How to Properly Extract Data
As it happens with many modern practices, establishing a successful data extraction method isn’t a one-time deal.
For quite some time now, tech experts and leaders have been pointing at big data as one of the defining trends in today’s business landscape. As we entered the golden age of data, that fact became more evident, so companies across virtually every industry started adopting big data practices. And then, the COVID-19 pandemic convinced the few remaining non-believers about the necessity of gathering and analyzing data to get a competitive advantage in a highly fluctuating scenario.
Now, you’d be hard-pressed to find someone that can argue big data’s capability of providing insights that allow you to make better decisions, identify new business opportunities, improve performance, and increase overall revenue. Of course, all that host of advantages isn’t easy to get — you need advanced AI-driven tools to collect and analyze the vast troves of data available.
To put it simply, big data is a challenging practice because of two main reasons. First, analyzing massive amounts of data can become a daunting task, especially if you don’t have the right tools by your side. And second, extracting the data from different sources isn’t as easy as it may seem.
While a lot of businesses worry about the analytical part of big data, today we’ll take a look at the often overlooked part of pulling data from different sources and prepare it for analysis. Why? Because the analysis-related tasks are greatly simplified by the use of one of the many analytic tools available, be them off-the-shelf software or the custom alternatives developed by nearshore developers.
Extracting data, on the other hand, is frequently treated as an easy thing to do. Sure, it might not be rocket science, but without a proper methodology, you can quickly get lost in a data labyrinth. That’s why I’m bringing you an easy way to properly extract data from all your available sources, an efficient method called ETL.
Extract, Transform, Load
Anyone that has ever worked with data may have already heard about the ETL process. That’s because ETL has been a popular notion since the 70s and has remained relevant until today, especially in data warehousing. Its name stands for “Extract, Transform, Load”, which are the 3 necessary steps to prepare data for analysis. Let’s see what each of them mean:
- Extract: the process through which the data goes from one platform to another. It’s the initial movement of data from your sources to your pipeline.
- Transform: the data extracted in the first step is raw, which means that you need to work on it to prepare it for analysis. You do that by transforming the data.
- Load: After the data is transformed, you have to store it in a place where it’s easily accessible for the analytic tools to retrieve it. Thus, you load it into a data warehouse or similar platform.
As you can see, the ETL method is a fairly straightforward process to take data from your available sources all the way to your data warehouse, where it’ll be ready for analysis. Now that you understand the basics of the method, it’s time to talk about implementing it.
Establishing an ETL Pipeline
How can you take the ETL concepts and turn them into an actual process integrated in your current workflow? You need to go through 3 different steps.
1. Set up an Automation System
Gathering data always starts with a specific action that tells your internal systems that they need to collect certain data. Those actions might include making a sale, getting a click on a button, receiving an email, and so on. Naturally, you can’t manually monitor all your relevant actions and register everything that happens. For that, you need an automation system that’s capable of identifying those actions and recording the relevant data.
For that, you can use a series of predefined triggers that “tell” the system that it needs to gather data. How you tabulate that data into your system will largely depend on what you’re measuring, which means that you’ll have to define actions, triggers, and format of the data you’ll collect.
2. Integrate a Storage Platform
The data you gather has to go somewhere, which means you’ll have to integrate a storage platform into your pipeline. The most frequently used option by everyone from big enterprises to nearshore software development companies and startups is data warehouses. These are often seen as core components of data pipelines, as they become central repositories where data from multiple sources is integrated.
While data warehouses are the logical choice, that doesn’t mean they are your only alternative. You can always use databases to store data coming from a single source, data lakes to store raw data that needs further processing, and data marts that are like small-scale data warehouses. The best option will depend on your particular needs but, if in doubt, it’s best if you consult with your IT team.
3. Keep your Data in Shape
Gathering data, formatting it, and storing it isn’t enough. All data has a limited lifespan, which means that it can get outdated quickly. If you use outdated data for your analysis, you’ll arrive at the wrong conclusions, and all of your big data efforts will be for nothing. That’s why you need to keep your data consistent.
What does that mean? That you have to monitor the data you have stored and check for potential holes and inconsistencies. Auditing the data, patching up the gaps, and repairing the information you have is essential to keep data relevant and useful. You can do that by regularly analyzing the data gathering process and the format you use to store the information to improve your entire data pipeline.
Data Extraction as an Ongoing Effort
As it happens with many modern practices, establishing a successful data extraction method isn’t a one-time deal. You need to constantly revise and adjust your extraction techniques to ensure that the data you gather can provide the value you’re after. Fortunately, there are plenty of ETL tools in the market to help you keep a close eye on your data extraction. Plus, you can always count on nearshore development services to lend you a hand to customize your ETL process.
Is there a different way to extract data? Sure — you can do all the extraction-related tasks manually, with everything that it entails: a lot of people working for hours on end for little to no returns. With such an alternative, it’s evident that the ETL method (and its tools) is the way to properly extract data and truly get a competitive advantage.
