who have the ‘what ifs’.
For lots more insight about multichannel customer intelligence, download the full report today.