Last week, I covered the first of the five “V’s” of big data, volume. This week, I’m looking at variety and how it can present a challenge to financial institutions.
“Variety is the spice of life” — or so goes the old adage. When it comes to online media, the phrase definitely holds true. Think of all the different types of data you consume online in your day-to-day life: photos, videos, articles and shareable content on the web; health data via your FitBit or other wearable device; not to mention the data from marketing companies, IT, and other businesses.
Looking at social media alone (the most common source of big data), in one month:
• 30 billion pieces of content are shared on Facebook
• 400 million Tweets are sent
• 4 billion hours of video are watched
Add the data consumed via email, Bluetooth, GPS, Near Field Communications, online purchases, offline purchases, etc., and you see that it’s not just the sheer volume that is a challenge to financial institutions and other businesses, but the variety, as well. In fact, volume is the least of the challenges.
Historically, most data, including financial information, has been structured, fitting neatly into spreadsheets and relational databases, organized by product, region or salesperson. Today, more and more of the world’s data — 80 percent, in fact — is unstructured. Big data allows the combining of these different types, but the variation in formats and sources makes storage and analysis difficult.
Fortunately, the challenge sounds worse than it is (how could you possibly store a video in a way that also takes into account text and photo?). Thanks to the advent of new technologies, such as “data-as-a-service” cloud solutions and packaged solutions like the “smart data lake,” the bridge between gathering data and using it to make business decisions is smaller than ever.
For small to mid-size financial institutions, these new technologies are especially compelling. Data-as-a-service provides the flexibility to select the tools a bank or credit union needs to solve specific business problems while still providing a single source of all enterprise data with documented quality and provenance. The smart data lake also provides a single source for all enterprise data, but allows the ability to collect vast amounts in its native, untransformed format — at a low cost.
Adoption of these two technologies helps solve the “variety” challenges of big data. I’ll be back next week with a post about the third “V” — velocity.
In the meantime, learn more about big data and its challenges and benefits for financial institutions in the Analytics section of our website.