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The global big data market revenue is projected to hit the 103 billion US dollar mark by 2027, according to the DataToBiz report. The current BI and analytics software market are valued at 16 billion USD globally. Apart from the stats that speak a thousand words, big data, in association with AI, ML, and other technologies, is fueling what we call the Fourth Industrial Revolution. Big data analytics is one of the most powerful technology trends and is reshaping numerous business processes and operations across the world.
Digital transformation is the global currency pushing technology all over the world. That much work done and work still to do leaves a trail of data the volume of which is pretty much unheard of in human history. It will continue to grow as IaaS providers scamper to cover the ground and build data centers. Digital transformation goes hand in hand with the Internet of Things (IoT), artificial intelligence (AI), machine learning, and big data. With IoT-connected devices expected to reach a staggering 25.44 billion devices in 2030 from 10.07 billion in 2021 (Statista, 2021), it's easy to see where that big data is coming from. Machine learning and AI tools will try to rein in that much big data spewing out of the massive data centers from operating the systems, making sense of the hidden relationships, and storing and projecting the insights within the bounds of human understanding.
Backing up the views and predictions of climate change organizations (IPCC, 2018) like the UN Intergovernmental Climate Change (IPCC) with solid data will put the raging climate change debate to rest. In the aftermath, nations will finally work together to execute the required actions needed to save the planet. That is not to say that the data might also show other interesting insights about what's really going on with the planet's climate. Whatever the case, none of it will be legitimate without the presence of cold data exempt from the biases of humans hailing from either side of the climate change debate.
At present, most sporting events have been canceled due to the pandemic. But once restrictions are lifted and people can watch live sports again, stadiums will use big data technology that can help with crowd control and the enforcement of social distancing. For example, smart surveillance cameras can count how many people enter and exit the stadiums and notify the venue staff once maximum capacity is reached. These cameras can also be placed at stadium choke points like kiosks, ticket booths, and food and beverage stands and detect when the crowd becomes too dense, which will make social distancing difficult (Security Magazine, 2020).
No, not really, but it's a great metaphor for how data-as-a-service is becoming almost as commonplace as the proverbial mom-and-pop stores that once covered the entire landscape of the USA. How commonplace? In the region, 90% of enterprises are getting into action and generating revenue from it. Data-as-a-service (DaaS) is really nothing new or revolutionary. In fact, it's already predicted to grow to $10.7 billion dollars in revenue by 2023. Plus, you've probably encountered it in the form of purchased music, videos, or image files from multiple sources online. However, while it isn't new, it brings about the entry of a whole lot of new players from map data providers to product catalog vendors that change the whole concept completely.
Analytics in the form of business intelligence solutions has been helping businesses for a time now, with many companies adopting it for day-to-day operations. While the numbers have been impressive thus far, the new generation of this software should allow new and old customers to scale new heights. The new trend in integrating every critical aspect of business operation from advertising, supply chain management, support, and social media management among others. The vast amount of data involved could be from landing page behavior patterns, customer transactions, geographical origins, video feeds from multiple store branches, customer survey results, and the like. No matter, the new analytic tools should plow through them even in real-time and produce insights that are not possible with many offerings today. While Netflix grabs the headline among the early winners of big data analytics adoption, the future will expand the list of those making the most of taking the numbers game to the highest levels.
Businesses have much interest in investing in human welfare. Healthy populations allow them to hire healthy workers and lessen the burden on health-induced absences, payments, and other work-related issues. An alarming piece of data is that in the US alone, healthcare expenses now account for 17.7% of its GDP (CMS, 2019). It thus makes sense that one of the raging applications of big data is in the field of medicine. With the number of human maladies old and new popping up around the world, the role of big data in this industry is only to grow further. Many scientists hope that by consolidating all the medical records ever accumulated on the planet, the speed of finding medical cures will become faster and sooner than expected. The challenge is to find a middle ground among research institutions private and public throwing patents all over the place and slowing down the process of making discoveries.
While fully autonomous driving is still a long way from truly taking off, there have been significant and notable developments in the field. For instance, Apple conducted more testing on their self-driving cars and saw an improvement in disengagement rates, from 8.35 disengagements per 1,000 miles in 2019 to 6.91 disengagements per 1,000 miles in 2020 (9to5Mac, 2021). In October 2020, Waymo introduced full-level autonomous driving vehicles that customers can use to hail a ride (Unite.ai, 2020). At the start of 2021, Walmart expanded its use of driverless trucks to deliver items from a Walmart Supercenter to a Walmart pickup point (Walmart, 2020). With the right analytic tools, the enormous traffic big data could shed light on trip generation and commuter transportation management. Tracking the locations and matching the origins and target destinations should give travelers the opportunity to calculate their travel times better.
One of the biggest beneficiaries of big data analytics is the petroleum industry. With exascale computing power now within reach of oil companies, they have a better tool to probe into the enormous amount of data generated by seismic sensors. Meanwhile, high-fidelity imaging technologies and new algorithms to simulate models give them an unprecedented level of clarity into the potential of reservoirs under exploration. With clearer information on hand, they minimize risks in identifying and mapping oil reservoirs and optimizing management and operational costs. In one such case, a large oil and gas company reduced operational costs by 37% (Business Wire, 2019) after the introduction of big data analytics. While deep research into these quantum realms does not give businesses immediate windfalls, they will most likely play a big part in the activities now reaching frenetic proportions.
Big data, AI, IoT, and machine learning are pushing the boundaries of human and technological interaction. It gives these technologies a human face through natural language processing (NLP). While populations have become enamored with technologies in general, there is a pervading sense of a line clearly drawn between gadgets and humans. Technophobes will perhaps not get their David-class Osment's flavor of AI to love soon. However, natural processing should give this class of technology a warmer face and further adoption than their more dystopian Blade Runner versions. And at their current state, natural processing is not going android or cyborg soon. Instead, they will help people engage and interact with various smart systems with nothing but human language. The more advanced of them will do so with a level that comes with the nuances of the language in use. NLP will allow even the most casual users to interact with intelligent systems. They don't have to resort to exotic codes which is the typical way it is done. Not only access to quality information, too. They can also prompt the system to give them the insights they need to move forward. The content will be delivered in human voice if they so choose it. They can also opt for the summaries to be read to them even while they are on the go.
Following the introduction of the General Data Protection Regulation (GDPR) guidelines last year, data governance initiatives continue to mobilize globally. This means more uniform compliance for all business sectors that handle big data. Otherwise, they face a substantial fine and other penalties. This compliance comes after recent 2018 studies show that 70% of surveyed businesses worldwide failed to address requests by individuals who want to get a copy of their personal data as required by GDPR within the one-month time limit set out in the regulations. When companies are more forthright in handling customer data while limiting what they can do with it, people will be encouraged to trust online payment transactions more than ever before.
