Big Data: What is it, exactly?

By Amy LaVange on Sep 24, 2018 11:21:57 AM

Big Data is like the Emperor's New Clothes. If you ask everyone who understands the difference between “data” and “big data” to raise their hand, I’d wager that at least 80% of those people couldn’t give you an accurate explanation when pressed to answer. So if you didn’t raise your hand (or you’re one of the 80%), don’t feel bad; even people who work in the marketing, medical, and tech worlds speak about them "data" and "big data" as the same thing.

That doesn’t mean all these people don't know what Big Data is, but it should certainly make you feel better. If experts speak about them as the same thing, how do the rest of us understand?

Before we go any further, let's just break down the vocabulary:

 

"Data" is any single piece of information or any individual dataset.

Data: Set(s) or individual data points that can be:

  • Qualitative or quantitative
  • Structured or unstructured
  • Machine readable or not machine readable
  • Digital or analogue
  • Personal or not personal

Let's use my wardrobe as an example. If my closet is my "database", each individual item or a single-layer group of items (one shirt, or all my shirts, or even all my red shirts) would be considered "data".

For your course, "data" could be each individual tee time. Your "datasets" could consist of all the tee times on one day, or even all the male players on your day's tee sheet.

Data can be evaluated with simple tools—or even your brain! In essence, if you can talk about the data in question, it's probably just regular old small to mid-sized data.

"Big Data" is so much more than single layers of data.

But understanding the complexity of the amount of data piling on top of itself is where people get a bit confused.

Big data is not just MORE data. It's not even a lot more data. It's so much data, coming in from so many channels, overlapping and interweaving, that you can't possibly correlate it in your mind. In fact, you can't use basic data tools like Excel or Crystal reports because there's no way to filter all the datasets.

Let's talk about my closet, again. If my closet is my "database", I already have a lot of BIG data going on. All the different items, in all the different colors, with all my shoes and accessories and even the impact each piece has on my mental health or physical appearance, would be individual "datasets". The BIG data would be every possible combination of every possible characteristic of each piece of "data".

  • Did people smile last time I wore that shirt? Did they comment on it?
  • When I add those pearls to that tank top, does it go from shabby to classy?
  • Which colors are best for this season, based on the feelings it elicited in other people?

Now, I don't collect all this data. I don't track everything people say, I don't ask for feedback on my outfits or even document for myself the way I felt, the way it impacted my internal temperature, or how appropriately put together my outfits may be given the way I've been wearing them. And even if I did, I'd never be able to filter my outfit options by all of these variables without some sort of advanced application that could run an algorithm to produce the perfect outfit based on every possible factor.

In other words, I'd need BIG DATA tools.

Clearly, looking at your business in terms of big data is more valuable than evaluating individual data points. It is what drives strategies that will make significant impacts in our businesses. It allows us to take all these random pieces of information or events, identify predictions, trends, correlations, and opportunities.

It is what will make me the best (or worst) dresser.

It's what gives you actual visibility into revenue streams, guest happiness levels and preferences, and areas to invest your marketing or staffing dollars. It's the difference between getting a couple comments from players as they pass you in the Pro Shop and surveying all your players with appropriate questions to gather collective feedback rather than potentially outlying opinions.

Sounds great, right? Unfortunately, there's a hitch.

The Problem with BIG Data

No longer are we worried about how to get more data. No, no. Now we are too busy trying to save ourselves from drowning in it. The volume, veracity, & variety of all the data coming into your course is… a lot. And if you can't pull out of the data sea, it's unlikely that you'll waste time trying to think about it.

What's the answer? How do we tap into this goldmine of information, and use it to strategically drive your business to higher heights?

To evaluate this, we're going to tackle a few questions:

  • Where is data coming from?
  • What things can I glean from what channels?
  • How do I use it to get a holistic view of my business?
  • How do I identify opportunities and support hypotheses?
  • What things can I start with today in order to take advantage of the data already in my database?

 Read our blog next week to read more about the answers to these questions and what Big Data can do for your course.

 

comments
0