Big Data Analytics has opened a treasure trove of opportunities for businesses all around the world. Opportunities that were previously unheard of have opened up as businesses microscopically analyze the data related to their business.
Analyzing data collected at various points during the consumer’s interaction with the business is proving to be a vital tool in the arsenal of enterprises.
Let us peep into the future of Big Data Analytics and see what the future holds for this exciting field.
Table of Contents
Analyzing Big Data is costly as it requires utilizing specialized resources called a data scientist who are experts in statistics and maths. It has given rise to companies moving towards data automation. By leveraging the power of A.I. and machine learning, Big Data automation has substantially reduced the time taken to analyze a vast data set.
These data automation systems are expected to gain prominence considering the benefits they provide in terms of cost reduction and in improving the speed of data analysis. Data automation also helps the data analysts in testing specific scenarios that they might not have otherwise considered.
Data automation models are especially useful for “citizen data scientists” who are people without high-level technical skills and can perform moderately tricky tasks. Thus aid themselves and the organization in growing by effectively utilizing the power of Big Data Analytics. Data automation models are expected to accelerate the adoption of data-driven cultures by giving the power in the hands of laymen.
The Emergence of Industry-Specific Job Roles
As the number of industries acquiring Big Data Analytics technologies increases, there is a rising demand for specialized Big Data analysts. Big Data Analytics companies understand the fact that people who have worked in the industry will be in a better position to understand the process and gain more powerful insights by applying Big Data Analytics technologies.
For e.g.:- a production engineer who is working on the production floor of an automobile company can prove to be a much more useful resource for a Big Data company specializing in designing Big Data solutions for the automobile industry.
What is quantum computing? As we know, our present computing system is based on binary numbers 0 and 1. A single combination of 0 and 1 is called as a 1bit; thus, a bit can have only two states either 0 or 1. In quantum computing, we use qubits, which are quantum bits. The beauty of quantum bits is that they can exist in any state between o and one, and hence a quantum computer is multiple times more powerful than a regular computer.
Google has built a quantum 54-qubit processor called “Sycamore.” They used this processor to test the idea of quantum supremacy, which says that a quantum computer is much more faster than a traditional binary computer. Google gave a set of calculations to Sycamore, and the processor gave the results in 200 seconds, an astonishing feat considering that the same set of calculations would have taken 10,000 years for the fastest supercomputer in the world.
We are living in a world where humans and machines(read IoT devices) are producing data at mind-boggling speeds. According to an estimate, we humans are producing 2.5 quintillion bytes of data every single day, and the pace of data creation is growing. Most of the data is in unstructured form and is of no use if we do not have a processor to make sense of this Data and give us actionable results. This is similar to a situation where the water of a considerable river goes wasted without a dam. Quantum computer can prove to be the dam which can enable us to harness the power of this vast river of data. Quantum computing can process vast amounts of unstructured at a fantastic speed and help in opening up new avenues and analyze previously unseen data patterns.
Google has taken the first stride in this exciting area, and quantum computing is undoubtedly going to become a reality in the coming year. Quantum computing can be used in many areas, which include drug discovery and protein folding in healthcare, portfolio risk assessment and fraud detection in the financial services industry, predicting the weather in real-time by analyzing the inputs from weather satellites all over the world and securing online transactions using quantum cryptography.
The USA is currently researching the prospect of using quantum computing to monitor its electrical grids. The U.S. electrical grid system generates three petabytes(3 million gigabytes) of data every 2 seconds, and without a method to analyze this vast amount of data quickly, all this Data is of no use.
Role of A.I. and ML in Big Data
Artificial intelligence systems combined with Big Data analytics and Deep learning algorithms will play a prominent role in the future.
Industries are dealing with increasingly complex data sets, unstructured data(data generated by IoT sensors, email messages, audio, video, photos, webpages, presentations) is the most prominent type of data set being made today. Analyzing such vast quantities of unstructured data sets is proving difficult. This is where the role of A.I. comes, as A.I. uses deep learning algorithms to allow the machines to make sense of this vast quantity of data. The combination of A.I. and Big Data has led to the creation of “Augmented Analytics,” which uses the power of A.I. to analyze the data in a much faster manner than humans, thus reducing the dependency of the system on data analysts and data scientists.
Many smart cities all over the world can use the power of augmented Analytics in identifying underlying patterns and taking faster and better decisions related to water management, traffic management, disposal of municipal services, and in other areas, which were previously dependent on traditional computers. This will free up the city’s administrative staff and help them in concentrating on other activities that require human intervention.
IoT and Data Analytics
It is predicted that by 2020, the population of IoT devices will be more than twice the human population. The number of IoT devices is expected to balloon to 20.4 billion by 2020. The data collected by these IoT devices are of little use without the use of Big Data Analytics algorithms, which will sift out valuable insights from the data collected by these devices.
IoT is already being used in many places to provide exciting insights into the consumer’s behavior. For e.g.:- IoT connected coffee makers are providing invaluable insights to manufacturers of these machines like how many cups of coffee does an average person make during a day, whether the coffee consumption is higher during the weekdays or during the weekends.
IoT is being used in the domain of sentiment Analytics, which pertains to studying the interaction of users with a brand on social media. IoT sensors are being deployed in fashion shows and in basketball league games to gauge the level of engagement of the audience with the event. These sensors provide data to the Big Data Analytics algorithms, which then determines the level of human engagement by analyzing the changes in the emotions of the audience. The human emotions are measured using a variety of sensors, which include gyros, high-speed video cameras(to detect the facial expressions), accelerometers, audio, heart rate sensors, skin conductance sensors, to name a few.
The sophistication level of these sensors will increase in 2020, and we will see many more such exciting applications, which would be a result of the marriage between IoT and Big Data.
Usually, Data is stored in the database on the SSDs; in in-memory computing, the software is used to store data in RAM across a series of computers that process the data simultaneously. The reason? RAM is around 5000 times faster than an SSD. In-memory computer systems thus allow for the processing of data at lightning speeds and are ideal for applications that involve handling a sudden increase in the number of queries.
An ideal application would be handling the data of a relative gaming leaderboard.
Usually, gaming leaderboards show the top positions in a game; a relative gaming leaderboard is slightly different; it shows the relative position of gamers with respect to many parameters. For e.g.:- it can show the relative position of players with similar skill levels. Having a relative gaming leaderboard boosts the engagement level of the users with the game and helps in popularizing it. Standard relational databases won’t be able to handle a large number of queries that a relative gaming leaderboard of a game having a large user base would require. In this scenario, in-memory computing systems can come to the rescue and help in providing real-time positions of gamers in a leaderboard.
In-memory computing can prove useful in any application which requires a database to handle the massive amount of queries in a fast manner. A few potential applications can be GIS processing, medical imaging processing, NLP and cognitive computing, Real-time sentiment analysis, and real-time ad platforms.
Rise of Data as a Service Model
The primary function of Big Data Analytics is to derive meaningful insights by analyzing tons of data. While most of the companies do recognize that Big Data is going to play a vital role in the future, many do not have the required level of expertise in analyzing the data that they have. This presents a massive opportunity for companies providing Big Data as a Service (BDaaS).
The market for data services is expected to reach up to $31.75 billion by 2024.
The BDaaS model will be used in many applications in the future, like predicting fashion trends, anticipating the turnover ratio of employees, and helping in detecting bank frauds.
Edge Computing in Big Data
As the population of IoT devices grows, so does the need for quickly analyzing the humongous amount of data produced by these devices. Edge computing comes to the rescue here.
Edge computing is the concept of processing data generated by IoT devices near its source. In many applications, the speed of data processing is of paramount importance, e.g.:- giving real-time data during an F1- Racing event. In such applications, edge computing provides an ‘edge’ above the cloud computing model.
Apart from IoT, edge computing also provides benefits in applications where there are significant privacy concerns. As the Data is not sent to the cloud, this plugs in a potential security loophole. Edge computing also proves to be a boon in applications where there is a connectivity issue. Edge computing is already being used in smart building solutions, and it is expected that in the near future, as the population of IoT devices increases, edge computing will emerge as a viable solution for many applications.
Using Dark Data
Dark Data is a kind of data that was previously unutilized by the companies. With the rise in the processing power of Big Data Analytics, previously unheard uses of dark data are bound to increase.
For e.g.:- research found out data relating to the population of zooplankton during the 1970s and ’80s and used it in analysis related to climate change.
Dark data can be utilized by using a data virtualization technique, which is a technique in which all the data of a particular company is presented in a single dashboard in an easily digestible form. Thus previously unutilized data of a company can provide invaluable insights that can ultimately help in improving the bottom line of a company.
In-depth data analysis can help in analyzing vulnerable population groups and assist in predicting the next outbreak of a disease.
Many companies do not know that they already have data, which can help them in analyzing the needs of their customers and help in increasing revenues. Dark Data is going to play a pivotal role in future Big Data Analytics.
Big Data has already breached the levels of our imagination by helping in building the first humanoid robot- Sophia, in discovering the black hole and in autonomous cars.
Possibilities of Big Data Analytics are exciting, and we are fast moving towards becoming a data-driven society. Big Data Analytics has already proven it’s worth in many sectors like banking, retail, manufacturing, and shipping, and logistics, but with the advent of technologies like edge computing, in-memory computing, in-depth data analysis and quantum computing the horizon of Big Data Analytics is going to expand exponentially.