Data extraction is the process of pulling data from one source in order to make further use of it. It is a fundamental step in data processing pipelines, and as the world becomes more digital, business professionals will need to gain a deeper understanding of data techniques such as this one.
In this post, we’ll cover the basics of data extraction, how data extraction is used in businesses, why you should adopt data-driven business processes, and other data-driven methods you should learn.
To start off, let’s look at the core steps and tools involved in data extraction. Then we’ll see where extraction fits into the data pipeline.
Why is data extraction important?
Data extraction, as mentioned, is the process of pulling data from one source in order to use it in another source.
In the analytics world, it’s the process of retrieving data from structured or unstructured sources for further use in the data pipeline.
Once the sources are chosen, the data is then “ingested,” profiled, and processed further.
What are the core steps in data extraction?
Data mining is the process of examining cleaned data for insights.
Data extraction, however, focused simply on the collection of data.
Where do we find the data? The first step is to identify where data is stored.
Sources can include a wide range of sources – here are just a few of many possible data sources that can be used:
- The web
- IoT sensors
- In-house data stores
- Software analytics
- Marketing analytics
Extraction is one of the key steps in data-driven processes – and the effectiveness of data-driven methods do depend on extracting the right data – but it is only one of many steps involved in the data pipeline.
Why do businesses need to use data-driven processes?
Data-driven methods offer major benefits over business processes that aren’t data-driven.
With data, business professionals can:
- Rely on real-world facts, rather than opinions or gut instincts
- Make more accurate decisions based on those facts
- Leverage that data to enhance the effectiveness and efficiency of business processes
Not only does data drive performance and process improvement across the organization, the applications of data within a business context are virtually limitless.
For instance, data can be used in business functions such as:
- Improving customer experiences
- Optimizing business processes
- Enhancing the employee experience
- Predicting and responding to changes in the marketplace
- Analyzing competitor activities
One key step in improving the performance of data-driven methods is, as mentioned, ensuring that data extraction and other steps in the data pipeline are understood and executed effectively.
What does the data pipeline look like?
The data pipeline is series of steps that data professionals take in order to migrate data from its original source to its end use case or database.
Although the exact pipeline architecture may differ slightly from business to business, the general steps remain the same.
These are:
- Selecting the original data sources
- Joining data from various sources
- Extraction
- Standardization, or ensuring that various data types can be used together
- Correction of errors
- Loading data into the system for analysis
- Automation of the data pipeline
In a data-driven world, this data pipeline is quickly becoming the heart of modern business, which means that businesses should invest in data tools, data scientists, and data-driven business capabilities.
However, that does not mean you have to be a data scientist to extract data effectively.
Who needs to know how to extract data and why?
Today, there are many tools that are democratizing data and data-driven methods.
For instance, certain no-code platforms offer business insights that can accelerate digital transformation efforts – and they are usable by anyone, putting the benefits of data in reach of every employee in the organization.
Many argue that data should actually become a central pillar of organizational processes and the organization’s culture. A data-driven business culture would embed data driven thinking and methods into the very fabric of the business, which would be essential to enabling and driving data-driven strategies.
Since data can be applied at both the highest level of the organization and the frontline, it is easy to see why many suggest data should become such an integral part of the business.
In today’s digital era, therefore, virtually everyone working in an enterprise or a data-heavy business should consider learning data tools and methods.
Conclusion
The global economy industry is in the midst of a major transformation, where digital technology and data-driven business strategies are becoming the norm.
In this world, a major question businesses must ask and answer is whether the data they’re gathering provides the insights they need to make informed decisions.
It’s time to dig deeper into data-driven business processes – employees must become citizen data scientists and data professionals must become data masters. The more effectively businesses and their employees can use data, the more advantages they can gain from that data and the more competitive they will be in the digital-first next normal.
