Seven out of 10 CIOs and other top executives say data analytics is a "crucial" or "very important" business driver, according to KPMG.
While there's no debate about the value of data analytics in all aspects of the business organization, there's often confusion about what data to mine and under which circumstances. Two key types of data analytics, big data and small data—the latter referring to data use that relies on targeted data acquisition and data mining—have benefits and pitfalls. However, overall, small data actually might have a slight edge over big data.
Here are five ways small data may prove more valuable than its larger counterpart:
1. Small data is targeted and personal; big data is vast and impersonal.
While big data measures overall sales organization results, small data can hone in on what drives the behavior that leads to increased performance and improved results. All it takes is identifying key performance indicators (KPIs) that you'd like to use to track individual stats. That's when the magic begins, and you can unearth a whole slew of sales insights that can be broken down to the individual rep level. This data can be used by sales managers to eliminate educated guesses, uncover what's really working, and, overall, leverage a "playbook" to influence sales team results.
2. Small data is actionable; big data is interesting.
While big data can be interesting, it's hard to wrap your head around all the information it provides. Companies seem to be providing reports on everything these days, but if you're not sure what to do with all that data, it doesn't offer much real value to drive change and positively impact your bottom line results. On the other hand, small data can immediately be gleaned to make swift organizational or team changes.
3. Small data is customized; big data is general.
The sheer volume of data that is collected and available today can be overwhelming and can create more confusion and questions than answers and awareness to your specific business needs.
Small data determines a focused outcome (typically based on customized information related to one's job role) at the outset, whereas big data's goal is more about capturing as much information across the organization as possible.
4. Small data is real time; big data is historical.
Small data provides actual insight and real-time information to uncover stats and trends. With small data, you can immediately take action and adjust strategies in real time. This maximizes the opportunity to leverage the data for positive results.
On the other hand, big data is based on historical metrics that give you a good snapshot of past performance. But it's small data that will provide you with a more accurate and sound decision-making process based on current performance and trends.
5. Small data is pushed to you; big data must be pulled.
Pushing and pulling are the two different methods of moving data from its source and reporting on it. Big data lets you pull all the data you have, whereas small data allows an organization to pose specific questions or identify target information/outcomes.
While big data provides a wealth of information and reports that organizations can pull from, small data allows for targeted and strategic information and reports to be pushed and delivered to you automatically. To keep this simple, reports can be pushed to the key decision maker's email, letting him or her easily view the reports online. And, as crazy as most of our days can get, when push comes to shove, we all know we'll be more likely to utilize reports that are delivered to us than those we have to search out.
While both big and small data have their specific uses and benefits, it's essential to remember that it's often small data that is most beneficial for businesses looking to gather and act on metrics quickly. By educating themselves on the different kinds of data available, businesses position themselves for greater future success.
Sean Gordon is the CEO of Intelliverse.