Data and analytics have gained traction in organizations driven by the promise of big data a few years ago and the potential of machine learning and other types of artificial intelligence more recently. Even as many enterprises seemed to be stalled in their production AI plans they are still making those plans and know they are crucial for success in the years to come.
1. Augmented analytics Across analytics, business intelligence, data science, and machine learning, organizations will leverage augmented analytics to enable more people to gain insights from data. Sallam said that augmented analytics will become the dominant thing that organizations look at when they are assessing vendor selections over the next few years. Also, vendors of other technologies like Salesforce and Workday are incorporating augmented analytics into their products and services to improve the experience for users. "It's really about democratizing analytics," Sallam said. "…It is really about getting insight in a fraction of the time with less skill than is possible today."
2. Augmented data management This trend will improve organizations' ability to analyze data that is coming in more dynamically and with greater levels of automation in closer to real time. There are many different tasks that come with the data management side of the operation such as schema recognition, capacity, utilization, regulatory/compliance, and cost models, among others. Augmented data management will target those pieces. Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management, Sallam said.
3. NLP (natural language processing)/conversational analytics NLP and conversational analytics are highly complementary with augmented analytics. They provide non-data experts with a new kind of interface into queries and insights. "Most people don't know SQL, and they can't build their own queries themselves," said Sallam. "These tools have made it easier." By 2020, 50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated, according to Gartner. Still, there is also plenty of room for improvement. Today most analytics and BI platforms have implemented basic keyword search. For instance you can ask 'What were my sales by product?' Sallam said. But more complex questions are still a challenge. You probably won't be able to ask 'What were my top 10 products or customers within a 50-mile radius of New York this year versus last year.' "That's more complex," Sallam said, and it involves ranking functions and synonyms and other functions that not every vendor can do today. Another emerging feature in this area is conversational analytics, which will let you drill down with more specific questions. "Until recently, it's all been about visualization," Sallam said. Conversational analytics will add another dimension to the insights.
4. Graph Graph processing and graph databases enable data exploration in the way that most people think, revealing relationships between logical concepts and entities such as organizations, people, and transactions, Sallam said. Gartner predicts that the application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science. Graph enables emergent semantic graphs and knowledge networks, Sallam said. One example might be an emergent linking of diverse data such the data from exercise apps and diet apps with medical advice and health news feeds.
5. Commercial AI or ML will dominate the market over open source Open source has been a big driver of big data and AI and machine learning, particularly at digital giant companies such as Google and Amazon. But most organizations don't fit into the digital giant category. These companies have run AI and ML pilots, but have been struggling to scale their projects to production. Gartner believes these companies will ultimately leverage commercial platforms to manage their AI programs. Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercial, instead of open source, platforms.
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