If you're a senior manager launching a Big Data initiative, you should start by asking three simple, high-level questions to guide your organization's data collection strategy. Once you have an analytics strategy in place, it's time to think about how you're going to apply the data you're collecting in the marketplace. How will you use it in interactions with customers, competitors, and collaborators? It's especially important to anticipate how your use of data will be perceived by your customers. Here are some crucial questions to consider when it's time to start feeding data back into the marketplace:
How transparent will we be about the data we use to make decisions? Clearly, businesses using sensitive data should be up-front and open about what they're collecting and how it's used. But there's a second level of transparency that might be better thought of as obviousness: how blatantly do you want to link your external activities to your data?
Consider the case of Target. The retail giant tracks customer behavior so rigorously that it had accurately predicted when a teenaged customer was pregnant based on her buying patterns; this was before she had even told her father, who was outraged when Target started sending her baby-product promotions. (It turns out consumers aren't comfortable with the idea that a big box retailer knows them better than their own family does!) Target didn't stop collecting and leveraging customer behavior data in its marketing, however. Instead, it simply became more subtle in its execution: customer-specific offers are now interspersed with more generic ads or a more diverse set of promotions.
Sometimes data-savvy companies get so excited by their analytical horsepower that they don't stop to think of how their business intelligence is perceived by outside parties. This is particularly relevant to marketing, where customer experience is key. However, it's also relevant when you think about what signals your activities may send to competitors. If your Big Data capabilities are a sensitive competitive advantage, delicacy in communicating what, and how much, you know may be important. Subtlety sometimes has its place.
How accessible and usable do we want our data products to be? Today, data can be integrated directly into products in ways never before possible. Think about Nike+, which allows runners to track and share their performance, or Weight Watcher's eTools, which provide an online dashboard for dieters. But translating data and analytics into a product offering raises a number of questions. How much data do you want to share with your customers — just theirs, or data from others as well (e.g., comparing running times)? How specific do you want the data to be (e.g., direct comparisons, or just rankings)? How can you make potentially complicated data more digestible and intuitive (e.g., through scoring systems or data visualizations)? How broadly accessible is your data — do users have to register, pay, or meet certain criteria to engage with your analytics? At Quovo, we've learned quickly that when data becomes a key part of the customer experience, it's important to think like a product manager as well as a data strategist. The more consumers become comfortable with data, the truer this will become in the future.
Are there win-win partnerships that will help us to be smarter about our data? As I suggested earlier, good data is often viewed as a competitive advantage to protect fiercely; but as information becomes more ubiquitous, a different, more collaborative mindset can be beneficial.
Consider: As the world's largest online social network, Facebook already has a trove of data on its users. But through its Facebook Connect program, which allows users on Facebook to sign onto other sites using their Facebook credentials, the network can also track behaviors such as "Likes" on external sites. In return, partner sites can offer visitors an easier log-in to access and share content through Facebook's newsfeed, which should help increase traffic.
As the Big Data landscape evolves, similar opportunities to scale data through collaboration will become more common, including through third party vendors. Organizations like Metamarket and LexisNexis Risk Solutions (full disclaimer: I've worked for LNRS) manage contributory databases that pool data together from multiple companies. In return for contributing, each organization gets access to data and analysis that would have been beyond its reach had it limited itself only to its own data set. Not every company is going to be comfortable with this arrangement, and data privacy is an important consideration; but as data volume grows across industries, businesses will soon need to find ways to scale up their data insights quickly. Partnerships may be a good way to get smarter, faster.
- Big Data Doesn't Work if You Ignore the Small Things that Matter
- Why Data Will Never Replace Thinking
- The Apple Maps Debate and the Real Future of Mapping
- Get Started With Big Data: Tie Strategy to Performance