Leading Experts Share 2020 Trends and Insights on Enterprise Analytics, AI, Machine Learning, Deep Learning, Digital Transformation & More
MicroStrategy Incorporated, a leading worldwide provider of enterprise analytics and mobility software, revealed its 10 Enterprise Analytics Trends to Watch in 2020 report during Solutions Review’s BI Insight Jam event a day dedicated to raising awareness around best practices when evaluating, deploying, and using business intelligence software.
In collaboration with leading analysts and influencers from Forrester, IDC, Constellation Research, Ventana Research and others, the annual MicroStrategy compilation highlights trends and insights that range from AI and mobile intelligence, to the explosion of data and data sources, to some very human factors including a predicted shortage of data and analytics talent.
“We’re thrilled to unveil our annual report on the top enterprise analytic trends to watch in 2020. We see a growing opportunity for decision makers to take advantage of the latest trends and advances in enterprise analytics, AI, ML, deep learning, and more,” said Vijay Anand, Vice President, Product Marketing, MicroStrategy Incorporated. “By collaborating with some of the world’s leading experts in the field, the report aims to drive an informed conversation for those leaders seeking disruptive technologies to leverage data, drive greater efficiencies and ROI, and beat the competition.”
The 10 enterprise analytics trends to watch in 2020 include the following:
1. Deep Learning Delivers a Competitive Advantage
“In 2020, the spotlight on deep learning will be the nexus between knowing and doing. No longer just a buzzword, the pragmatic advent of deep learning to predict and understand human behavior is a tempest disruptor in how companies will perform with intelligence against their competitors.” – Frank J. Bernhard, Chief Data Officer and Author, “SHAPE—Digital Strategy by Data and Analytics”
2. AutoML Improves the ROI of Data Science Initiatives
“Machine learning is one of the fastest-evolving technologies in recent years, and the demand for development in machine learning has increased exponentially. This rapid growth of machine learning solutions has created a demand for ready-to-use machine learning models that can be used easily and without expert knowledge.” – Marcus Borba, Founder and Principal Consultant, Borba Consulting
3. The Semantic Graph Becomes Paramount to Delivering Business Value
“The semantic graph will become the backbone supporting data and analytics over a constantly changing data landscape. Organizations not using a semantic graph are at risk of seeing the ROI for analytics plummet due to growing complexity and resulting organizational costs.” – Roxane Edjlali, Senior Director, Product Management, MicroStrategy and former Gartner analyst
4. Human Insight Becomes Even More Important as Data Volumes Increase
“As more and more knowledge workers become comfortable working with data, they should also become conversant with data ethnography, or the study of what the data relates to, the context in which it was collected, and the understanding that data alone might not give them a complete picture.” – Chandana Gopal, Research Director, IDC
5. Next-Gen Embedded Analytics Speeds Time to Insights
“Concise analytics delivered in the context of specific applications and interfaces speed decision making. This style of embedding and the curation of concise, in-context analytics can take more time, but with advances including no-code and low-code development methods, we’re seeing rising adoption of next-generation embedding.” – Doug Henschen, VP and Principal Analyst, Constellation Research
6. The Need to Combine Data Sources Continues to Grow
“We expect to see a continued focus on data diversity. Organizations rarely have a single standard platform for their data and analytics and multiple tools are used to access the data. The need to combine these data sources will only continue to grow.” – David Menninger, SVP and Research Director, Ventana Research
7. Data-driven Upskilling Becomes an Enterprise Requirement
“Enterprise organizations will need to focus their attention not just on recruiting efforts for top analytics talent, but also on education, reskilling, and upskilling for current employees as the need for data-driven decision making increases—and the shortage of talent grows.” – Hugh Owen, Executive Vice President, Worldwide Education, MicroStrategy
8. AI Is Real and Ready
“Next year, more of these confident CDAOs and CIOs will see to it that data science teams have what they need in terms of data so that they can spend 70%, 80%, or 90% of their time actually modeling for AI use cases.” – Srividya Sridharan, Mike Gualteri, J.P. Gownder, Craig Le Clair, Ian Jacobs, Andrew Hogan, Predictions 2020: Artificial Intelligence—It’s Time to Turn the Artificial Into Reality (Checks), Forrester, October 30, 2019.
9. Mobile Intelligence Evolves for 2020 and Beyond
“Half of organizations will re-examine their use of mobile devices and conclude that their technology does not adequately address the needs of their workers, leading them to examine a new generation of mobile applications that enable a better work experience and far more effective connectivity to the rest of the organization and to customers.” – Mark Smith, CEO and Chief Research Officer, Ventana Research
10. The Future of Experience Management Is Powered by AI
“As apps get decomposed by business process to headless microservices, automation and intelligence will play a big role in creating mass personalization and mass efficiencies at scale. The Intelligent Enterprise will take context and data to power next best actions.” – R “Ray” Wang, Founder and Principal Analyst, Constellation Research
Copper scrap import regulations Recycling equipment for Copper scrap Scrap yard services
Copper cable recycling news, Metal scrap supply chain, Copper scrap handling
Copper scrap authenticity verification Copper scrap transportation logistics Scrap metal reclaiming and reuse
Copper cable reuse options, Scrap metal brokering, Copper scrap volume estimation