{"id":5038,"date":"2020-01-09T17:17:27","date_gmt":"2020-01-09T22:17:27","guid":{"rendered":"https:\/\/solutionsreview.com\/business-intelligence\/?p=5038"},"modified":"2020-01-10T13:46:40","modified_gmt":"2020-01-10T18:46:40","slug":"13-analytics-and-ai-predictions-from-12-experts-for-2020","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/business-intelligence\/13-analytics-and-ai-predictions-from-12-experts-for-2020\/","title":{"rendered":"13 Analytics and AI Predictions from 12 Experts for 2020"},"content":{"rendered":"

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We polled 12 experts and received 13 analytics and AI predictions for 2020.<\/strong><\/em><\/p>\n

As part of the first annual Solutions Review #BIInsightJam<\/a>, we called for the industry’s best and brightest to share their analytics and AI predictions for 2020. The experts featured here represent the top BI and data science solution providers with experience in this niche. Analytics and AI predictions have been vetted for relevance and ability to add business value as well. These are the 13 best predictions from the dozens we received. We believe these are actionable and may impact a number of verticals, regions and organization sizes.<\/p>\n

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Haoyuan Li, Founder and CTO at Alluxio<\/strong><\/h3>\n

\"HaoyuanAI and analytics teams will merge into one as the new foundation of the data organization<\/strong><\/em><\/h4>\n

“Yesterday\u2019s Hadoop platform teams are today\u2019s AI and analytics teams. Over time, a multitude of ways to get insights on data have emerged. AI is the next step to structured data analytics. What used to be statistical models has converged with computer science to become AI and ML. So data, analytics, and AI teams need to collaborate to derive value from the same data they all use. And this will be done by building the right data stack – storage silos and computes, deployed on-prem, in the cloud, or in both, will be the norm. In 2020 we\u2019ll see more organizations building dedicated teams around this data stack.”<\/em><\/p>\n

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Raju Vegesna, Chief Evangelist at Zoho<\/strong><\/h3>\n

\"RajuAI data cleansing will become key<\/strong><\/em><\/h4>\n

“I expect to see a lot more places where AI data cleansing gets implemented. Smaller organizations will begin to expect AI functionality in things like spreadsheets, where they’ll be able to parse information out of addresses or clean up inconsistencies. Larger organizations will benefit from AI that makes their data more consumable for analytics or preps it for migration from one application to another.”<\/em><\/p>\n

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Dean Guida, CEO and Founder at Infragistics<\/strong><\/h3>\n

\"DeanCreating an amazing embedded [analytics] experience will remain difficult<\/strong><\/em><\/h4>\n

\u201cMost embedded BI and analytics vendors started out building a web or desktop-based BI dashboard tool. In time, many of these vendors decided to create an embedded option, enabling customers to take the app experience and deliver it in their own apps. Keep this in mind when talking to your embedded analytics vendor: Was the Solution purpose-built for embedded? Was the embedded experience an afterthought, or was it designed from the beginning? Does the embedded user get the full app experience? Can the user go beyond simply viewing dashboards, and be able to edit existing dashboards and add new ones as well? Do you see limitations in the embedded product when compared with the SaaS or desktop offering?\u201d<\/em><\/p>\n

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Eric Raab, SVP Product and Engineering at Information Builders<\/strong><\/h3>\n

\"EricVisualization is no longer enough<\/strong><\/em><\/h4>\n

“It\u2019s relatively easy to take data inputs and create a pretty visual output. That picture may be worth a thousand words, but businesses don\u2019t want or need a thousand words, they need the 20 that give them clear insights and a path of action to creating more value. That requires a data and analytics solution that leverages AI to control and manage the entire data value chain from connection and mastering to sampling and analyzing to application-building and distribution. Organizations are already starting to see the difference and that will only continue in the coming year.”<\/em><\/p>\n

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Will Hayes, President and CEO at Lucidworks<\/strong><\/h3>\n

\"WillEnterprise search as a use case is a relatively dead market<\/em><\/strong><\/h4>\n

“While organizations are still looking to leverage the mountains of data they have created over the years and make information more accessible to their employees, the idea that a vendor can simply provide a connector and a search bar and think that this is something that enterprises will value, let alone invest in, is long gone.”<\/em><\/p>\n

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Seth Elliot, Chief Marketing Officer at Gtmhub<\/strong><\/h3>\n

\"SethEnterprises will cool it on costly BI integrations and acquisitions<\/strong><\/em><\/h4>\n

“Though 2019 brought big name BI acquisitions, reality seems to be that for the most part business intelligence tools aren\u2019t delivering actionable insights for driving a business. Their design and integration aren\u2019t very seamless and they often rely on a combination of technologies to collect data and pinpoint impactful next steps for what that data actually indicates within an organization. To combat this, organizations will likely start to look for enterprise platforms that integrate existing tools, remove functional silos, and gather and analyze data all in one place.”<\/em><\/p>\n

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Ravi Shankar, Senior Vice President and Chief Marketing Officer at Denodo<\/strong><\/h3>\n

\"RaviConversational analytics will take center stage<\/strong><\/em><\/h4>\n

“2020 will usher in the practice of voice-support platforms using NLP and AI-based conversational analytics to help organizations to improve personalization and targeting through deeper consumer insights. In the coming year, conversation analytics will be a technology that transliterates voice interaction through natural language query and converts it into data. This data is structured so that conversations can be analyzed for insights. With machine-learning trained systems, conversational analytics will continue to help organizations to improve their chatbots and voice applications in 2020 resulting in better data-driven decisions and improved business performance.”<\/em><\/p>\n

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Joshua Poduska, Chief Data Scientist at Domino Data Lab<\/strong><\/h3>\n

\"JoshuaConsolidation of power and platforms will accelerate in 2020<\/strong><\/em><\/h4>\n

“The explosion of AI efforts will be accompanied by a growing trend toward consolidation of data science organizational power. The idea of setting up an internal data science practice is not new, and most companies have already invested here. With access to better centralized platforms, data scientists will be significantly more productive, but business leaders will be slower to define and enforce the processes needed to ensure that work gets successfully into production to improve decision making. While the particular implementation details will vary, this trend of consolidation of power and platforms will also accelerate in 2020.”<\/em><\/p>\n

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Amandeep Khurana, President and CTO at Okera<\/strong><\/h3>\n

\"AmandeepConsolidation of analytics and machine learning companies<\/strong><\/em><\/h4>\n

“We\u2019re more than a decade into a massive transformation in the world of data platforms, analytics, and machine learning, and some winners are emerging. This year, we\u2019ll see a consolidation of companies and technologies via acquisitions as well as the merger of projects and initiatives. It\u2019ll be the beginning of the trend of consolidation that will likely accelerate into 2021.”<\/em><\/p>\n

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Peter Guagenti, Chief Marketing Officer at MemSQL<\/strong><\/h3>\n

\"PeterBI tools go far and wide<\/strong><\/em><\/h4>\n

“The march toward being a data-driven enterprise has become a self-fulfilling prophecy, and in 2020 it will mean more than \u201cconsume this report.\u201d We now have a generation of young business professionals who are used to asking questions and getting immediate answers. This dynamic has resulted in increased pressure on IT to make it easier and faster to get answers to questions, and that pressure is only going to multiply in 2020.”<\/em><\/p>\n

AI and ML go big \u2013 for real this time<\/strong><\/em><\/h4>\n

“We\u2019re going to start seeing some dramatic breakthroughs and some real transformative changes in 2020. To an extent not seen to date, AI and ML will, so to speak, emerge from the lab and infiltrate your life. A recession, if there is one in 2020, will accelerate this coming AI\/ML impact. That\u2019s because a lot of the efforts that AI and ML are focused on relate to automation and building efficiencies in the way humans work.”<\/em><\/p>\n

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Roman Stanek, Founder and CEO at GoodData<\/strong><\/h3>\n

\"RomanInsightful data analytics will remain elusive for companies if they don’t overcome the advanced data analytics challenges that impact business outcomes<\/strong><\/em><\/h4>\n

“Lack of alignment and collaboration within companies will persist. Data analytics and marketing teams still don’t collaborate well and often have different goals. BI is still largely reserved for data analysts, but for data analytics to reach its full, transformative potential, almost everyone in an enterprise needs access to data analytics. Most tools on the market today are still too complex and designed for expert users. In many companies, valuable data is still stuck in silos so different departments don’t even know what the others have, making good insights a big challenge.”<\/em><\/p>\n

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Helena Schwenk, Global AR and Market Intelligence Lead at Exasol<\/strong><\/h3>\n

\"HelenaIn 2020 we will see the demand for data satisfied by a growing availability of synthetic datasets<\/strong><\/em><\/h4>\n

“This trend will enable smaller organizations to make meaningful strides in their AI journey. Synthetic data is generated programmatically. For example, realistic images of objects in arbitrary scenes rendered using video game engines or audio generated by a speech synthesis model from known text. Common strategies for synthetic data usage include taking observations from real statistic distributions and reproducing fake data, and models that are created to explain observed behavior. This strategy is strong when trying to understand interactions between agents that are had on the system as a whole.”<\/em><\/p>\n

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MORE: Five Can’t-Miss Analytics and Business Intelligence Trends for 2020<\/span><\/strong><\/a><\/h3>\n