Data processing and biggest Big Data processing challenges

Author Paula Grubiša
Category Technology
Date Jun 04, 2020
4 min read

When big data gets acquired and enters an organization, there’s a high probability that it will not be clean or in the right format to be analyzed or presented to users – that is where data processing comes into play.

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Depending on your business strategy — gathering, processing and visualization of data can help your company extract value and financial benefits from it. Our new ebook will help you understand how each of these aspects work when implemented both on their own, as well as when they’re linked together. Download it for free!
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Data processing means converting raw data into a machine-readable form and its subsequent processing by a computer, manually or automatically. That process consists of exploring, transforming and modeling data with the goal of highlighting relevant information and extracting useful insights for a specific business.

The action of data processing consists of 4 different steps that turn raw data into usable information:

  1. Cleansing
  2. Standardization
  3. Transformation
  4. Aggregation

In order to clean, standardize, transform and aggregate the data from different sources, data processing systems need to touch every single datapoint that enters them. Once data has passed all of these four steps, only then it can be passed to the next phase (data visualization) and be presented to users in many forms such as image, charts, graph, tables, audio or any other desired format.

When it comes to the business side of big data processing, everything revolves around scalability and the time needed to process gathered data.

Processing software and systems are only as good as their outputs — valuable insights that are extracted from gathered data and presented to users. Data processing helps businesses in extracting the most relevant content for later use, thus bringing numerous benefits to the company.

How to achieve scalability in big data processing?

#1 Parallel processing

As the volume and complexity of data increases, so should the number of parallel processes — each of those processes continues to handle a similar amount of data as it did before, so the entire system maintains peak performance.

#2 More hardware

Having an optimized system is very important, but in order to handle the increase in parallel processes, that system needs to be powered by more servers with more processors, memory and disks.

Biggest big data processing challenges

With more and more different types of data being created by more and more sources every single day, certain issues may arise when it comes to big data processing. Here are a few challenges you need to be aware of if you’re looking to build your business strategy around big data processing.

#1 Duplicity

Data is usually collected from a number of data sources, and many times you may end up with duplicates. Identical entries not only put an additional strain on the data processing system, they can also affect the results and lead to incorrect insights.

#2 Inconsistency

When gathering very large amounts of data from various sources, you can’t expect that all of it will be complete or even usable, because raw data is extremely heterogeneous and each source can generate it in its own specific way.

#3 Variety

Data from different sources can come in different forms, and that means your data processing systems need to work extra hard to make sure everything is in order.

#4 Integration

Data integration focuses on combining data that was acquired from various sources and in various formats, and presenting them in a structured, unified way. As more and more sources create more and more different types of data, that can become quite difficult.

#5 Volume

Big data is enormous both in volume and in complexity, and storing such large amounts of data in a way that is easily accessible is not a simple task. And when you add all of the necessary backups you need to have, that becomes an even bigger challenge.

#6 Security

Security is an extremely important aspect within the field of big data because it can be quite costly and potentially destroy the reputation of your company. Some of the security breaches that may occur and that you should look to prevent at all costs are data leaks, deletion of stored data and malicious modification of stored data.

Embrace the valuable insights extracted from data

With proper processing of data, more valuable information can be sorted, and that can ultimately lead to better productivity and more profits for various business fields. You should explore the unlimited benefits data processing can bring to your business and realize the importance of effective data management.

Embrace all the valuable data you gathered – it can help you work smarter and make your everyday tasks more efficient and less time-consuming.

If you want to know more about big data gathering, processing and visualization, download our free ebook!

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