9 Tested Big Data Best Practices to Apply

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Written By Haisam Abdel Malak

About: Haissam is a digital software product manager with 15 years of expertise in developing enterprise content management solutions. His core capabilities encompass digital transformation, document management, records management, business process automation, and collaboration.

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Organizations have been using data to make decisions and improve processes for a long time. But now that we have such a wealth of information available, data has become the new currency. Organizations are using big data to make better decisions, create new products and services, and cut down costs. In this new reality, we need to be mindful of how we use and extract data, which means developing best practices for big data.

Big data management is a complex process that organizations must face while running a business. Big data challenges can impact the success of the implementation, and this is why it is necessary to discuss these nine big data best practices in order to facilitate the process.

Before we continue, I strongly recommend reading our previous article “big data benefits” to get an in-depth idea of how big data can impact your organization.

So, let’s get started

Big data

What are the key big data best practices?

The purpose of big data best practices is to ensure that the data is not just collected, but also analyzed and stored in a way that it can be retrieved when needed. There are many ways to store the data and it depends on what you intend to do with it.

The most critical big data best practices are:

1- 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.

2- Ensure high quality

Data quality 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 controlled 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

Ensuring high-quality data should be on top of the list of big data best practices

3- 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

4- Choose proper big data storage locations

Another big data best practices is understanding the different components of big data including the necessity of choosing a location where the data should be stored.

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

5- 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

6- Implement high data 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.

7- Plan for the future

Planning can be difficult. We often have to rely on instinct and past experience to make decisions and consider the consequences of potential outcomes. The same is true with data.

In order to make the most of the information, it’s important to understand how big data trends evolve over time and how they impact your business. This is the most crucial big data best practices.

8- Use your tools effectively, they will make your life easier

The importance of data in the digital age cannot be overlooked. It is embedded into the architecture of many things. Analyzing big 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.

9- Find ways to make your data more accessible

Poor data accessibility can cripple a company’s performance. One of the most important best practices for big data is to make sure that data are accessible to others who may need it in the quickest way possible.

Take These Best Practices and Improve Your Organization Today!

Big Data best practices are an important aspect of any organization. These practices help you handle your data in a way that is both useful for your business and safe from misuse or abuse.

They include setting clear policies, educating users on the importance of data privacy, and using encryption tools to protect your data from cyberattacks.

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