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The vast technological changes prevailing in today's digital world has opened the door for big data to improve business opportunities. However, most of us are unaware of the fact that we are producing more data in two days than decades of history. Every time we click an option to search, it generates data. What can we do with so much data? Actually, a lot. Data is an asset to business entities. They can leverage big data to improve their business. The continuous use of big data will impact the way organizations perceive and use business intelligence. Besides, the introduction of artificial intelligence, machine learning, IoT and other latest technologies has upped the quality of the data-oriented solutions. To be precise, big data is making the business smarter. Henceforth, Analytics Insight brings you a list of top 10 big data trends that will accelerate smart business.
Big data that an organization holds can only be utilized in a useful way when it is analyzed by artificial intelligence and subsequent technologies. Machine learning is leveraged by businesses to parse data, learn from them and make predictions using neural networks. To further minimize the tech hurdle, businesses are shifting to cloud sources, making data accessibility easier. Hybrid cloud provides great flexibility and more data deployment options by moving the processes between private and public clouds.
Gartner defines continuous intelligence as a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events. Companies leverage a variety of facilities like optimization, business rule management, event stream processing, augmented analytics and machine learning using continuous intelligence. The technology helps businesses to mitigate customer troubles and highlight special offers designed to tempt specific customers.
Augmented analytics uses artificial intelligence and machine learning to enhance analytics across all phases of the data lifecycle, starting from the way data is prepared to how analysis is performed and insights are delivered. Augmented reality is a combination of data science and artificial intelligence that makes data analytics accessible for business to get value from data, allowing them to ask questions and automatically generate insights in an easy and conversational manner. The technology significantly reduces the time-consuming tasks in data preparation. For example, data cleaning takes up to 80% of the data scientist's valuable time. This can be drastically minimized using augmented analytics.
Businesses are quickly adopting data analytics solutions to get the maximum out of their data assets. However, the impact of data analytics can further be extended using DataOps. DataOps is an agile solution that is used to manage data. This helps businesses increase the speed and quality of data management using automated and process-oriented technologies. Self-service analytics also aids businesses to get a smooth process across the entire information value chain.
It is pretty clear that data is generated at every end. The influence of IoT devices has further accelerated the creation of data which can be used for business operations. However, the trouble here is that these data travels a long way to reach a centralised source. Fortunately, technology gave us a solution to address the increasing crisis. Edge computing allows data to be stored in the local storage device near the IoT device instead of in the cloud to manage data better.
The chatbots market is exponentially growing. Chatbots are taken as a major source of communication between business and consumers. They are being deployed by companies to handle queries and to deliver more personalized interactions while eliminating the need for actual human personnel. Remarkably, big data sits at the core of chatbots. The communications source needs to be fed with large datasets to perform on a personalized scale. Big data acts as the main source that feeds information into chatbots.
Security threat is a major setback that smart businesses face today. Big data can be of great use when it is deployed to the security strategy of a company. The big data of a company holds critical information like past cyberattack attempts, phishing attacks, ransomware, etc. This can be used to predict, prevent and mitigate future attempts.
Big data-as-a-service (BDaaS) refers to the vast amount of data that is being stored, analyzed, processed and created in cloud-based systems. BDaaS provides insights into big data that drive business growth for viable advantage. A major advantage of BDaaS in business is that it brings together data warehousing, infrastructure and platform service models under a unified platform to deliver advanced insights that will help transform the business to a smart source.
Dark data is a less spoken part of big data. Dark data represents the data that is neglected by the business to be used for analytics. These data are gathered from several network operations that are not utilized to determine insights or for prediction. Big data market needs to streamline dark data awareness as these could hold bigger value than how we think. Unfortunately, this could even pose a threat to business security. Henceforth, it is better to scrap or utilize this data for the right purpose.
When we talk about cloud, the first thing that is kept on the table is a public cloud, which is available to anyone willing to use it. Businesses have shown interest in using public clouds. Around 41% of businesses were expected to start using public cloud platforms in 2020. Besides hybrid cloud and multi-cloud strategies are gaining more attention as they handle a variety of cloud computing projects based on their project needs.
