For every new device launched, technology takes the world a step further into the future. According to Statista, there are over twenty billion interconnected devices that run on more than one software at a time. This provides enough context for the famous ‘software is eating the world’ maxim in today’s tech space. So, exactly which software can you throw your weight around to carry you into the future? Here are three modern software and what they mean for the future.
1. Data Fabric
Gradually, the world as we know it is getting a hold of the fourth industrial era. As innovations like the Internet of things (IoT) increase in adoption, the modern world’s reliance on data cannot be ignored. About 2.5 quintillion bytes of data are created per day. And this number is far from a downward trend if the increasing global mobile device proliferation rate is anything to go by.
So, how can businesses leverage these vast amounts of data for operational efficiency in the future? Well, the future of data use is dependent on real-time access and convenient use case benefits. However, the challenge of an enterprise’s multiple sources, formats and storage networks can hinder the smooth realization of this future.
By using data fabric software, businesses can ensure frictionless access and data sharing experience for an enterprise’s users. Data fabric can be a one-stop shop for a company’s data architecture, data integration, and shared data. The benefit of a data fabric solution is in its power to standardize data management across cloud, on-premises, and edge devices. Data fabric trumps the limitations of space, data locations, and all the other inhibiting technicalities of managing a company’s data sets.
With convenient access to an enterprise’s data, businesses can access essential insights and analytics in real-time to streamline workflows and inform. Data fabric systems can also help organizations understand new customers better and tailor their services accordingly. Such customer intelligence is critical for businesses to stay relevant and profitable in the future.
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2. Machine Learning (ML)
Machine learning is a branch of AI which makes it possible for software to learn from data, map out patterns and make decisions automatically. ML software is pushing the adoption of innovations like predictive analytics to new heights in the business world. Early adopters of ML technologies have enjoyed significant levels of competitive advantage. Today, the global ML industry has a value of about eight billion dollars growing at an estimated CAGR of about 44% each year.
Considering the increasing growth of ML, it’s hard to imagine a future without machine learning. There are many ML use cases applicable to everyday life. Machine learning software is the mainstay of the rapidly growing autonomous industry. It even influences how we shop. Today, an online grocery store can leverage machine learning software to tailor product recommendations so that new customers can find what they want faster.
Machine learning algorithms have also become a go-to for workforce collaboration platforms to make communication between individuals and teams a seamless experience. Experts predict the global gig economy to grow exponentially due to the new realities of the COVID-19 pandemic.
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3. Data Virtualization
Data remains one of the critical areas for business growth. However, leveraging data for efficiency gains is easier said than done. Today’s big data world has several challenges, including accuracy, integrity, and security. It takes resilient data systems to arrive at data results devoid of all the technical distractions for data users.
Data virtualization (DV) presents a big-picture understanding of data’s significance in our world today. The DV process involves the use of virtual machines, which can help businesses bypass the inefficiencies of on-site physical servers. Beyond this convenience, businesses with virtualized data environments can detect challenges in their processes in real-time. This can save businesses the time and money expended in restarting their processes.
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