January 30, 2020
Table of contents
Once global data started to grow exponentially a decade ago, it has shown no signs of slowing down. It‘s aggregated mainly via the internet, including social networks, web search requests, text messages, and media files. Another gigantic share of data is created by IoT devices and sensors. They are the key drivers for the global big data market growth, which has already has reached 49 billion dollars in size, according to Statista.
The world is powered by big data now forcing companies to seek experts in data analytics, capable to harness complex data processing. But will it be the same in the future? In this article, you will find experts’ opinions and five predictions on the future of big data.
The majority of big data experts agree that the amount of generated data will be growing exponentially in the future. In its Data Age 2025 report for Seagate, IDC forecasts the global datasphere will reach 175 zettabytes by 2025. To help you understand how big it is, let’s measure this amount in 128GB iPads. In 2013, the stack would have stretched two-thirds of the distance from the Earth to the Moon. By 2025, this stack would have grown 26 times longer.
What makes experts believe in such a rapid growth? First, the increasing number of internet users doing everything online, from business communications to shopping and social networking.
Second, billions of connected devices and embedded systems that create, collect and share a wealth of IoT data analytics every day, all over the world.
As enterprises gain the opportunity for real-time big data an, they will get to create and manage 60% of big data in the near future. However, individual consumers have a significant role to play in data growth, too. In the same report, IDC also estimates that 6 billion users, or 75% of the world’s population, will be interacting with online data every day by 2025. In other terms, each connected user will be having at least one data interaction every 18 seconds.
Such large datasets are challenging to work with in terms of their storage and processing. Until recently, big data processing challenges were solved by open-source ecosystems, such as Hadoop and NoSQL. However, open-source technologies require manual configuration and troubleshooting, which can be rather complicated for most companies. In search for more elasticity, businesses started to migrate big data to the cloud.
AWS, Microsoft Azure, and Google Cloud Platform have transformed the way big data is stored and processed. Before, when companies intended to run data-intensive apps, they needed to physically grow their own data centers. Now, with its pay-as-you-go services, the cloud infrastructure provides agility, scalability, and ease of use.
This trend will certainly continue into the 2020s, but with some adjustments:
Playing a huge role in big data, machine learning is another technology expected to impact our future drastically.
Machine learning is becoming more sophisticated with every passing year. We are yet to see its full potential—beyond self-driving cars, fraud detection devices, or retail trends analyses.
Vice President and General Manager at Intel
Machine learning is a rapidly developing technology used to augment everyday operations and business processes. ML projects have received the most funding in 2019, compared to all other AI systems combined:
Not until recently, machine learning and AI applications have been unavailable to most companies due to the domination of open-source platforms. Though open-source platforms were developed to make technologies closer to people, most businesses lack skills to configure required solutions on their own. Oh, the irony.
The situation has changed once commercial AI vendors started to build connectors to open-source AI and ML platforms and provide affordable solutions that do not require complex configurations. What’s more, commercial vendors offer the features open-source platforms currently lack, such as ML model management and reuse.
Meanwhile, experts believe that computers’ ability to learn from data will improve considerably due to the application of unsupervised machine learning approach, deeper personalization, and cognitive services. As a result, there will be machines that are more intelligent and capable to read emotions, drive cars, explore the space, and treat patients.
What fascinates me is combining big data with machine learning and especially natural language processing, where computers do the analysis by themselves to find things like new disease patterns.
Author, Big Data: Using SMART Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance
This is intriguing and scary at the same time. On the one hand, intelligent robots promise to make our lives easier. On the other hand, there is an ethical and regulatory issue, pertaining to the use of machine learning in banking for making loan decisions, for example. Such giants as Google and IBM are already pushing for more transparency by accompanying their machine learning models with the technologies that monitor bias in algorithms.
The positions of Data Scientists and Chief Data Officers (CDOs) are relatively new, but the need for these specialists on the labor market is already high. As data volumes continue to grow, the gap between the need and the availability of data professionals is already large.
In 2019, KPMG surveyed 3,600 CIOs and technology executives from 108 countries and found out that 67% of them struggled with skill shortages (which were all-time high since 2008), with the top three scarcest skills being big data/analytics, security, and AI.
No wonder data scientists are among the top fastest-growing jobs today, along with machine learning engineers and big data engineers. Big data is useless without analysis, and data scientists are those professionals who collect and analyze data with the help of analytics and reporting tools, turning it into actionable insights.
To rank as a good data scientist, one should have the deep knowledge of:
Striving to improve their operations and gain a competitive edge, businesses are willing to pay higher salaries to such talents. This makes the future look bright for data scientists.
Also, in an additional attempt to bridge the skill gap, businesses now also grow data scientists from within the companies. These professionals, dubbed citizen data scientists, are no strangers to creating advanced analytical models, but they hold the position outside the analytics field per se. However, with the help of technologies, they are able to do heavy data science processing without having a data science degree.
The situation is unclear with the chief data officer role, though. CDO is a C-level executive responsible for big data governance, availability, integrity, and security in a company. As more business owners realize the importance of this role, hiring a CDO is becoming the norm, with 67.9% of major companies already having a CDO in place, according to the Big Data and AI Executive Survey 2019 by NewVantage Partners.
However, the CDO position stays ill-defined, particularly in terms of the responsibilities or, to be more precise, the way these responsibilities should be split between CDOs, data scientists, and CIOs. It’s one of the roles that can’t be ‘one-size-fits-all’ but depends on the business needs of particular companies as well as their digital maturity. Consequently, the CDO position is going to see a good share of restructuring and evolve along with the world becoming more data-driven.
Data security and privacy have always been pressing issues, showing a massive snowballing potential. Ever-growing data volumes create additional challenges in protecting it from intrusions and cyberattacks, as the levels of data protection can’t keep up with the data growth rates.
There are several reasons behind the data security problem:
Statistics demonstrate the scale of the problem. Statista calculated the average cyber losses which amounted to $1.56 million for mid-sized companies in the last fiscal year, and $4.7 million across all company sizes, as of May 2019.
Apart from the EU’s GDPR, many states in the US have passed their own privacy protection laws, such as the California Consumer Privacy Act. As these laws bring out severe consequences for non-compliance, companies have to take data privacy into account.
Another point of concern is reputation. Though many organizations treat privacy policies as a default legal routine, users have changed their attitude. They understand that their personal information is at stake, so they are drawn to those organizations that provide transparency and user-level control over data.
It's no wonder that C-level executives identify data privacy as their top data priority, along with cybersecurity and data ethics. Compared to 2018, companies invested five times more into cybersecurity in 2019:
Yet another prediction about the big data future is related to the rise of what is called ‘fast data’ and ‘actionable data’.
Unlike big data, typically relying on Hadoop and NoSQL databases to analyze information in the batch mode, fast data allows for processing in real-time streams. Stream processing enables real-time big data analytics within as little as just one millisecond. This brings more value to organizations that can make business decisions and take actions immediately when data arrives.
Fast data has also spoilt users, making them addicted to real-time interactions. As businesses are getting more digitized, which drives better customer experience, consumers expect to access data on the go. What’s more, they want it personalized. In the research cited above, IDC predicts that nearly 30% of the global data will be real-time by 2025.
Actionable data is the missing link between big data and business value. As it was mentioned earlier, big data in itself is worthless without analysis since it is too complex, multi-structured, and voluminous. By processing data with the help of analytical platforms, organizations can make information accurate, standardized, and actionable. These insights help companies make more informed business decisions, improve their operations, and design more big data use cases.
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