Data scientists are getting paid huge salaries, presumably to infuse their businesses with insight and allow stunning differentiation from competition. However, many organizations aren’t using this field to its full potential.

• Fail #1 – Too Much Data, Not Enough Science
• Fail #2 – Lack of Clearly Established Goals
• Fail #3 – Chasing After Leprechauns
• Fail #4 – A People Problem
• Fail #5 – Thinking it’s Just a Trend
• Fail #6 – Demanding Black and White

We can all agree that the era of data science is in full swing. If we need any further evidence of that truism, we can look at the high salaries companies are willing to pay to specialists. For instance, in Australia, the average data scientist now makes $200,000 – the equivalent of $146,000 in USD – according to a report from data intelligence consultancy Which-50.

Although business leaders know that having experts who are focusing their efforts on data analytics is increasingly critical, companies still are not fully capitalizing on their data science investments. Nearly as many analytic experts say that their business is not making full use of their capabilities (37%) as say that their role is fundamental to their firm’s competitive advantage (40%). Utilizing a resource such as this is essential in this day and age of data gathering, that is why lead mortgage data intelligence and other forms of data intelligence, need to be further looked into for optimum output.

Part of the problem is the chain of command. “Executive level understanding of data and analytics’ was cited as analytic professionals’ biggest challenge in converting insight into action (47 percent),” notes Which-50.

What else is keeping businesses from thriving with their data science objectives?

Fail #1 – Too Much Data, Not Enough Science

Business is obviously fundamentally concerned with capital, but you can’t profit from data science without using basic scientific principles.

When you approach data science, in other words, you need to root out any potential bias. You come up with an objective and a hypothesis – basically question and possible answer. What do you want to know, and what do you suspect is true?

Furthermore, you need to make sure that your data is of high enough quality that you aren’t being misled.

Fail #2 – Lack of Clearly Established Goals

It’s important to align your data accrual and analysis process with business goals. Otherwise, strong ROI will often be elusive.

Above, the issue of leadership was cited as a challenge. However, data scientists often are out of sync with the business mission, notes Data Science Association president Michael Walker. “I see a lot of data scientists collecting everything without thinking about the business their client is in, the data they have now, and the data they’re going to need to do a better job,” he says.

Fail #3 – Chasing After Leprechauns

Everyone wants to hire that diversely knowledgeable data scientist, the leprechaun who might offer you some of his pot of gold. However, most data analysts have niche expertise.

Any project that works with big data should be considered a group effort. Disappointment will typically result from expecting a single person to establish insight. Some people will be better at experimental design, while others will be better at coding, and others will have an incredible grasp of probability theory. Combine and conquer.

Fail #4 – A People Problem

Long gone are the days when someone could casually describe themselves as an “Internet expert.” However, today the guru du jour is the data scientist. Part of the problem is that someone might technically have a “data science” degree, but all it amounts to is a marketing ploy by their university, notes Kennesaw State University data science PhD director Jennifer Priestley.

“You have a lot of programs that yesterday were operations research and today they’re data science,” she says, “or you had an MBA and now you have an MS in analytics or data science, but it’s the same curriculum.”

Fail #5 – Thinking it’s Just a Trend

Although there is a lot of hype surrounding data science and a lot of charlatans popping up like prairie dogs, being intelligent with data is becoming increasingly important.

Even the data itself is different, notes Priestley. “Technology enables us to treat audio, video, and text in the same way we have treated numbers like age and income, historically,” she says. “This is a sea change in the way businesses operate.”

Fail #6 – Demanding Black and White

To get back to probability theory, you have to embrace the idea of directing yourself toward what is probably correct. That’s the nature of data science. Executives often want to know what they need to do in order to get a certain result, but the best way to leverage data is to understand there is gray area and make reasonable educated guesses.

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