Data Acquisition – Why Is It Important?

September 16, 2021 Rafał Siwek

In many instances, data can become the fuel that will sky-rocket your operations. However, to make this scenario possible, you’ll need the key ingredient – good data. To help you with that, we’ll explain the details behind the various ways to acquire data.    

 

And if you’re not familiar with the data pipeline (or ETL workflow), make sure to check out this article before reading forth: What is Data Engineering? | PGS Software

 

 

 

What is Data Acquisition?

 

Data Acquisition translates into the collection of data and ingesting it into a system for further use (which will initially be processing).

O’Reilly divides Data Acquisition into two steps – data harvest and data ingestion. Understanding these steps will enable you to get a clear picture of where data comes from and how it can become a fundament of your data-driven operations.

 

 

Data Harvest

 

Citing O’Reilly, data harvest refers to the process by which a source generates data. Practically speaking, it considers what data is acquired.

Usually, data flows from two types of sources – it can be acquired by your own services (from websites, APIs, etc.) or it can come in in form of a stream and batch.

Stream data flows in continuously. This kind of data can come in from all types of sources, different formats and volumes; and with stream processing technology, data streams can be processed, stored, analyzed, and acted upon instantly, as it’s generated in real-time.

On the other hand, batch data is produced periodically, at a certain time interval, with a boundary.

Comparing these two, streaming indeed sounds more interesting, and you’ve guessed it – way more expensive. That’s why if you don’t need instant outputs out of your data pipeline, streaming isn’t necessary, and batch processing will suffice.

 

Read more about the most popular tools and techniques for acquiring data: Top Data Engineering Tools and Technologies | PGS Software

 

 

Data Ingestion

 

As the name suggests, data ingestion focuses on bringing the produced data into a given system.

O’Reilly divides data ingestion into three operations – discover, connect, and sync. The goal of these three stages is to identify accessible data sources in an enterprise’s environment, connecting these data sources so they can be directly accessed, and finally, copying the data into a controllable system.

So, at the end of the data harvest and data ingestion stages your organization should possess valuable data that can be used to the benefit of the enterprise.

 

 

 

Data Acquisition Methods – How to Get Valuable Data?

 

But having said what data acquisition is, it’s also crucial to explain how the process works.

You can get data from many different sources. After all, nearly everything can be a data source. From websites and apps to IoT protocols or even physical notes, the list is somewhat never-ending – and new data sources pop up literally every day.

The US government-run USGS names four methods of acquiring data:

 

  1. Collecting new data.
  2. Converting and/or transforming legacy data.
  3. Sharing or exchanging data.
  4. Purchasing data.

 

These methods also include automated collection, recording empirical observations manually, and acquiring existing data from other sources.

 

 

Data Acquisition – Things to Consider

 

Before you decide to put one of these four data acquisition methods into action, USGS suggests considering certain business goals and data characteristics.

For example, first, you should think about the business goal (why is this data required and what will it bring?). Next, you should also consider the costs, time restrictions and format. And if you’re operating in a specific, heavily regulated industry like banking, or are a government-controlled entity, additional restrictions may also apply – for instance, data standard thresholds or business rule limitations.

What’s more, every data acquisition method comes with additional challenges and characteristics you will have to consider. For example, when it comes to transforming legacy data, you should first assess the legacy quality. And if you’re purchasing data, it would be wise to analyze all the licensing issues.

The following diagram sums up all the relevant challenges:

 

 

 

Source: usgs.gov (Public domain.)

 

 

 

Data Acquisition – Summary

 

In today’s business environment, data-driven decision making (DDDM) has become one of the key enablers of taking optimal business decisions. However, as the name suggests, this can’t be possible without the right data.

Luckily, valuable information is everywhere. Yet, the challenge is to find the right one and put it to use. I hope this article has helped you to get a general idea of where to start.

Good luck!

 

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