Big data is essentially a large data set that, when analyzed, reveals new patterns or trends that helps with determining human behaviour, preference, and interaction. Public organizations use big data to gain insights about people’s needs and wants to better set up community improvement plans. Many companies rely on big data to help them understand consumer behaviour and predict future market trends. Big data can benefit individuals, too, as information about people’s responses to products and services can in turn be used by consumers to make decisions about what to purchase and what to avoid.

Protecting Corporate Data

As the world’s digital landscape continues to expand and evolve, our ability to efficiently and effectively secure big data is becoming ever more important. In the corporate world in particular, these datasets are essential to assuring that targets are being met and that companies are moving forward in the right direction. Without this information, it would be much harder for organizations to market to the appropriate audience. As big data becomes exponentially more relevant, losing this data equals losing potentially valuable information, which could lead to significant monetary loss and wasted time and resources.

In order for growth to continue, businesses must ensure that their databases are backed-up and are able to be restored in the event of a disaster. With such massive amounts of important information at stake, preventing data loss by having a recovery strategy can be extremely helpful. To create an effective restoration plan, organizations should first determine the processes for data loss avoidance, high-speed recovery solutions, and constant visibility.

Data Loss Prevention

To minimize the chances of losing important information, a key component of securing data is implementing and communicating procedures to prevent data loss situations in the first place. One solution is to simply limit the access of users who do not need big data for their tasks. Minimizing access and keeping track of employees who have access will reduce the chances of individuals potentially erasing, accidentally wiping out, or misusing the data.

Limiting Downtime

Coming up with a high-speed solution that limits downtime is also crucial to ensuring a recovery strategy’s success. For example, establishing a recovery point objective (RPO) time would be quite beneficial. RPO time is the maximum amount of time that a dataset is allowed to be unplugged after an outage or disaster before loss starts to occur. Knowing this will allow authorized employees to work as quickly as possible in the event of an outage to restore data. RPO times can vary depending on the size of the data sets. Keeping in mind whether a business deals with large or small data sets can be helpful in determining an RPO time. Regardless of size, the ultimate goal is to try and reduce the downtime of your data and find the most efficient way to restore your specified data set.

Scheduled Testing and Restoration

Another way to ease the minds of consumers and employees in terms of security is deciding on a testing/restoration frequency. Setting a semi-annual and/or bimonthly testing/restoring schedule can help ensure data sets are accurately updated and adequately protected. More importantly, testing frequency schedules should help indicate whether or not the decided RPO time is accurate. These tests would ultimately conclude that if a disaster or accident were to ever happen, a company’s big data workload would have a chance of surviving.

Know Your Data

As there is a variety of big data environments, recovery strategies can be complex and wide-ranging. Many organizations operate and analyze unique data sets in different ways. Knowing what kind of data set you’re working with and its projected size will aid in producing an effective recovery strategy. It is also important to note that a strong recovery strategy necessitates the data to have constant visibility. Knowing where each dataset is being stored, how to access it, and keeping snapshots of it can provide an easy and efficient solution in case of an outage or disaster. For example, a live replication of a UPS means having a secondary outlet in case of an accident, ensuring that the data is always saved on a second source.

When constructing any replications, it is crucial to have a certified professional present or have trained individuals involved in the process. Although having a live replication does prove to be an attractive solution, as it will ensure a smooth data transfer between devices, an incorrectly built live replication runs the risk of losing data. Therefore, persons involved in the live replication construction should be aware and knowledgeable about the topic and process.

Keep Your Data Safe

Our world is becoming more technologically savvy, and organizations will continue to heavily rely on big data to predict future trends, analyze current patterns, and determine new consumer characteristics. Ensuring that datasets are consistently restored and backed-up means organizations can continue to flourish and improve their services. There are many solutions available to guarantee an efficient and effective recovery strategy. Every organization is unique when it comes to data and how they decide to analyze their own datasets, but one thing that all organizations should have in common is cementing a plan on how to construct, implement, and improve their data recovery strategy.