Data is a collection of facts and statistics used for reference or analysis. Businesses are collecting, measuring, reporting, and analyzing data to further understand clients, products, skills, and customers.

Big data is the analysis of large sets of data that reveal certain patterns, trends, and associations related to human behaviour and business interactions. In today’s world, information is continuously being collected, which is why big data analytics has come to the forefront of the IT industry.

Glassdoor, one of Canada’s fastest growing job recruiting sites, released a 2017 report distinguishing the 50 hottest jobs. It identified the overall job score for Data Analysts as 4.8 out of 5, with a job satisfaction score of 4.4 out of 5 and an average base salary of $110,000.

Why is big data so popular in the job market? Because dig data will always be needed in the technological world we live in.

The Evolving World of Big Data

According to Bernard Marr in his article “17 Predictions about the Future of Big Data Everyone Should Read,” big data has taken over the business world. Data is continually growing and developing, and it will never stop. New information will always come into existence. Here are some of the main predictions formulated by industry experts that Marr believes every big data analyst should keep in mind.

1. New devices with AI technology are expected to make more appearances in the future, including robots, self-driving vehicles, virtual assistants, and smart advisers.
2. The job market for big data is expected to grow even more, and there will not be enough people to fill all the positions that companies need to fill. Some companies will have to look inward and train existing personnel on big data analytics to fill those spaces.
3. Another reaction to the shortage of data analysts will be an increase in the use of cognitive computing, which allows computerized models to mimic how humans think and react to information.
4. New and more intuitive analyst tools will appear on the market, and some of those tools will allow non-analysts to access data. Furthermore, experts believe that programs that can allow users to use data to make decisions in real time will ultimately come out on top.
5. Simultaneously, more companies may look to buy algorithms rather than attempt to program them. Expect algorithm markets to grow.
6. Privacy is and will continue to be a huge issue facing big data. The public should expect to see more ethics violations related to data.
7. More companies will try to use big data to drive up revenue.
8. More companies will sell not just data but also ready-for-use insights.
9. Big data, due to its sheer volume, can become too cumbersome. Many businesses have no utility for all of the data that they collect. One day, “fast data” and “actionable data” with specific utilities may replace big data.

The Future of Big Data

Imagine if there was a computer that had the ability to tweak the social and economic constructs of our society; a computer like that could ultimately evolve and mould society to its liking.

The theory about this omnipotent computer is called the Universal Graph.

In mathematics, a universal graph is an infinite graph that contains every finite graph as a subgraph. In simpler terms, it is a graph or network in which a single piece of information can be connected with other bits of information until all finite information pieces are integrated into one single graph. You can think of the Universal Graph like a computer that contains all the information in the world — a “supercomputer” of sorts.

Not only does the theory for a Universal Graph exist, the required technology already exists in the disconnected forms of big data for large companies like Netflix, Google, Facebook, and others.

The Universal Graph is designed to take the information that all these entities possess and put it together in a computational alternate reality of our world. This alternate reality is then subject to formulas that are able to determine large-scale patterns all over the world. It is similar to how big data companies collect and analyze data now but on a universal scale.

The Universal Graph

So, what exactly will this supercomputer include? The possibilities are endless.

A Universal Graph could technically contain information about anything and everything from an animal’s set of genes, a particle, a book, a company, and even an entire person. The Universal Graph interlocks these data points with the rest of the data. For example, the Universal Graph could implement the information from a textbook into a specific gene and splice that gene into a human being who would then carry a gene that allows them to know everything within the contents of the aforementioned textbook.

A Universal Graph may sound like a fantastical science fiction concept, it can become a possibility in the near future.

Right now, big corporations are already collecting data about you as an individual. They know your date of birth, college or high school transcripts, shopping habits, social media posts, and even the things you eat.

It’s an Information World

The world is getting closer and more connected as technology continues to advance, which may or may not be a good thing. What is known is that big data is here to stay, and something like the Universal Graph might have seemed far-fetched 20 years ago has now become a very plausible concept we may soon see. Nothing is for certain about the future. The technological world that we live in is full of surprises. Let’s wait and see what the future of big data has in store.

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.