Demand for big data expertise is growing every day, as more and more companies become aware of the benefits of collecting and analysing data. Unfortunately, the number of people trained to analyse this data isn’t growing in line with the demand. This creates a challenge for companies looking to hire expert people, especially for smaller firms less able to compete on salary and benefits.
The good news is that, even if you’re having trouble recruiting data scientists because of stiff competition, or if you simply haven’t got the budget to recruit, you can still access big data skills. Hiring in-house staff isn’t the only way – let’s look at some of the best alternatives.
Focus on attracting or developing certain skills
I believe there are six key skills required to work with big data: analytical skills, creativity, a knack for maths and statistics, computer science skills, business acumen, and communication skills. Rather than hiring people with these skills, you may be able to build on your existing skills in-house. For example, you may have an IT person who already covers the computer science side of things who would love the opportunity to learn about analytics. You could pair them up with a creative, strategic thinker who understands the business’s needs and you’re well on your way to having the skills you need without hiring anyone new.
Nurture your existing talent
Developing your existing people is a brilliant place to start, especially in smaller businesses or companies on a tight budget. Increasingly colleges and universities are putting courses online for free. Some of the courses offer certificates of completion or other forms of accreditation, some don’t. But the skills learned should be more important than a piece of paper.
Excellent examples include the University of Washington’s Introduction to Data Science course, which is available online at Coursera (www.coursera.org/course/datasci), or Stanford’s Statistics One course, also available on Coursera (https://www.coursera.org/course/stats1). For those interested in the programming side of things, check out Codecademy’s Python course.
Thinking outside the box
It’s worth considering unusual sources where you might be able to recruit help, either on a permanent basis or on a temporary basis (such as getting help to analyse data for a one-off project). Universities with a data science department, or any kind of data institute for that matter, are a good place to start. You could offer an internship, taking on some students to help with an analysis project, or you could see if the university is open to a joint project of some kind. If you’ve got data to crunch, they may very well be up for crunching it! In return you could mentor students on the key skills needed to survive in business or offer interview training and practice.
Thinking outside the box is really about finding creative ways to pull the necessary skills together in whichever way works for you. It may be easy to find someone with statistical and analytical skills but they may fall short on business insights or communication skills … but that needn’t be a problem if other staff could help supplement those skills.
Also consider whether there’s an opportunity to create an industry group with other companies facing similar challenges to your own. Even if you’re not keen to share detailed data with these companies (they probably don’t want to with you either), you can still pool resources to get data analysis done on a large scale without necessarily sharing your private data. Remember that data can always be aggregated or anonymised to remove specifics that you don’t want shared.
Harness the power of the crowd
You might consider crowdsourcing your big data project. Crowdsourcing is a way of using the power of a crowd to complete a task. (If you haven’t heard of crowdsourcing before, you’ve probably heard of crowdfunding platforms, like Kickstarter, which operate on a similar basis – using the power of a crowd to achieve a goal.)
A few crowdsourcing platforms, like Kaggle, now allow thousands of data scientists to sign up for big data projects. A business can then upload the data they have, say what problem they need solving, and set a budget for the project. It’s a great option for companies with a small amount to spend, or those who want to test the waters. But it’s also a regular resource for big firms like Facebook and Google. Some firms are even known to recruit full-time analysts from crowdsourcing platforms if they’ve been blown away by the work they’ve done. This gives you an idea of the quality of talent on crowdsourcing platforms.
Tapping into external service providers
If none of the above options work for you, you can still make the leap into big data. A great way to supplement missing skills, particularly when it comes to the statistical, analytical and computer science aspects, is to hire external providers to handle your data and analytics needs. There are more and more big data providers and contractors springing up who are able to source or capture data on your behalf and analyse it (or work with data you already have). Some big data providers are household names, like Facebook and IBM, but you certainly aren’t limited to big blue-chip companies. There are tons of smaller providers out there who have a great deal of experience working with small and medium-sized firms, or expert knowledge of specific sectors.
As always, I’d be interested in hearing your thoughts in the comments below.
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Businesses of all shapes and sizes can access big data skills – and on almost any budget. My new book Big Data for Small Business For Dummies is packed with ideas and information on how to get started with big data, along with real-life examples from a wide range of sectors.
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