Big data and analytics (BDA) is a crucial resource for public and private enterprises nowadays, as well as for healthcare institutions in battling the COVID-19 pandemic. Thanks in large part to the evolution of cloud software, organizations can now track and analyze volumes of business data in real-time and make the necessary adjustments to their business processes accordingly. As the industry goes deeper into the age of AI, what big data trends should businesses be most wary of?
Given that the BDA market is projected to become a more lucrative field in the following years, what does this mean to the way you should be conducting business moving forward? Should you be looking into harnessing data analytics to move your business forward? Here are eleven big data trends impacting the current landscape to help you see the bigger picture.
There are already clear winners from the aggressive application of big data to clear cobwebs for businesses. On the winning circle is Netflix, which saves $1 billion a year (TechJury, 2021) retaining customers by digging through its vast customer data.
Further along, various businesses will save $1.2 trillion through IoT (American Family Insurance). Businesses with hypercomplex processes, multiple branches, departments, and thousands of teams will benefit the most when smart structures, machines, and gadgets do most of the necessary adjustments by themselves.
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However, the figures for losses are more pronounced than those for the winners. For example, poor data quality alone will cost the US economy $3.1 trillion a year (IBM). That’s already more than the GDP of many countries, but it’s further confounded by 91% of companies who feel they are consistently wasting revenue because of their poor data (Chicago Analytics Group, 2017).
This is no longer a normal global economy we are witnessing in our lifetime. Ecommerce and online carts have already obliterated thousands if not millions of businesses big and small all over the world. Watch out for how many of them will further fall by the wayside (Forbes, 2018) because of poor understanding of all the data they have.
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. They will do so from the bowels of the ocean or to the literal ends of the earth (Forbes, 2016)–the polar regions–to drive away heat which is data centers’ constant challenge.
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.
Still, corporations have much work to do optimizing the use of all that data on their data servers. In the US economy alone, for example, they are losing as much as $3.1 trillion a year (IBM) from the cost of poor data quality. It remains to be seen how these enterprises are going to address that.
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 requisite 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.
Humans would like to know whether carbon dioxide emissions are all there is to know about climate change. Who knows whether looking at the faraway galaxies might reveal some patterns about the solar system’s path along with the Milky Way’s regular celestial rotation?
We would like to know and that entails unimaginable data input from all the giant scientific observatories stationed on earth and its atmosphere (ScienceDaily).
Not only that: we would also have to incorporate unimaginably massive inputs from ocean research, earth sciences, meteorological research centers, and perhaps even from the mind-boggling nuclear research facilities as they approximate events from the Big Bang to the current age of the universe.
Why should businesses worry about climate change?
For one, agriculture production would be most affected by even the tiniest drop in the local temperature.
Two, severe climate change will drastically impact the health of populations worldwide. What that means for businesses everywhere is much too deep to even contemplate (Forbes, 2018).
Drained resources? Check. Massive population movement? Check. Massive lands submerged in oceans? Check. Food security thrown out the window? Check. Governments unable to meet the devastating changes to their lands and populations? Check.
In the face of all that, where would businesses go?
No matter which side of the climate change debate you happen to be with, a few thoughts stand out:
Apart from the scintillating game served up by the Djokovich-Federer match during the 2019 Wimbledon final, the viewers were also thrilled by the constant feed of live statistics immediately related to the on-court drama transpiring before their eyes. Those who were casual followers of the game were caught up in the clash of numbers that described the unfolding play. For a fleeting moment, they became expert analysts without the extraneous ad-libs so commonly dished out by live commentators.
For the sections of the audience who were rooting for Federer, they all but won everything except the trophy. Federer was ahead in the stats that matter, except the clutch plays that matter most when the trophy was on the line. So Djokovic took the trophy and left thousands if not millions of Federer fans watching in tears. Interesting, nerve-racking watch.
But the live statistics presentations may be more interesting for a number of reasons.
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).
For one, they go beyond tennis or any other sport that uses them—the NBA and football have been using them too, as do other major sports.
Beyond sports, think what the financial world could do with such immense power—to comb through petabytes of live data coursing through intricate network connections and finally to the servers that work with countless other devices to produce the tantalizing numerical reports. See an ongoing financial fraud as they are committed in concert by linked criminals all over the world? Check.
How about helping with earthquakes and other natural disaster prediction and prevention? Big data, AI, and machine learning are working together to finally solve this natural world riddle (Datanami, 2019).
In the meantime, organizations like Oracle are leveraging robotic process automation (RPA), machine learning, and visual big data analysis to thwart increasingly sophisticated criminal activities (Help Net Security, 2019) in the financial sector.
El Niño and other tremendous weather anomalies next get the AI and big data treatment. The latest development on the field is grabbing the headlines, with predictive capability going as deep as 18 months in advance (American Association for the Advancement of Science, 2019).
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 changes the whole concept completely.
It doesn’t have to be just dedicated SaaS software solutions getting on the act too: if you have a company whose data could mean something to others—okay, hello Cambridge Analytica— or have a hard time maintaining it, your best bet is selling it per megabyte, per specific file format, or by volume quotes.
Since data resides in the cloud, you could well be atop Timbuktu and have a play of the latest Netflix show when the clouds are not too kind to give you a spotless view of the stars.
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.
Retailers already realize increased margins of up to 60% with current analytics methodologies (Kambatla et al, 2014). The addition of the aforementioned capabilities in tandem with location-aware and located-based services should see the numbers shoot up even more.
Source: NewVantage PartnersDesigned by
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 finding new discoveries.
Consolidating all medical data is easier said than done, too. Data containing clinical records go in the vicinity of 170 exabytes for 2019 alone, with a yearly increase of about 1.2 to 2.4 exabytes per year. Getting around all those vast zeroes and ones is no mean feat but the rewards are more than worth it.
This early there are promising studies in various research laboratories to cure cancer and aging, with Silicon Valley stalwarts actively getting on in the last part. Variously called immortality project or longevity research, vast amounts of money and brain talent are being thrown to make this vision come true within their lifetimes.
Vast libraries of DNA records, patient records, research studies, and other related fields are accessed to get AI to make connections and perhaps come out with new medications altogether.
More: big data is fueling research on improving staffing of medical facilities, storing and automatically processing access to mountains of electronic health records, and allowing for real-time alerts of patient status.
As for cancer itself, big data has already produced an unexpected finding, discovering that the anti-depressant Desipramine is capable of healing certain types of lung cancer, for example (Forbes, 2016).
Various applications of big data are also proving useful in managing the COVID-19 pandemic. First, it can be used to track the impact of the pandemic in different regions, such as the COVID-19 tracker developed by the Mayo Clinic with data for over 50 states in the US Second, machine learning can come up with predictive models for patient outcomes using data on their health conditions. For example, it can predict how patients fare when they contract the disease depending on their lung condition and smoking habits (Health IT Analytics, 2020). Lastly, machine learning algorithms can be used to screen therapeutic antibodies that can be used for COVID-19 treatments, cutting down processing time from years to just weeks (Health IT Analytics, 2020).
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 has 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.
The powerful algorithms should have no trouble crunching the numbers. This could be to monitor city traffic in real-time and identify congested routes and recommend alternative roads instead.
The cost of congestion is appalling. In 2017 alone, the US, the United Kingdom, and Germany lost $461 billion due to traffic. That figure is equivalent to $975 per person (The Economist, 2018).
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But with governments imposing lockdowns and stay-at-home orders due to the pandemic, road traffic has come to a grinding halt. In the US, for example, the number of miles Americans drove was reduced by 40% in April of 2020 (LexisNexis, 2020). But interestingly, there was a 20% increase in motor vehicle death rates in the US during the first six months of 2020. This was the highest increase in death rates in a six-month period since 1999 (National Safety Council, 2020).
Though initially, people felt unsure about the safety of self-driving cars, it appears that public perception of these technologies has changed in the wake of the pandemic. In a survey, 26% of consumers in the US viewed self-driving vehicles and other autonomous delivery technologies more favorably than they did before the pandemic. These consumers with favorable views of autonomous delivery technologies are young adults aged 18 to 34 and belong to a household with children (Consumer Technology Association, 2020).
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 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.
The same advances in processing, I/O solutions, and networking allow us to model spatial scales from the subatomic realm to the supergalactic clusters. We can even add at the scale of the universe or multiverse if it comes to that.
In terms of timescales, the combination of big data, machine learning, and AI is opening up portals to the scales of femtoseconds to eons.
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. We are talking about corporations and nations already casting their eyes on future space mining ventures.
Big data, AI, IoT, 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.
NLP can give businesses access to sentiment analysis. It will allow them to know how their customers feel about their brands at a much deeper level. There are many ways the information can then be tied to specific demographics, income levels, educational demographics, and the like.
In the same vein, augmented data management will also see a rise in importance within companies. This will happen as AI becomes more efficient with enterprise information management categories. These include data quality, metadata management, and master data management among others. This means that manual data management tasks will be lessened. All of it thanks to ML and AI developments, enabling specialists to take care of more high-value tasks.
That said, companies looking to utilize this innovative technology should carefully review the available augmented data management and data analytics tools in the market that best fits their business operations. This way, they can properly integrate such solutions into their business processes and properly harness the big data.
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 than ever before.
GDPR places the power back in the hands of customers. This is done by appointing them as the firm owners of any information they create. It gives them the right to cart away their data from a misbehaving business. They can then give it to another who appreciates doing clean business with them better.
Moreover, companies and businesses shouldn’t just worry about getting fined if they fail to comply with GDPR regulations.
The effects of GDPR are a two-way street. Companies that comply will see positive effects on their brand reputations. This is most likely as customers vote for trustworthy vendors with their wallets.
Trustworthy businesses will generate more reliable big data. This ensures that any analytics thrust into the data sets will come out with solid bases.
When you pair big data with security, it’s too easy to fall for popular clichés. Among these is: “The bigger they are, the harder they fall.” How about “With great power comes great responsibility”?
And yet the events at Yahoo wherein three billion accounts were compromised (Quartz, 2018) and the much-publicized Facebook and Cambridge Analytica fiasco reminds us that when it comes to our private data, nothing is ever small and safe at the same time.
In this day and age where the world pays dearly for not properly addressing cybersecurity flaws to the tune of $600 billion a year (Mordor Intelligence, 2020), it’s much easy to become paranoid about sending financial codes over the internet superstructure. Nowadays, the average total cost of a data breach is $3.86 million. The cost of data breaches is most expensive in the US where it can reach $8.64 million (IBM).
Businesses and organizations have many cybersecurity challenges in their hands. Most likely it’s one aspect of big data that will linger longer than we would like to hear about. Non-relational databases, limited storage options, distributed frameworks are just some of the most lingering challenges of big data.
With big data becoming more and more of a lucrative resource, it is prudent that companies of all sizes should look into and invest in reliable cybersecurity software providers in order to protect such valuable business information from cyberattacks.
As we are only in the first quarter of 2021, we can expect further developments in big data analytics. Much of data use will be regulated and monitored in both the private and public sectors.
Based on market projections, big data will continue to grow. This will affect the way companies and organizations look at business information. Companies should be keen on bolstering their efforts to adapt their business operations. For that, they can begin to optimize the use of information with analytical software so that they can successfully navigate business challenges during and after the pandemic. The objective is to make their businesses grow while transforming their data-driven environment. As such, it is best to keep up-to-date with the latest big data research and news.
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