When people start talking about data and analytics tools, does it sound like the adults from Peanuts are talking? We understand. It’s normal to hear that droning sound every time data extraction and the associated methods come up. Thankfully, we realized it doesn’t have to feel so foreign.
Let’s take a look at this topic from a more understandable point of view.
What is Data Extraction?
You probably have an impression already of what we might mean when we talk about “data extraction.” And you’d be right. Mostly.
Data extraction is the business of analyzing various data sources to pull out pertinent and relative information. It’s then moved to be housed in a new setting for processing and consolidating.
The data is typically not structured and may not even be well organized or contextual when extracted. Therefore, after pulling it, the information needs to be refined and analyzed to make it useful.
The data can come from various sources; a database, data warehouse, SaaS platform, or web pages. The goal of data extraction is to collect a variety of data into one central location, effectively warehousing it. For example, you don’t leave essential papers just floating around your home, do you? No! You organize them and store them in a safe spot, of course. So why wouldn’t you do the same with data, which is just as important?
So, yep. Data extraction requires methods, techniques, and, above all, tools. These are usually products with funny-sounding names that you can purchase and will effectively work on your extraction.
Data extraction tools are wildly helpful. For example, they can help automate your company’s workflow, effectively increasing employee productivity. According to McKinsey Digital, CEOs waste 20% of their time doing work that could be automated. Shouldn’t they be doing more important things, like leading the company?
Another reason to use extraction tools is that they can ensure more accurate data. When you’re cutting down manual interaction with your data, you’re also eliminating the opportunity for human error.
Data Extraction and ETL
Data extraction is generally part of a more extensive process called ETL (Extraction, Transforming, Loading). Before your eyes glaze over and you start hearing a Peanuts’ teacher droning in your head, we’ll explain.
This comprehensive process makes it possible for businesses to compile data from many sources and load them into one centralized location, and format different types of data into a uniform approach.
There, that wasn’t so painful, was it?
It is possible to perform data extraction without ETL, but that’s not a suitable method for analytics purposes. When you take that approach, you only get raw data. And it’s generous to say that raw data is challenging to analyze and organize. And when you consider that 43% of accessible data is left untouched, you might agree that that’s a huge waste of potential.
Furthermore, extracting data alone can also cause formatting difficulties. Different data sources store their information differently, so most of it won’t look the same. Pulling from sources that express their information in different ways makes it tricky to have your data all work together. Geez, that’s like a real life teacher drone when you have data that can’t communicate!
The Pros of Using a Data Extraction Tool
There are many reasons an organization may need to extract data, and it only makes sense to seek it out from a variety of sources. For analytics purposes, it’s good to have a variety of viewpoints.
The right ETL tool can make data extraction a relatively simple process because it does all the heavy lifting for you while you kick back with a cup of coffee. Or take a nap at your desk like Peppermint Patty. Whichever.
These tools automate the whole process, so you don’t need to get bogged down in the minute details. One of the most rewarding benefits of using extraction tools is that they give you command over your own data, so your information doesn’t become siloed and difficult to locate. Instead, it’s all in one spot, so you can see it all clearly and don’t risk missing consequential information.
You also benefit from more precise data. No human involved, so no error. Automation keeps opportunities for foul-ups minimal. And as a bonus, your employees don’t need to take precious time away from their more pressing jobs to fix data errors.
If you share data with partners, extraction tools can make that process possible while keeping your more sensitive data secure. You don’t need to share the whole kit and caboodle with them, only the pertinent information.
Types of Data Extraction Tools
Not all data extraction tools are the same. Some approach them from completely different angles, while others have different origins. Each one has different benefits. So which one would suit your needs best?
Batch Processing Tools vs. Streaming Tools
Some tools give you a constant flow of data, coming in as soon as it’s collected. It’s reliable and fresh. These are streaming tools, and they provide you with real-time data.
Batch processing tools don’t give you that steady drip of consistent metrics. Instead. They grab your data in large chunks. Batch processing requires a lot of computing; it’s automated to work on its own time, usually after the close of the business day. This avoids interference with work.
Open-Source Tools vs. Cloud-based Tools
Cloud-based tools are the newest entry in the world of data extraction tools and are perfect for super-fast extraction needs. You won’t usually find cloud-based tools as stand-alone but as part of an entire cloud ETL process. The beauty of the cloud is that it gives you scalable storage and analytics, which is only appropriate, seeing how corporate data grows by 42% annually. So, yeah. Grab that scalability.
If you don’t have the dough for an entire system or don’t want to invest in one, an open-source tool is a perfectly fine avenue. It’s free sourcing software, but it requires know-how. Make sure your IT team is up to the task.
There are a lot of moving parts in analytics, and it can be overwhelming to keep up with each aspect. If you’re feeling like you’re heading down the rabbit hole when you start looking at data extraction tools, remember that it doesn’t need to be complicated. What are tools, after all, other than something designed to make your life easier? Hopefully, you now understand them a bit better. And maybe a discussion on data extraction tools won’t sound so much like nonsensical droning.