Software, Data and Algorithms: The currency of Digital Transformation, by 2020:
- 10% of enterprise applications spending will be for new task-level applications that incorporate software, data and algorithms.
- 66% of enterprises will implement advanced classification solution 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 a extra $430 billion in productivity benefits over their less-analytically-oriented peers.
Content Analytics and Information Handling: Key to Organizational Success
- Most of this content is stored in dozens, if not hundreds, of individual silos.
- Content analytics and information-handling technologies are not created equal – especially open source technologies.
- In 2018, according to IDC’s, Global DataSphere model, of the 29 zetabytes of data creation worldwide, 88% is unstructured content.
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 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 and Information-Driven Organization
To facilitate digital transformation, organizations should adopt these best practices:
- Create a strategy to tie structured and unstructured data sources together.
- Develop and promote 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 and Machine Learning Provide Actionable Insights to Enable Intelligent Automation and Decision Making
Key technology and process considerations:
- AI and 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.
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.