4 qualities marketers should look for in a data scientist
Demand for people with the expertise required to learn from and experiment with data is soaring.
According to a survey of 2,719 business professionals from around the globe - which was conducted by MIT Sloan Management Review and SAS - 63 per cent of organisations are delivering formal data analysis training to their employees.
Data science versus analysis
Marketing departments are no exception to this rule. If marketers expand their data science capabilities, they can reinforce their creativity with educated insights.
But what should marketers look for in a data scientist? To clarify, we're not talking about data analysts here. iCrossing Head of Research and Insight Sam Vining told B2B Marketing that a data scientist is capable of experimenting with information to identify new methods of using it.
In other words, whereas a data analyst uses analytical models to learn from data, data scientists develop those analytical models based on the information at their disposal.
Now that we know what a data scientist does, what characteristics should you look for in such a professional?
1. Proactive
In 2014, SAS asked 569 data scientists from the UK and Ireland to fill out a confidential psychometric survey consisting of 24 questions. The software company scrutinised the responses and classified participants into several personalities, one of them being 'The Drivers'.
These particular data scientists are introverts, but they're proactive. They're incredibly practical individuals who use the resources at their disposal to drive projects to completion. These are the kind of people you need at the head of audience enhancement profiling initiatives and other data science endeavours rife with complexity.
2. Communicative
In any team-oriented effort, it's helpful when the parties involved exercise transparency. This is particularly applicable to data scientists. With such a technically complex subject, ensuring data scientists can explain intricate problems succinctly is crucial to keeping projects on track.
This trait is especially important when marketers are simply trying to get data science initiatives off the ground. If a data scientist can explain to a CMO why investing in such a project would benefit the company, your team will have the support it needs moving forward.
3. Dynamic
EMC Global Services Director of Business Operations Frank Coleman maintained that ambiguity is a part of the job. When experimenting with data, a scientist may come to a point where the next step in the process isn't necessarily clear. Based on the insights a data scientist derives from his or her investment, his or her original objective may be no longer applicable.
Bottom line: A good data scientist knows how to make informed decisions in the face of uncertainty. For instance, if an unforeseen bit of information arises out of a cross channel targeting endeavour, a data scientist shouldn't be undeterred or discouraged.
4. Creative
In an interview with The Data Warehouse Institute (TDWI) Bill Franks, chief analytics officer at Teradata and author of "Taming the Big Data Tidal Wave", maintained that creativity enables data scientists to make sense of befuddling situations.
"The fact is, in school it might be by-the-book, but in a business environment, there are so many unknowns - the data is not as clean as it should be, for one thing. It takes lots of creativity to figure out how to take this non-perfect set of data, analytics, methods, and business problems you've been given and make something really useful out of it," said Mr Franks.
Of course, technical know-how is a necessity. Being able to work with Hadoop, MySQL and other systems is one of the prerequisites of a capable data scientist. However, applying the aforementioned skill sets to scrutinising consumer data libraries, market share statistics and other information is necessary.
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