The advantage of becoming Information-Driven

The Benefits of Becoming
Information Driven Using
AI & Machine Learning

Software, Data, & Algorithms: The Currency of Digital Transformation

10%
of enterprise applications spending will be for new task-level applications that incorporate software, data, and algorithms

Cognitive Search & Analytics to Accelerate Innovation

BY 2020
66%
of enterprises will implement advanced classification solutions to automate access, retention, and disposition of unstructured content, making it more useful for analytics
Organizations able to analyze all relevant data and deliver actionable information will achieve an extra
$430 BILLION
in productivity benefits over their less-analytically-oriented peers
(source IDC)

Content Analytics & Information Handling:
Key to Organizational Success

Most of this content is stored in dozens, if not hundreds, of individual silos

In 2018, according to IDC’s Global DataSphere model, of the 29 zettabytes of data creation worldwide, 88% is unstructured content

Content analytics and information-handling technologies are not created equal—especially open source technologies

IT focus on structured data issues

Handling Information Challenges with Ai and Machine Learning Top Information-Handling Challenges:

  • IT focus on structured data issues, not unstructured data (where most of the value lies)
  • 86% of organizations expect to adjust their data strategy (evaluate implications of data management, data availability, data access control, and quality) to make effective use of AI and machine learning technologies
  • Siloed data models and applications
  • Fragmented processes and development approaches to handling unstructured information

Becoming an Information-Driven Organization

To facilitate digital transformation, organizations should adopt these best practices:

Extract values from all data sources, structured and unstructured
  • Create a strategy to tie structured and unstructured data sources together
  • Develop and promote an organizational culture that treats information as a key asset
  • Use advanced technologies such as text analytics, auto-categorization, auto-tagging, etc., to identify, facilitate, and extract value from data sources
  • Create a single, unified index to provide secure access to all information within the organization

AI & Machine Learning Provide
Actionable Insights to Enable Intelligent
Automation and Decision Making

Key technology and process considerations:

Key technoligies with AI and Machine Learning
  • AI & machine learning can glean insights from unstructured data and help “connect the dots” between previously unconnected data points, surfacing relationships explicitly
  • Actionable information must be presented in context to surface insights, inform decisions, and elevate productivity with an easy-to-use application
  • Look for information-handling technologies that can be used in large scale deployments for complex, heterogeneous, and data-sensitive environments
  • Enrich content automatically and at scale
  • Continuously improve relevancy over time based on user actions driven by machine learning
  • Improve understanding by intelligently analyzing unstructured content
Become Information-Driven with Automate Decision-Making

The Bottom Line

Enabled by machine learning–based automation, there will be a massive change in the way data and content is managed and analyzed to provide advisory services and support, as well as automate decision-making across the enterprise.

Using information-driven technologies and processes, the scope of knowledge work, advisory services, and decisions that will be automated will expand exponentially based on intelligent systems.

IDC - Analyze the future
All IDC research is © 2018 by IDC. All rights reserved. All IDC materials are licensed with IDC’s permission and in no way does the use or publication of IDC research indicate IDC’s endorsement of Sinequa’s products/or strategies.

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