Since its inclusion as "hype" in the technology world, big data has been repeatedly projected as some sort of a miracle for all the corporate woes of the connected age. But since hypes are impermanent, the initial frenzy around big data is subsiding. Analyst firm Gartner already removed it from its famous "Hype Cycle" in 2015. So, have enterprises slammed the brakes on mining it?
No, they haven't. Big data is going strong and poised to make even bigger strides in delivering eye-opening insights. What has happened is big data is now an established technology which has outgrown the unnecessary fuss. Enterprises are seeking real value from it, not a bunch of towering promises. On that note, let's discuss the future of big data and where it's currently standing.
SNS Research had predicted that by the end of 2017, combined revenue from big data related hardware, software, and professional services would fetch vendors over $57 billion. In 2020, at a CAGR of 10%, it will leap beyond $76 billion. While IDC is far more optimistic; it projects that revenues from big data and business analytics will surpass $210 billion at a CAGR of 11.9% in two years. Hence, according to Grand View Research, the big data market will boast a size of $123.2 billion by 2025. Now, are these exciting vistas making you curious about the upcoming trends in big data? Then, let's dive into the world of big data in 2020.
Industries, worldwide are headed for a paradigm shift with the evolution of Big Data. Some of the top upcoming changes in the industry are as follows -
This is going to be one of the key trends of big data in 2018 and it won't die down thereafter. Back in 2016, Gartner estimated that by 2019, 9 out of 10 large companies would have a CDO. Reiterating the spirit of the Gartner forecast, NewVantage Partners declared that over three-fifths (62.5%) of firms have appointed a CDO. Going forward, Chief Data Officers will have greater authority within the enterprise. In essence, they will no longer play second fiddle to Chief Information Officers. More and more businesses will expect them at the forefront of data monetization. Further, a significant outcome of elevating CDOs to highly responsible positions as they can be instrumental in connecting corporate data assets with line-of-business users.
To use Big Data in the most fruitful and secure manner, businesses need a foolproof governance framework that provides an accurate description of the provenance of data, fosters democratization, and effectively manages accessibility. The European Union's imminent General Data Protection Regulation (GDPR) will have a monumental impact on how enterprises handle data of persons living in any EU member country. Strict provisions of GDPR will not allow a business guilty of malpractice get off the hook easily. Penalties will run into millions (4% of annual global turnover or €20 million whichever is higher). However, a report from Forrester Research published in January revealed a bleak picture about GDPR preparedness, with just 26% of European companies being compliant. Across the pond also the situation is equally pathetic, says another survey. So, when you conceive of the future of Big Data, the governance issue will stay relevant.
So, what is Dark Data, anyway? Every day, businesses collect a lot of digital data that is stored, but is never used for any purposes other than regulatory compliance and since we never know when it might become useful. Since data storage is easier, businesses are not leaving anything out. Old data formats, files, documents within the organization are just lying there and being accumulated in huge amounts every second. This unstructured data can be a goldmine of insights, but only if it is analyzed effectively. According to IBM, by 2020, upwards of 93% of all data will fall under Dark Data category. Efforts to utilize this type of data will pick up considerable steam from 2018 onward. Thus, big data in 2020 will inarguably reflect the inclusion of Dark Data. The fact is we have to process all types of data to extract maximum benefit from data crunching.
The next computing juggernaut is getting ready to strike, the quantum computers. These are the uber-powerful computers that have principles of Quantum Mechanics working on their base. Although, you have to wait patiently for at least another half a decade before the technology hits the mainstream. One thing is for sure; it will push the envelope of traditional computing and do analytics of unthinkable proportions. Predictions for big data are thus incomplete without quantum computing.
For some time now, data lakes - storage repositories that hold all the raw data of enterprises in their native formats - have been the darling of enterprises. One of the main selling points of a data lake is that it puts an end to information silos. But the issues of quality, consistency, lack of alignment with business teams, or over-the-top governance are acting as stumbling blocks to grab actionable insights. An SAP study titled "Data 2020: State of Big Data" has found that nearly one-third (31%) of respondents consider data lakes as one of the most challenging data sources. Data lakes have to live up to their promise or else they will fall by the wayside.
The deadly duo will get beefed up with more muscles. Continuing with our round-up of the latest trends in big data, we will now take stock of how AI and ML are doing in the big data industry. Artificial intelligence and machine learning are the two sturdy technological workhorses working hard to transform the seemingly unwieldy big data into an approachable stack. Deploying them will enable businesses to experience the algorithmic magic via various practical applications like video analytics, pattern recognition, customer churn modeling, dynamic pricing, fraud detection, and many more. IDC predicts that spending on AI and ML will rise from $12 billion in 2017 to $57.6 billion in 2021. Similarly, companies pouring money into AI are optimistic that their revenues will increase by 39% in 2020. Moving ahead, the technologies will aid enterprises in prognosticating events with unmatched precision.
The phenomenal proliferation of IoT devices demands a different kind of analytics solution and edge analytics is probably the befitting answer. Edge analytics means conducting real-time analysis of data at the edge of a network or the point where data is being captured without transporting that data to a centralized data store. For its on-site nature, it offers certain cool benefits: reduction in bandwidth requirements, minimization of the impact of load spikes, reduction in latency, and superb scalability. Surely, edge analytics will find more corporate takers in future. One survey says between 2017 and 2025, the total edge analytics market will expand at a moderately high CAGR of 27.6% to pass the $25 billion mark. This will have a noticeable impact on big data analytics as well.
The landscape of big data is not free of challenges. Below, we outline the top challenges in Big Data -
We have made a humble attempt to present a brief overview of trends, growth and challenges of big data industry in 2020. But, some thoughts expressed herein may later prove to be off the mark, as tomorrow can usher in a different reality. That's the beauty of a space as dynamic as big data.
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