Big data is a big deal. Spotting trends in data enables business leaders and entrepreneurs to make better decisions, improve team performance and increase revenue.
Sales, customer and operations data can make a night-and-day difference for your business. But with the bevy of analytics tools available, the challenge isn’t analyzing but extracting data.
The most efficient method for extracting data is a process called ETL. Short for “extract, transform, load,” ETL tools pull data from the various platforms you use and prepare it for analysis.
The only alternative to ETL is manual data entry — which can take literal months, even with an enterprise amount of manpower. Save yourself the trouble by getting a grip on the ETL process.
How ETL Tools Work
Knowing how an ETL tool works is part and parcel of understanding its value. Let’s take each term of that acronym in turn:
The first step is getting data out of one program and into another. It’s the part of the process most leaders already know they need, but it’s only useful when paired with the other two.
The data extracted by your ETL tool is raw. Most programs don’t “talk” to each other well, so their data must be converted to a common language before you can work with it.
Finally, your processed data is loaded into your storage vessel of choice. Many people use a data warehouse: a program built specifically for the storing and viewing of processed data. Once there, it’s finally ready to be analyzed.
What You Need to Do
The ETL process seems simple enough, right? Now that you understand it, let’s get your data pipeline flowing:
1. Set Up Triggers
To get your extraction started, you’ll need to set up an automation system. This involves designating triggers for your ETL process.
One trigger might be a sale made on your company website. When a customer checks out, it initiates an action: the purchase data is delivered to your ETL program. Information like the amount spent and the time of purchase are then compiled, converted and piped into your data storage system.
2. Choose a Storage Platform
As mentioned previously, extracted data needs to be stored somewhere. There are a few options to choose from, each with their own strengths and weaknesses:
- Data warehouses are used for storing data from one or multiple sources that has been processed for a specific function.
- Data lakes store raw data that has yet to be manipulated to fit an exact purpose.
- Data marts are similar to warehouses but on a much smaller scale, typically devoted to a single team or department.
- Databases are used to store information from a single source, often to support a system that uses a lot of data.
Data warehouses are the default choice for most big data initiatives, but the other three options have their merits. When in doubt, ask your IT team to weigh in.
3. Audit Your Data
Don’t blindly trust every byte sitting in your data warehouse. Address change. People switch companies. The point is, data gets outdated faster than you might think.
Regularly audit your data, ensuring everything is in place and in the correct context. Without a data audit, you may be getting an incomplete picture. When you work with limited information, you can’t make informed decisions.
Reviewing your data can identify holes in your ETL process or business strategy. You might realize, for instance, that you need to set a trigger on LinkedIn in order to compare candidates who apply there to those who fill out a form on your site.
What if you spot data that isn’t factual, consistent, or within the parameters you meant to set? Sometimes, you can repair it automatically using a data enrichment tool. Other gaps might require some research and manual revision on your part.
The data your business generates is at least as valuable as any physical asset it owns. You wouldn’t let your office or company vehicles fall into disrepair, would you? Don’t let your data collect dust, either.
Use an ETL tool to pull your data and give it a polish. Until then, you won’t be able to unlock all the insights analytics tools love to tout.
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