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How To Avoid Gathering Too Much Data (Yes, There Is Such A Thing)

With all the talk about big data lately, you might feel your company needs to collect stats about everything — constantly. If you do, though, you could end up with data overload. The consequences of that include obsessively collecting data to try and resolve issues, but being so caught up in that goal that you never move forward. You might also feel reluctant to make any decisions unless it’s possible to back them up with massive amounts of data. Intuitive decision-making then becomes a virtual impossibility.

Let’s take a look at some actionable strategies you can employ to make sure you’re collecting data in a way that makes sense for your business — instead of just doing it to jump on the big data bandwagon.

1. Pick Your Purpose

One of the most practical ways to keep your collected data amounts to a reasonable level is deciding why you need the information. In other words, what will you do with it? Retailers like Wendy’s and Starbucks use big data to pick store locations. You might use it to identify unmet needs. Once you’ve settled on a reason for gathering data, it’ll be easier to focus on only the most relevant information.

2. Use Careful Collection Processes

Compiling data about your customers or target market could also get you in trouble if you don’t have a meticulous approach in place. Unscrupulous hackers could reveal data you shouldn’t have, or you could mistakenly collect data about kids without their parents’ permission, which could garner unwanted attention from the Federal Trade Commission. Before diving deep into big data, make sure to have a clearly defined process for how you’re collecting data and storing it after it’s in your possession. Otherwise, you might become stuck in a situation that could hurt your reputation.

3. Come Up With a Data Collection Plan

In the same way that startup businesses and marketers write detailed plans before beginning a project, you need to address key points before getting started with big data. For example, once you have the information, who will access it? How will you keep the data protected? What’s the cost for those security measures? How would customers feel if they knew you had this information about them and do you have a crisis plan in case of a backlash? Answering those questions will make you feel more equipped at all stages of the process and aid you in determining if some of your techniques need improvement. It’ll also help ease the minds of stakeholders who may be concerned you haven’t thought things through before working with big data.

4. Ask Customers About the Data They’re Willing to Give

If you suddenly begin asking customers for data, they might become wary and even take their business to other companies that seem less intrusive. The amount of willingness shown could be largely generation-dependent. A survey conducted by Aimia found that 51% of 18-34 year-olds in the U.S. were okay with sharing their mobile phone numbers, but that was true for only 30% of baby boomers.

When coming up with your strategy, it’s important to think like your customers. If they’re just signing up for an email list, they might wonder why you’re also asking for date of birth and a phone number. Two-thirds of people who chimed in for the Aimia study said they want to understand why companies asked for the information.

As you directly ask for data from customers, it’s smart to disclose how you’ll use what’s collected, including the security measures taken to keep it safe. Plus, tell customers how their openness to giving details will benefit them. Personalized offers based on buying habits, free stuff on their birthdays and targeted perks based on location are a few ideas.

Now that you’ve read these tips, it’s easier to collect data that genuinely drives your business forward rather than makes your customers feel you’re invading their privacy. By being thoughtful about data-gathering principles, you’ll steer clear of collecting too much while still getting enough to make the investment worthwhile.

Contributed by: Kayla Matthews, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog.

 

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