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6 minutes read

Charting the Course of Big Data Evolution

By Jonathan Tarud
By Jonathan Tarud
6 minutes read

Big data evolution has occurred rapidly since the introduction of smartphones, cloud computing, and the Internet of Things. What once was a niche discipline for data scientists and HiTech developers, big data analytics have become the norm in every industry, from manufacturing and medicine to telecommunications and Major League Baseball. 

If your organization isn’t using big data, you will likely fall behind your competitors. Data collection and analytics have become critical aspects of modern business. However, big data is constantly evolving. As a result, if you want to gain a competitive advantage in the market, your business needs to understand the evolution of big data and the trends driving future growth. 

This post will briefly explore how big data analytics has evolved. Then we will share the current technology trends driving the future of big data. If your business wants to retain a competitive edge over the competition, it needs to utilize the latest advancements in the evolution of big data. 

A Brief History of Big Data Evolution 

You might be surprised to learn that we can trace the roots of big data analytics to 1660’s London. During this time, a man named John Graunt began collecting data on mortality rates connected to the bubonic plague to track how the disease spread and create a warning system for society. 

While other interesting achievements and milestones occurred in data analytics over the next three hundred years, we will skip ahead to 1965. In 1965, the United States created the first national data center. This data center is an early example of cloud computing. The government used this data center to manage all government records from a central location. 

In the following years, many of the principles associated with big data analytics were pioneered as part of database management systems, including database queries, data extraction, expanding storage capacities, and reporting tools. During the late 90s and early 00s, when the Internet greatly expanded and websites experienced an incredible growth rate, an enormous amount of new data was generated. 

As social media, smartphones, GPS technology, and the Internet of Things became ubiquitous during the 2010s, big data as we know it today was adopted by organizations everywhere to improve operations and reduce costs. Over the last decade, IoT devices have improved significantly from rudimentary fitness trackers to a wide array of different objects capable of capturing staggering amounts of data. Savvy businesses use these innovations to capture data from every facet of their business, from digital interactions to industrial operations and equipment. 

Today, data analysis is improving at a rapid rate. Machine Learning algorithms no longer rely on the traditional relational database models to draw conclusions. Now, seemingly unconnected data points from separate sources can be analyzed and connected to form more accurate predictions and more precise insights. 

What Does the Future of Big Data Hold? 

Possessing a basic sense of the history of big data is a great way to gain context for the current trends and technologies driving the innovations in big data analytics. Whether your company wants to use big data to power your mobile app’s offerings, improve the efficiency of your business operations, or make more informed strategic decisions, you need to know about the latest trends and technologies driving the current phase of big data evolution. 

The most important trends and technologies driving the evolution of big data today are: 

  • Artificial Intelligence (AI)
  • Growing data volumes 
  • Cloud-native solutions
  • Data security 

Artificial Intelligence (AI)

As Artificial Intelligence continues to improve, it seemingly has been a driving force of innovation in almost every technology field. Make no mistake; AI will fundamentally change the world as we know it. However, at this point, Artificial Intelligence has been mostly used in data analytics, forecasting, fraud detection, and other data-related tasks. 

Every year, AI and Machine Learning algorithms get more advanced, accurate, and efficient. AI is the most important technology driving the evolution of big data. The more information Artificial Intelligence is given access to, the more accurate it can be. Luckily, while AI programs are getting more sophisticated, more data is being created and collected. 

If your business invests in one technology, AI should be at the top of your list. 

Growing Data Volumes 

We briefly touched upon this while discussing Artificial Intelligence, but it needs to be clearly noted. Growing data volumes are one of the driving factors to the adoption and evolution of big data analytics. For the last decade or more, businesses have discovered how valuable data truly is. As a result, data volumes will only continue to grow.

It only makes sense that as data collection improves, big data analytics improves. In addition, more connected devices are commercially available than at any other time. As a result, businesses can afford to buy powerful IoT devices and implement them throughout their operations. Plus, consumers use more devices, create more profiles, and generate more unique data than ever before. 

Don’t miss out on the wealth of data being generated. Big data solutions can help your company collect and analyze data you weren’t even aware was being generated. 

Cloud-Native Solutions 

Growing data volumes have created greater demand for storage. Cloud computing has been around for a while, but big data has driven more businesses and consumers to invest in cloud solutions. As more data is being stored on the cloud, cloud-native development will become a necessity businesses shouldn’t overlook. 

Cloud-native development can help your company create applications that run on the cloud and integrate seamlessly with your big data analytics. Cloud-native applications can handle larger amounts of data, don’t have a single point of failure, and can scale easily as demand increases. 

Data demands will only get heavier as you collect more data and offer more services. Cloud-native architecture ensures that your business can handle all of the demands growing data volumes will surely put on it. 

Data Security 

With growing data volumes and cloud storage, data security becomes a pressing concern for businesses and users. Poor cyber security can lead to breaches that damage the reputation of your business and are financially costly. Cyber attacks evolve rapidly, and some of the most damaging attacks are the least sophisticated because they prey on operator error.

Data security technology is improving too. Beyond AI, security technology is one of the most significant forces of innovation in big data. The consequences of security breaches are well-publicized, and no company wants to put its customers, partners, or employees at risk. 

When you start implementing big data analytics in your organization, ensure that data security is a priority. Beyond security tools and employee training, you need to hire security professionals to actively protect your data. 

Final Thoughts 

Technological advancements have marked the evolution of big data, but the underlying trends such as data volumes, cloud computing, and security are just as important. After all, innovations in technology drive trends. If you need help utilizing the latest innovations in the history of big data evolution, reach out to an app development partner.

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