Five Hottest Big Data Trends

By Jyoti Nigania |Email | Jul 27, 2018 | 8949 Views

What are the Current Trends in Big Data?
Answered by Anna Kowalcczyk on Quora:
The growth of data is not going to stop. This will usher in new challenges and opportunities. Below you can find the five hottest big data trends for techies and business.
Five Trends for Business:
1. Growing demand for data science and analytics talents:
According to a recent Glassdoor report, data scientist is "the best job in America in 2018". At the beginning of the year, there were 4,524 job openings with a median base salary of $110,000. Candidates follow the market closely, and it should come as no surprise that new ways to become a data scientist are emerging. As Linkedin's 2017 U.S. Emerging Jobs Report states, there are 9.8 times more Machine Learning Engineers working today than in 2012.
2. Augmented analysis on the rise:
According to Gartner, augmented analytics is "an approach that automates insights using machine learning and natural language generation" that "marks the next wave of disruption in the data and analytics market". With augmented analytics, companies can use new technologies to efficiently harvest the insights from data, both obvious and less obvious ones. According to Forbes Insights, 69% of leading-edge companies believe that augmented intelligence will improve customer loyalty and 50% of all companies surveyed think that these technologies will improve customer experience. What's more, 60% of companies believe that augmented analytics will be crucial in helping them obtain new customers. Clearly, this is not a technology to overlook over underestimate in future investments. Given the volume of data being produced today and the amount of possible correlations it gives rise to, the days of its being inspected manually are numbered.
3. Edge computing speeds up data transfer:
Just as the number of connected devices is growing, so too is the need for computing power required to analyze data. There will be no fewer than 30 billion data-producing devices in 2020. With the decentralization of data comes a plethora of challenges, including delays in data transfer and huge pressure exerted on central infrastructure. To optimize the amount of data to be transferred via the Internet, companies perform more and more computing near the edge of the network, close to the source of the data. Consider autonomous vehicles, each of which, according to GE, generates 40 terabytes of data for every eight hours of driving. It would be neither practical nor cheap and for sure not safe to send all that data to an external processing facility.
4. Convergence, or merging the past, present and future world in one dashboard:
Merging data from various sources is nothing new companies have been using weather forecasts or combining historical data with sales predictions for a long time. New technologies, however, are bringing data merging into the mainstream, and there's a growing number of use cases to exemplify just why this is happening. One inspiring example comes from Stella Artois, the producer of apple and pear cider. Analysis of its historical data showed the company that its sales grow when the temperature rises above a certain degree. So it decided to run an outdoor campaign on digital billboards on a cost-per-minute basis, triggered only by specific conditions proper temperature, sunny weather and no clouds. The company reported a YOY sales increase north of 65% increase during the period when its weather-responsive campaign ran, efficiently harnessing the power of its data.
5. New data types, Excel is not enough (nor even close):
With voice-activated personal assistants or image recognition technology going mainstream, companies will gain access to new, unstructured types of data. By dint of the sheer amount and nature of such data, those flowing from diverse and unstructured sources need to be processed with machine learning algorithms. Manual processing would just be too time-consuming and ineffective. Build your AI solutions the right way's cooperation with global researcher Nielsen provides a fine example of such algorithms at work. With the power of deep learning, Build your AI solutions the right way built an app that swiftly recognizes the ingredient fields on various FMCG products and then uploads the information into a structured, centralized database. With an accuracy of 90%, the solution gives researchers a new tool to deliver faster analysis of the retail market.
The growth of data is not going to stop. This will usher in new challenges and opportunities. Below you can find the five hottest big data trends for techies and business.
Another answer given by Shawan Masters on Quora:
Making more data processing and results available to more people is the biggest and most basic trend. For a long time big data required large computer infrastructure and specialized programming. It wasn't uncommon for custom code to utilize MPI on supercomputers/clusters to achieve some of the results a decade ago. Cluster computing has gotten progressively easier to implement and databases have gotten faster reducing the need for custom implementations in places.
Now hardware is fast enough, and cheap enough (pay for what you need, cloud model) that solutions don't require massive investments in custom computation. Now a lot more people can do the same big data projects without hand optimization and a private computer room. The class of problems that requires more than a data scientist with an AWS account has shrunk significantly.
Now that handling of the data is more often a commodity, the interaction with the data is the next big thing. Doing more in the user interface for more reactive and engaging experiences is important. While a good computer scientist could get the same result given time and hardware, the user now expects those results with just a small gesture of the mouse. Moving partial indexes to the client is one coming change that lines up with client based rendering.
This all points to a underlying shift that most people don't talk about directly. What do you invest in? It wasn't long ago that millions would go into the back end to produce a table and maybe a graph or two. Now that money is going into making the user experience better. You can see shifts in the valuation of different intellectual property and investments.

Source: HOB