We have finally published our last data and analytics related hype cycle, thus completing the entire set for this year. Here they all are in their glory:
All organizations are challenged in getting more value from their data and maximizing its role in driving better business outcomes. This Hype Cycle tracks the practices and disciplines of EIM and serves as a guide for CIOs and chief data officers leading data and analytics programs.
Information governance and MDM are technology-enabled business disciplines that enable tailored application of decision frameworks to achieve a “trusted version” of business-critical data such as master data, transactional data and other data types that vary by industry, organization and use case.
The hype around data science and machine learning has increased from already high levels in the past year. Data and analytics leaders should use this Hype Cycle to understand technologies generating excitement and inflated expectations, as well as significant movements in adoption and maturity.
Back-office analytic applications are the first touchpoints for analytics and artificial intelligence for many business users. This Hype Cycle helps data and analytics leaders in analytics teams and business domains understand where they can leverage these powerful technologies for business benefit.
This Hype Cycle helps CIOs and data and analytics leaders understand the evolutionary pace of maturing and emerging data management technologies and challenges. It will create awareness of novel technologies as well as the opportunities and risks in the nascent areas.
Improved customer experience from real-time, contextualized interactions supported by AI and machine learning is a top trend. This year’s Hype Cycle will help data and analytics leaders prioritize investments through the maturity, adoption rates and benefits of customer experience analytics.
This Hype Cycle will help data and analytics leaders modernize their analytics and BI programs. The main story that emerges from it concerns the upcoming transition from visual data discovery to augmented analytics using machine learning and natural-language interfaces to augment human intelligence.
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