Business Intelligence Trends: Why data scientists are in high demand?

Business Intelligence Trends Why data scientists are in high demandData scientists are one of the highest growing occupations in the US. In a 2011 report, McKinsey Global Institute estimated that by 2018 there will be 4 million big data related positions in the US. Considered one of the most attractive positions to pursue according to Information Week which is not surprising if you take into account the median salary for a data scientist in the U.S. is a not too shabby $115,000 according to Glassdoor.

It’s not the first time we’ve heard of this. If you follow business intelligence then you already knows that the field of data science is growing. The big question is “why is the field growing at such a precipitous rate.” In a TNW article titled, “How data scientists are changing the face of business intelligence,” author Nick Cicero, Director of Client Strategy @Expion and Editor @socialfresh, gives us his thoughtful explanation. Below are important snippets from the article.

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1) Accessibility to data is greater than ever before

The rise of online communities, e-commerce, mobile and the overall digitization of society has created new data sets that go beyond just number crunching.

Companies look at the different interactions by different users within the site, from how they came in, to the activity taken on the site, to how they completed a particular activity. With the data, teams can look at the optimal characteristics of users who have satisfied their goals.

The tweets we post, the reviews we leave on Yelp, the blog posts we write, the searches we make… they’re all adding an additional layer on the data puzzle that really hasn’t been seen before, revealing new relationships about groups of people and their behaviors.

2) Data is now crucial to product development

Data’s influence on product development is easily seen in nearly every company today. Optimization based on this influx of data helps drive actionable business decisions in real time, whereas before data scarcity forced iteration based on gut, or unreliable and outdated information.

Imagine your website was your primary product. “Maybe you can’t write an algorithm to automatically increase conversions on your site, but you could possibly write an algorithm that makes it easy for a human to understand what’s happening on the site, then the human can make the change in their behavior.“ -Matthew Ruttley, Data Scientist at Mozilla

“One time we tried changing the color of a button in the app and it didn’t change the experience much, then we changed the copy on one of the buttons and that drove up conversions 15 percent.” – John Sandall, Data Scientist at YPlan.

3) We live in a balancing act of privacy

Today’s free-flowing exchange of ideas, goods and services is punctuated by one overarching relationship that’s constantly at the center of debate: the right to data.

As mentioned before nearly every interaction is measured, giving these companies more access to map personas of the people using their technology. For the most part, this is a good thing – it helps the user experience improve and makes the things we love even better.

But a big part of that is also making these companies sustainable in the marketplace for the future, and that means experimenting and monetizing.

Earlier this year Facebook came under fire for just that. A team of Facebook data scientists published a research study conducted by altering 689,003 users’ feeds over a week to test their emotional reaction.

4) A more complex role than you think

Perhaps one of the main reasons that data scientists are in such high demand is that they require an incredibly diverse skill set, which can sometimes be hard to find in people.

“The data scientist is someone who has fantastic communication and empathy ability, as well a lot of mathematical skill, and then the engineering skill they need to do the math that they want to,” says Hilary Mason, Founder at Fast Forward Labs.

The data scientist spends a lot of time working on statistical analysis, developing machine learning algorithms, hacking and mining for data, but also managing and communicating with multiple different teams within an organization.

Click here to read the entire article called, “How data scientists are changing the face of business intelligence,” by Nick Cicero.