11 Tested Big Data Best Practices in 2024 to Apply

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Written By Haisam Abdel Malak
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Big data best practices, specifically tailored for efficient management, play a pivotal role in transforming raw data into actionable insights. Organizations are managing big data to make better decisions, create new products and services, and cut down costs. As businesses continue to accumulate vast volumes of data from diverse sources, the need for comprehensive guidelines becomes increasingly imperative.


Adopting big data best practices and staying ahead of the latest trends is a must for organizations aiming to thrive in today’s data-centric landscape. With the exponential growth of data volumes, diverse data sources, and the evolution of technologies, such practices offer a strategic advantage.

big data best practices

The best practices for big data are:

#1- Define Clear Objectives

Defining clear objectives is the foundational step in any successful big data initiative. Organizations must articulate specific and measurable business goals that their big data efforts aim to address.

Whether the objectives are centered around improving operational efficiency, overcoming related challenges, enhancing customer experiences, or gaining a competitive advantage, a well-defined roadmap ensures that the strategy aligns seamlessly with broader organizational goals.

This clarity not only guides the selection of appropriate technologies and data sources but also provides a benchmark for measuring the success of the initiative.

#2- Know what data is important and what it is not

The first step in using big data is knowing what data is important and what it is not. This will allow you to choose the best data for your business. You need to be specific about the type of information you want, what answers you are looking for, and how much time you have available.

#3- Data Quality Assurance

Ensuring high quality data is an important issue in the digital era and considered an important aspect of big data characteristics. The data we rely on can have a huge impact on our lives and businesses, so it is essential to ensure that it is of high quality. From healthcare to social media, data quality needs to be monitored and checked for accuracy.

  • Data is accurate, complete, and up-to-date.
  • Data is timely, in that it was created no more than 60 days earlier or later than the last modified date.
  • Data is complete, meaning that it includes all records for a particular variable.
  • Related data is linked to the appropriate variable in the database.

#4- Commit to Proper Data Labeling

With the increasing popularity of big data labeling, it is imperative to understand the importance of different types of datasets. A good dataset is one that has been properly labeled and can be used for a variety of purposes.

  • Label your big data appropriately so it can be easily understood and sorted later on.
  • Consider how you will label your data before you start collecting it (i.e., what are the categories?)
  • Use consistent labels that are understandable by everyone.
  • Keep labels as brief as possible.
  • Use a legend or table of contents to show what each label means.

#5- Choose Proper Storage Locations

Choosing proper storage locations is one of the top big data best practices with far-reaching implications for the efficiency and effectiveness of data management. The vast volume and variety of data generated by organizations require thoughtful consideration of storage solutions.

Data storage is an important responsibility of the modern business world. With so many data breaches occurring in recent years, it’s more important than ever to know where to store your data. Cloud storage is a very popular option for companies, since it’s accessible from multiple devices and can be scaled up or down as needed.

  • Keep your data in close proximity to where it is used and referenced.
  • Place your metadata (information about your data) with the physical file that contains the actual raw data.
  • Store your data in multiple physical locations for redundancy.
  • Store full backups of your data and metadata on a separate system than the machine that contains the raw data.
  • Understand the components of big data effectively.

#6- Data Lifecycle Management

Data Lifecycle Management is a fundamental aspect of big data best practices, emphasizing the need for a comprehensive strategy that spans the entire lifespan of data within an organization. This approach involves orchestrating the collection, storage, processing, analysis, and, when necessary, the archival or deletion of data.

By carefully managing the data lifecycle, organizations can optimize resource allocation, maintain data quality, and comply with regulatory requirements. This practice ensures that data remains relevant, accurate, and accessible throughout its journey, allowing businesses to derive ongoing value from their information assets.

#7- Simplify Backup Procedures

One of the most important tasks when dealing with big data is to have a backup. This is because it can be lost or corrupted, and in this age of increased cyber-security threats, your data needs to be backed up safely. The easiest way to do this is to use a cloud storage provider.

Amazon, Dropbox, Google Drive, and Microsoft OneDrive are all popular options. Alternatively, you can back up your data on an external hard drive or CD/DVD.

– Take frequent backups of your entire system

– Use free software to create backups of your files

– Create automated backup scripts for your data and metadata

– Store backups offsite

– Use remote storage services like DropBox to store additional backups of your files

– Perform regular data backups and integrity checks on the backup

– Avoid using magnetic media for storing your data, as it can degrade over time

#8- Implement Security Measures

Data security has become a major concern for many businesses and individuals. Big data breaches can lead to identity theft, loss of trade secrets, and much more. If a company has sensitive information, it should be encrypted and stored on an isolated drive.

Encryption ensures that only the person with the decryption key has access to the big data. The main difference between cloud-based security and physical security is that with the first, data is not stored on one’s own device, but on a cloud-based server. This means less storage capacity for one’s work and more dependence on the service provider.

#9- Scalable Infrastructure

A scalable infrastructure ensures that an organization’s technological foundation can effortlessly adapt to the ever-growing demands of big data. By leveraging scalable solutions, such as cloud-based platforms, organizations can flexibly expand their computational and storage capabilities as data requirements evolve.

Scalability empowers businesses to efficiently accommodate spikes in data influx, whether due to seasonal variations or unforeseen surges, without compromising on performance or incurring unnecessary infrastructure costs.

#10- Use your tools effectively

The benefits of big data in the digital age cannot be overlooked. It is embedded into the architecture of many things. Analyzing data can help you get ahead of the competition and make your life easier. You just need to know what to do with it, and how to interpret it.

Using BI tools can make you analyze the datasets you have to get more insights about customers behavior, product performance, and other related important subjects.

#11- Regular Data Audits

Conducting systematic reviews of data processes and systems allows organizations to identify and rectify potential discrepancies, inconsistencies, or vulnerabilities that might compromise data integrity.

By implementing a routine audit schedule, businesses can maintain the quality of their data assets, reducing the risk of errors that could adversely impact decision-making processes. This practice also aids in compliance efforts by ensuring that practices align with relevant regulations and industry standards.

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