But knowing that doesn't necessarily simplify the task of hiring qualified data scientists. Booz Allen Hamilton's "The Field Guide to Data Science" is a 110-page primer that goes into great detail on how to build a data science team. We've summarized some salient details below.
When building your team, it's important to focus on these key qualities:
Curiosity: Required to "peel apart" problems and study relationships between data, including those that may at first glance seem unrelated.
Creativity: For devising and attempting new problem-solving approaches and potential solutions that haven't been tried before.
Focus: Essential for designing and testing techniques over lengthy periods (days or weeks). A tenacious attitude is important for learning from failure and trying until you get it right.
Attention to detail: Important for maintaining rigor and for avoiding an over-reliance on intuition when analyzing data.
Of course, proficiency in key technical disciplines is required, too, specifically computer science, domain expertise, and mathematics.
A computer science background is essential for data processing and manipulation. Advanced math skills, including a solid background in calculus, geometry, linear algebra, and statistics, are required for understanding the basis for algorithms and other data science tools, the report said. And domain expertise helps the data scientist understand the problem at hand and how to measure it.
Where do you find data scientists? Your first impulse may be to look outside of your organization for qualified candidates, but the Field Guide recommends first looking in-house for people "who have a high aptitude" for data science.
Potential team members will likely have advanced degrees in the three technical areas described above, but you shouldn't immediately dismiss candidates who don't.
"Don't discount anyone -- you will find data scientists in the strangest places with the oddest combinations of backgrounds," the report states.
Leadership skills are important, particularly for the first members chosen for your team.
One common weakness of data science teams is an inability or unwillingness to imagine new and different approaches to problems. You'll want to foster an environment of openness, one that encourages "trust and communication across all levels, instead of deference to authority." Managers should encourage data science team members to speak up and ask questions frequently.
In addition to building the team, you'll need to choose an operating model. The Field Guide suggests three options:
A centralized team that works under a chief data scientist and serves the analytical needs of the entire organization
Smaller data science teams deployed to specific business groups for short- or long-term assignments
Diffused teams embedded over the long term within each business group
Political, not technical, problems are often the biggest challenges facing a data science unit, particularly if management is ambivalent about the team's mission.
To prove its value, a team "needs to initially focus on the hardest problems within an organization that have the highest return for key stakeholders." Doing so can change (ideally for the better) how the organization approaches future challenges.
A data science team also needs support from management to lessen in-house "fears and doubts" about its mission. Leaders must be strong advocates to ensure "widespread buy-in" of the team's objectives, the report says.
Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger's Personal Finance, the Saturday Evening Post, and InformationWeek.
You can use distributed databases without putting your company's crown jewels at risk. Here's how. Also in the Data Scatter issue of InformationWeek: A wild-card team member with a different skillset can help provide an outside perspective that might turn big data into business innovation. (Free registration required.)
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
Top IT Trends to Watch in Financial ServicesIT pros at banks, investment houses, insurance companies, and other financial services organizations are focused on a range of issues, from peer-to-peer lending to cybersecurity to performance, agility, and compliance. It all matters.
Join us for a roundup of the top stories on InformationWeek.com for the week of September 18, 2016. We'll be talking with the InformationWeek.com editors and correspondents who brought you the top stories of the week to get the "story behind the story."