“Slice and dice.”
They’re buzzwords that get tossed around quite a lot when it comes to big data.
But what do they actually mean? And how do they help YOU do your job better?
Let me take a moment today to unpack the term, by using the metaphor of eating an apple.
Sure, you could bite into a whole apple and chew chunks of it at a time. But what if you took the time to slice the apple, then diced those slices into smaller and smaller pieces? You'd get a finer level of detail, and a greater number of options for turning those dice into something visually and texturally appealing to a larger number of people. Do the same process with different kinds of apples, combine the flavors to maximum effect, and a whole spectrum of options opens up.
Slicing and dicing data is like that: it provides a closer, more granular view of the data and presents it from new and diverse perspectives. You're removing the extraneous bits and reducing the set of data to the choicest, most essential components that yield the best results. Do the same process with different data sets, combine the insights to maximum effect, and a whole spectrum of actionable information opens up.
Now let me apply "slicing and dicing" to a real-life scenario.
Let’s say you’re planning to introduce a new wine, perhaps a rosé, whose momentum right now seems to be very strong. But you need to base strategic business decisions on more than "rosé's momentum seems strong." That's where slicing and dicing data comes in, particularly when the data comes from different sources. It empowers you to NOT take a shot in the dark. It provides granular and empirical evidence for business decisions. And it confirms (or in some cases disproves) a hunch.
Say you're interested in rosé sales in restaurants. We can slice and dice a partner's on premise data in order to identify markets where rosé wines with a similar profile to your rosé are already popular.
Or let's say you're interested in targeting DTC consumers through flash sales. We can slice and dice another partner's multi-year data in order to determine rosé sales trends over time in particular zip codes.
Let’s also say you want to daypart your outreach about rosé to savvy consumers on their mobile devices. We can help you slice and dice a partner's geolocation data in order to identify the specific markets where clusters of consumers searched for information about rosé at specific times of the day.
And so on from there.
Does that make sense?
I hope so. But if it doesn't, or even if you'd like to talk about other capabilities and possibilities, please let me know (firstname.lastname@example.org). I'd love to hear your thoughts.