The first step to understanding data? Understanding data terms.
Of course you know data is uber important to your business. You’re likely already collecting it all the time. Maybe you have sensors. Or dedicated platforms. Maybe you outsource this work. But for many companies across most industries, this whole big data thing is still sort of hard to manage.
There’s a lot of data.
And a lot of perspectives on where to store it and what to do with it.
It can be difficult to enter the conversation and advocate for your business’s goals if you’re fuzzy on the terminology. This concept of data-driven decision-making is still fairly new, and the tools and best practices are constantly evolving, which is awesome but can also make it even tougher to keep up. Here’s a rundown of some useful data-related terms, delivered (as promised) in our no-nonsense, plain language style. Enjoy:
1. Data Analytics
This term is the big kahuna when it comes to your data. It’s the act of finding the story within the data. Analytics is the examination of data to find trends, correlations and other actionable insights. Businesses employ a wide range of analytics types, including diagnostic (pinpointing what happened and why), predictive (identifying trends and forecasting future behaviors), prescriptive (helping determine actions based on predicted impact) and more. There’s a lot of math involved. And sometimes artificial intelligence and machine learning.
2. Data cleansing
No one wants dirty data, right? Since you can’t just plop your data in the bathtub, data cleansing exists as the process of making sure the data you analyze is usable. This typically entails getting rid of duplicates and irrelevant data, updating incomplete data when possible, and making sure data is formatted correctly. It’s an important step in ensuring data quality and security.
3. Data lake
A data lake is a big ol’ repository for all of your raw data, in whatever format it comes in, just hanging out without a defined purpose. This data hasn’t yet been filtered according to any set parameters and may or may not be needed. We sort of wish they’d called it a data swamp. It’s all a bit messy.
4. Data mining
Think of data mining as “prepping” your data for all that cool analysis we talked about earlier. When you have really large data sets, mining can help find notable patterns that you’ll want to examine more closely. A variety of techniques, from artificial intelligence to statistics (you paid attention in math class, right?), can help identify patterns in data.
5. Data warehouse
You know how an actual warehouse is a massive storage center for all the stuff your business needs? And it usually has shelving and designated areas and a clear system in place for locating what you need? Yeppers, a data warehouse is essentially the same thing for your data. It’s a comprehensive framework for storing and aggregating your data, and its purpose is to make it way easier for you to find the information you need for business intelligence and self-serve reporting.
6. Dark data
It’s a funny term. We had to include it (sorry not sorry.) While you may think that dark data refers to all the info Voldemort’s been collecting in the wizarding world, it’s actually something much tamer. Dark data refers to all the data your organization collects and processes but ends up not using.
Quite simply, metadata is data about your data. OMG. That is so meta, right? When you share documents, images, links, etc., the metadata identifies all kinds of info – when and where the data was created, who created it, associated permissions, and much more. Metadata has a lot of uses but, for your business purposes, it’s often helpful with digital archives and search queries.
So, there you go, seven data terms (that even all have the word data in them!) hopefully made a little easier to understand. Of course, data management is a complex and ever-changing field, so there’s probably a whole slew of terms you still have questions about. Feel free to ask us anytime!