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How Large Companies Are Building Big Data Teams, And How Startups Can, Too

This article is more than 8 years old.

Big data is big business, but it can be hard for startups to understand how they can get in the game. Fortunately, the same tactics that Fortune 500 companies are using to create big data teams apply to startups as well, although the tools and techniques you choose to implement will certainly be different.

"Traditionally Enterprise has heavily invested in Big Data teams and infrastructure, but what we are seeing now is democratization of Big Data - the tools/services to collect and analysis data at scale are now within the price range of mainstream business. It's the start of something very exciting'
David White, CEO Import.io

Start Small with Big Data

All teams - whether they’re Fortune 500s or small startups - have a tendency to bite off more than they can chew. This is especially true with big data, which can be hard to define to begin with.

To build an effective team, start by making sure you understand your business goals and your purpose for diving into big data. Paytronix, a company that helps restaurants build brand loyalty and gain data-driven insights into their customer base, narrowed their initial project down to a single question: “Who had breakfast for dinner?” It may sound trivial, but this question proved to be the stepping stone that helped both the customer and the team understand what they were aiming to accomplish.

In your own big data efforts, make sure you’ve narrowed your focus to one - or a few, at most - simple, understandable questions that both your team and your clients can easily grasp. For example, don’t set out to understand every possible demographic or behavioral insight your data can provide. Pick 1-3 scenarios that will provide you the most immediately actionable information and focus your big data efforts around these key questions.

Remember, you can always go back and dig deeper later on!

Address Talent Issues

Though it may seem as if Fortune 500 companies have an advantage here, just about every company out there has trouble finding the right talent to perform the kind of data deep-dive that’s required to analyze big data. The fact that big data manipulation is a relatively new skill set means that the number of professionals performing this type of work is still small - and that’s no less true in big companies than it is in smaller startups.

Demonstrating this effect in action, Price Waterhouse Cooper recently did a survey of 1,100 business and technology executives, and found that only 44% of them felt they had enough talent to capitalize on big data. As a result, addressing this potential lack of talent should be the first thing you handle as a startup looking to capitalize on big data.

Fortunately, as a startup you have a lot more flexibility than a lot of other companies when it comes to choosing and training your staff. PwC recommends that you take the following steps to address talent needs when forming a big data team:

  • Break down your talent needs. Are you looking for help with business analysis, analytic expertise, data technology, or visualization? The clearer you can be about what you’re looking for, the easier it will be to find the right worker for your needs.
  • Scan your staff for those who have the skills you need, even if they don’t work in data or analysis right now. Training internal candidates will save you the recruitment and onboarding time associated with bringing on a new hire.
  • Fill talent gaps with hiring and recruiting. Recruit for the select capabilities you need. Don’t look for unicorns - seemingly-perfect staff members that don’t actually exist. Instead, focus on people like statisticians that you can source easily and train.
  • Cross-train your team to promote collaboration and minimize the effect of losing a team member to another project or a different company. The last thing you need to deal with is a key stakeholder leaving your big data team in a lurch.
  • Empower your staff members with the freedom to explore and discover unexpected insights. When properly encouraged, your team will be more motivated to grow and develop their skills over time.

As you hire, don’t overlook the benefits of remote staff. This is where the flexibility mentioned above comes into play. If it isn’t in your startup’s budget to hire a full-time staff member (with a full-time salary and benefits package), consider bringing on a consultant or take advantage of remote overseas hiring to get the big data help you need at a price you can afford.

Choose Your Tools Carefully

Once you’ve defined your business goals and analyzed your staffing needs, you’re ready to begin choosing the tools you need. In particular, you’ll need the specific tools that address the problem you’re trying to solve, and that work within the culture of your specific startup. Again, this is a place where small companies actually have a certain advantage. While they may not have the funds to buy an enterprise-level solution, they’re limber enough to move quickly in order to find what works and what doesn’t.

One pitfall to avoid is falling for the “latest and greatest” new solution in the world of big data tools, as the industry’s hot new product may not work in your environment or help solve your particular question. There’s a lot of hype about hot technologies in the big data field, and it’s easy to be led off-course by well-meaning marketers. Focus on your particular solution and your particular team’s needs, and ignore the hype.

A Few Recommended Tools:

Consider How to Make Insights Highly Useable

The final step in the big data process is to make sure your team is focused on generating data insights that are actually actionable to the client - whether the client is your own company or an external customer. To do this, you may want to utilize cloud technologies or consider some version of self-serve analytics. But however you choose to deploy your solutions, make sure your team is trained and capable of using the software involved to produce meaningful results.

While a larger company may have various deployment options, a startup may need to focus on only one due to staffing or budget limitations, as this will help minimize the need for additional employees or training. That said, you’ve got to make sure that you’re very good at the solution you choose so that your big data team’s hard work does not go unnoticed and unused.

Creating a big data team at a startup may seem difficult, but the same steps apply to small companies that apply to the Fortune 500. Set your goals, start small, get the right talent, and make your insights highly usable. All it takes is these steps to make your team successful.

Has your startup built a big data team? If so, share what you’ve learned from the experience by leaving a comment below!

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