"Big Data" has fast moved from being a buzzword to being used and understood as a business asset. An asset, when linked with effective management and rigorous data analysis, drives informed decision-making, increases business efficiency and identifies new business opportunities.
The continued management and analysis of Big Data is proving to be a significant differentiator in large organisations. We are now seeing the rise and massive potential of advanced data technologies to support the staggering growth of data that's being generated worldwide.
Below are 5 trends in Big Data for now and beyond:
1. Automated data analysis
As data analysis is further built into business processes, it has been found that automating the analysis speeds up decision-making and reduces the response time to opportunities. The International Data Corporation (IDC) predicts that by 2025, about 30% of all data will be streamed in real-time, compared to the 15% that was streamed in 2017.
2. Natural Language Analytics (NLA)
A critical element to making data-driven decisions is making the data available to be used by all decision-makers; NLA enables this. NLA allows computers to understand human language. Doing so empowers non-technical business users to query complex data using words and phrases (via voice or text). Gartner has predicted that by the end of 2020, 50% of data queries will be processed using natural language processing technology, instilling a greatly enhanced data-driven culture in organisations.
3. Edge Computing
By 2025, more than 150 billion devices will be connected across the globe, and these devices are predicted to generate 90 zegabytes of data. As this massive data growth creates data management and analysis challenges, new technologies and computing processes naturally follow. In comes Edge Computing - a distributed computing model where data is processed close to the area where data is generated, instead of a centralised server or the cloud. This innovative infrastructure uses sensors to collect data and edge servers to securely process data in real-time and on location, while also connecting other devices to the network, such as laptops and smartphones. The most significant value of edge computing is in reducing the costs that come with latency and network traffic. Gartner predicts that by 2022, approximately 75% of all data will be analysed and actioned at the edge.
4. Data-as-a-Service (DaaS)
More and more, businesses are providing data access and digital files via DaaS, both internally and commercially. DaaS is defined as "a data management strategy that uses the cloud to deliver data storage, integration, processes and/or analytics services via a network connection". It is predicted that large organisations, beyond 2020, will bundle data with business intelligence tools to boost revenue. This is possible as DaaS leverages the data ecosystem and real-time data analytics to create customised and real-time datasets - changing the game for marketers. They will further move from modelling data based on what they think a prospect might do, towards having real-time insights into the actual behaviour of a prospect.
5. Evolving from predictive to prescriptive analytics
In recent years, data has been used for predictive analysis to give insight into the possible outcome of business decisions. We are now seeing the rise of prescriptive analysis software, which suggests decision options to benefit from the predictions. Ventana Research expects that by 2021, about 67% of analytical processes will be prescriptive. For instance, prescriptive analytics give insight into whether to publish material for readers, based on search and social shares data of related topics.
Moving into the future, data will continue to be king. InfoBluePrint is excited to be a part of the revolutionary journey that will come with the proper management and analysis of high-quality Big Data.
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