Information management is an organization-wide program to control information across people, processes, and technology.

Data management is a subset of information management that refers to treating data as valuable through its lifecycle. More specifically, data management covers acquiring, validating, storing, securing, backing up, and processing data.

By expertly handling information management and data management, data generated across an organization can be utilized to inform business decisions, gain insights into customer and market behavior, and ultimately create a competitive advantage. 

Unstructured data is managed through enterprise content management systems to make it structured, accessible, and meaningful. It is estimated that 80% of data that passes through an organization is unstructured. Since managing unstructured data is a challenge, companies may only consider or properly manage 20% of their data.

Unstructured data examples include items such as documents, social media feeds, pictures, videos, phone calls, messaging, and more.

By implementing tools and strategies to effectively manage unstructured data, companies can process unstructured data in the cloud or on-premise, append it to the appropriate system of record, and have it available for future use and analysis.

This allows them to make better-informed decisions and gain considerable competitive advantages.

Data Management is a set of disciplies that include data governance, data architecture, data modelling, data storage, data security, data integration, data warehousing, business intelligence, data quality, and more.

Companies need to have strategies and tools to perform each type of data management in order to have a holistic data approach and gain true insights into their operations, customers, and competitive situation.

Structured data is formatted for further processing through the Big Data pipeline. Unstructured data lacks organization and formatting.

Structured data makes up only about 20% of data generated by organizations. It is data that is codified in a specific format, generated in a way that can be easily stored and referenced in the future.

Unstructured data makes up about 80% of data generated by organizations. Unstructured data examples include items such as documents, social media feeds, pictures, videos, phone calls, messaging, and more.

While companies may be mature in their handling and analysis of their structured data, they are likely only seeing a subset of the complete picture. Therefore, having systems to manage both structured data and unstructured data are necessary in order to achieve optimal business intelligence.

Analyzing unstructured data requires advanced extraction tools and technical expertise. Given that the majority of data is unstructured, being able to handle it offers a competitive advantage.

In general, the extraction process refers to gathering points from unstructured data and cataloging them in a structured manner. To determine what points are gathered and how they're cataloged, organizations must first determine the goal of the analysis. 

For example, if a company wants to catalog social media messages by sentiment so that appropriate responses can be made, they may use a machine learning model to categorize messages as positive, neutral, or negative. Based on this, each message can be analyzed and actioned-upon as appropriate.

Intelligent automation is the automation of enterprise business processes with the support of analytics, data, machine learning, and artificial intelligence.

Intelligent automation is commonly referred to as "digital transformation" and encompasses the orchestration of activities across people, tasks, systems, and robots.

These activities are supported by analytics and machine learning to derive process improvements, dynamic handling, and better decision making.

Intelligent automation includes four primary fields: Business Process Management, Robotic Process Automation, Artificial Intelligence, and Integrations.

Intelligent automation automates all manual steps in a business process.

Automation can be broken down into hard automation (fixed sequential steps), programmable automation (flexible steps with prebuilt logic), and soft automation (adaptable steps with greater tolerance for real-world non-conformities).

These three types of automation can extend across manufacturing, business processes, and operations. Each are supported by software engineering and can be enhanced by machine learning.

Enterprise intelligent automation is the use of artificial intelligence and robotic process automation to automate manual steps across an enterprise's entire set of business processes. 

Human-centric and legacy business processes can be automated by both software and hardware-based robotic process automation. 

Enterprise intelligent automation takes automation a step further by also bridging activities across organizations and traditionally siloed teams. 

This leads to end-to-end efficiency, quality, and business intelligence.

The main purpose of intelligent automation is to orchestrate end-to-end automation in business processes.