After the growth of the data worldwide, it has never slowed down. It’s collected from the web, including social networks, text messages, media files, and web search requests. IoT devices and sensors create another enormous size of data. These devices majorly drive worldwide big data market growth, which has reached 49 billion dollars already as per Statista.
The world is boosted by big data that now compels the businesses to find professionals in big data consulting, capable of tackling complex data processing. This article will discuss experts’ views and forecasts on the big data in the next five years.
Predictions for The Future of Big Data
1. Data size will continue to increase and move to the cloud
Most big data experts think that the volume of data produced will increase substantially in the future. IDC predicts the data will reach 175 zettabytes worldwide by 2025, as per reports in Data Age 2025. To help you comprehend its size, we need to measure this size in 128GB iPads. In 2013, the heap would have stretched 2/3rds of the distance from the Earth to the Moon. This heap would have grown 26x longer by 2025.
Why experts believe in such fast-paced growth?
First, the rising number of web users are doing everything online, whether shopping, social networking, or business communications.
Second, tons of connected gadgets and empowered frameworks develop, gather, and share a wealth of IoT data analytics globally.
As businesses gain the opportunity to save and evaluate huge data sizes, they will get to create and oversee 60% of big data shortly. But, individual consumers have a significant role to play in data growth. IDC also anticipates that 6 billion consumers, or 75% of the global population, will be communicating with the online data each day by 2025. In other words, every connected user will be having at least one data interaction every 18 secs.
Large datasets are difficult to work within the context of their storage and processing. Open-source eco-systems like NoSQL and Hadoop solved big data processing difficulties. But, open-source tech needs manual configuration and problem-solving, which can be intricate for most businesses. To get more flexibility, organizations started to move big data to the cloud.
Google Cloud, AWS, and Microsoft Azure have evolved the ways big data is saved and processed. The pay-as-you-go services have enabled cloud infrastructure to provide scalability, usability, and agility.
2. Machine learning will alter the future drastically
ML is fast-paced tech used to expand daily operations and business processes. ML projects got the most funding in 2019, then all other AI systems combined.
ML and AI have not been available to several companies until recently, due to dominance by open-source platforms. Though open-source platforms were build to make technologies closer to people, many businesses lack skills to form needed solutions on their own.
This scenario has changed once commercial AI vendors began to develop connectors to open-source AI and ML platforms and offer affordable solutions that do not need intricate formations. Also, commercial vendors provide the features open-source platforms presently lack, like ML model management and reuse.
Experts believe that computers’ ability to learn from data will enhance widely due to more advanced unsupervised computations, intense personalization, and cognitive services. Hence, machines that are smarter and capable of studying emotions will ride cars, explore the space, and treat patients.
3. High demand for CDOs and data scientists
Despite the data scientist and CDOs (Chief Data Officers) being new, their demand for these experts on the labor market is already high. As the amount of data continues to grow, the space between the need and the availability of data experts is already significant.
KPMG, in 2019, surveyed 3600 CIOs and technology executives from 108 nations and found out that 67% of them grappled with skill shortages, with the top 3 skills being AI, security, and big data analytics.
The career as data scientists is one of the fastest-growing alongside machine learning and big data engineers. Big data is worthless without analysis, and data scientists are those experts who gather and evaluate data with the support of analytics and reporting tools.
To connect the skill gap, organizations now also increase the data scientists within the companies. These experts, dubbed citizen data scientists, create advanced analytics frameworks; however, they hold the position outside the analytics field. But with the technologies, they can do the extensive data science processing without having a data science degree.
4. Security will remain an issue
There have always been pressing issues surrounding data security and privacy. Increasing data volumes creates extra challenges in protecting it from cyber-attacks and invasion, as the stages of data protection can’t match the data growth rates.
Below are some problems behind the data security problem:
The security skill gap is due to a lack of education and training facilities. This gap is continually growing and, by 2021, will reach 3.5 million unfilled cybersecurity positions, as per Cybercrime Magazine.
The threats used by hackers are transforming and getting more intricate by the day.
Despite the government measures to standardize data protection regulations, most businesses still neglect the data security standards.
Another concerning factor is reputation. Though many businesses treat privacy policies as a default legal routine, consumers have changed their attitude. They think that their information is at risk, so they are attracted to those businesses that provide clarity and user-level control over data.
5. Fast data and actionable data will be leading
Fast data, unlike big data, allows processing in real-time streams. Due to such stream processing, data can get evaluated instantly. This brings more value to the businesses that can make business decisions and take action as soon as the data arrives.
Fast data has offered users real-time interactions. With businesses getting more digitized, one can expect better customer service experiences and more personalized services.
Actionable data is the missing piece between big data and business value. Big data in itself is useless without evaluation since it’s too intricate, multi-structured, and sizable. By processing information with the help of analytical platforms, businesses can make more precise, standardized, and actionable, thus helping companies make better decisions and optimize their operations.
The big data in the next five years guarantee to change the way businesses function in healthcare, finance, manufacturing, and other sectors.
The enormous volume of big data may create more challenges in the future, including data security and risks, lack of data experts, and data storage and processing problems.
But, most of them believe that big data will mean more value. It will increase job categories and also entire departments responsible for data management in big organizations. New norms and frameworks will emerge as businesses continue to use user’s private information. Most organizations will switch from being data-producing to data-boosted, making use of actionable data and business insights.