Big Data Trends that will Shape 2019
The road ahead
While 2018 saw continued growth and dependency on big data, the world also witnessed high-profile data breaches and online scams. Today, big data has become integral for business, particularly online business. Its growing dependency has manifested in a clear shift in the focus of business towards data-driven decision making. Companies, both large and small, are always on the lookout for new tools to accurately analyze data and make critical business decisions based upon such analysis.
Even though big data has become the spine of a business, it’s still too early to predict whether, in 2019, the spine will mature or make companies vulnerable to data breaches. Regardless of the outcome, it’s clear that big data will shape 2019, and continue to shape decisions in years to come.
With advancements in technology, and the eagerness to keep up with increasing consumer demands for innovative and improved products, data analysis becomes even more important to keep companies alive. This applies to developers, manufacturers, retailers; virtually every branch of the business world. Knowledge is in high demand, and to have knowledge is to realize what consumers truly want and drive decisions based on such information.
Having such knowledge is what enables educated guesses regarding technological advancements, what’s likely to be trending, and what’s likely to flop. That knowledge, of course, is born from big data, but is only as good as the analysis of that data and how it’s applied.
Here, we take a look into how big data is shaping technology in 2019.
1. Smarter and intelligent cars
From self-driving smart cars to vehicles connected using the Internet of Things (IoT), big data has been changing the entire landscape of the automotive industry for years. Previously, data has been used to improve driver experiences, vehicle safety, and reduce carbon emissions. All of this data collected has served another important purpose too, in laying the foundation of autonomous vehicles.
IoT enables sharing and receiving crucial data with ease, which has the ability to make cars smarter and more intelligent, eventually even helping to develop the first fully autonomous vehicle. The vehicle will be able to collect crucial data concerning maps, intended routes, obstacles on the road, operational status of the engine, tire pressures, the status of the oxygen sensor and many other details remotely, allowing data to be shared between IoT connected cars, enabling them to communicate with each other, improving the possibility of automation.
As car companies gather more data about the customer, they will also come to know a lot more about the behavior of the consumer. For example, data can determine a link between the type of music a consumer listens to and the places they frequently visit. This type of crucial information can then give the company knowledge of where to focus marketing resources and other business decisions. By gathering big data connected to cars, companies can discover numerous crucial correlations like this.
The downside, however, is the fear of too much accessibility. In this case, that means the connected vehicles will be more susceptible to hacking. With any data-driven application, this is a common worry, but with increased knowledge of how such connectivity can be exploited, expect increased security measures to prevent such breaches.
2. The rise of quantum computing
According to a study by Northeastern University, approximately 2.5 exabytes of data are created every single day, and data continues to grow exponentially in the digital age. With such a vast amount of data at stake – the only available answer is quantum computing.
For example, an e-commerce website has a database (favorite shopping time, spikes in product searches, etc.) with 150 quintillion entries. A classic computer is likely to take an unreasonable amount of time to search for a particular data. However, with quantum computing, the time needed for such a search is reduced because the computer will not examine the entire data-set to search the desired data. It will not only improve the speed of analyzing big data, but will also help in classification and topological analysis of extremely complex data. Quantum computing will probably be the hottest trend to look for in the coming year.
3. Increased penetration of Artificial intelligence platforms
Similar to 2018, Artificial Intelligence (AI) will continue to rule the tech world in 2019. Because AI can be implemented in numerous ways, the technology is making the tech world curious, and companies are likely to explore the capabilities of AI platforms processing big data.
With the rise of AI platforms such as Microsoft Azure machine learning, Google Cloud Prediction API, and many more, the big data processing will be faster and more accurate. When an AI platform is well-designed, it provides quicker and more efficient communication with the data scientist. This will indirectly reduce the number of unnecessary overheads, including time spent on copying, pasting, automating simple, time-consuming routine tasks.
4. Improved data retention
While most companies have to save their big data for some time, it doesn’t have to be stored forever. In 2019, it is likely that machine learning will be capable of removing the stored data while maintaining the integrity of the data-set. Data that’s no longer required will be cleaned up without compromising security.
In short, the improved data retention would mean securely flushing out the data automatically. Interestingly, the data won’t be lost forever because the algorithm created will help in the restoration of the data.
5. The rise in the number of open source solution
In the coming year, it is expected that companies will launch open source solutions on the cloud to improve data processing speeds. These open source solutions don’t burn a hole in the wallet, given their affordable. This will help small businesses and startups to reduce their operational costs while gaining crucial information about consumer behavior and trends.
6. A surge in predictive analysis
Predictive analysis is the practical result of big data and its improvements on BI (Business Intelligence. It is the practice of obtaining information from previous data sets, to help identify patterns which can help form predictions for future trends. It is not exactly a true predictor of the future; instead it is used to forecast future possibilities.
With the help of predictive analysis, largely attributed to big data, companies in 2019 will be able to create better cross-sell opportunities, predict consumer behavior and analyze crucial information.
In the technology sector, using big data, predictive analysis will forecast the probable network downtime, resources needed, etc. For example, it can empower companies to take preventative action concerning critical applications to avoid downtimes before they occur.
Also, due to improvements in technology, it’s likely that predictive analysis reduces the instances of false positives. Another positive of predictive analysis in 2019 will see its application help cryptocurrency investors predict market trends.
This year is likely to bring a major shift in the way companies use and handle big data for their decision-making processes. The increased pace of technological advancements creates more opportunities for big data applications. Add the increasing consumer demands, and the result is an even greater need for big data applications to determine consumer wants and needs.
As technology and innovation become increasingly reliant on data, the fear of data breaches increases also. The over-connectivity of this digital era has proved beneficial to innovation but has also proven to be a treasure trove for hackers. With the aforementioned increased reliance on data, companies must never forget to focus, significantly, on protecting such data to avoid catastrophic breaches that severely damage their reputation permanently.