The Four Pillars of Cognitive Automation: A Guide for Enterprises

By
Taylor Wills
The Four Pillars of Cognitive Automation: A Guide for Enterprises

Cognitive automation is a proven platform to achieve agility at scale for global businesses and supply chains, here's how it works

Cognitive automation is not simply about introducing a new platform type into your enterprise. It’s about getting a machine that establishes a better balance of what people are doing and detecting the areas where they bring real value. And to make this happen, cognitive automation systems rely on sophisticated data collection and analysis algorithms that people use to help them augment and automate their decision making.

Cognitive automation requires four foundational pillars to work: data, science, process, and engagement. Precisely, the platform relies on the right data, applies data science to create recommendations, links them to the decision-making context, and presents them in an engaging and actionable manner.  

Data: Data is the heart of the cognitive automation platform. The collection process starts by getting information from various sources in real-time, including logistics, inventory, financing, planning, sourcing, orders, sales, and demand. It then incorporates external and physical data, such as weather information, commodity pricing, and IoT sensor data. All the enterprise information becomes stored in one place, easily managed, updated, and actionable.

But it’s more than just an aggregation and harmonization of data. It also holds a permanent memory of all the decisions made on the platform, along with the context and results of those decisions. The cognitive automation system uses this information to learn and optimize future recommendations.

Science: Making sense of the data requires an integrated data science capability as a part of the platform. Today, many data scientists are on their own island, trying to collect clean data, train their models and show their results to their internal stakeholders. By integrating data science natively in a platform, data scientists not only have access to enterprise-wide harmonized data, but also make their trained models and AI algorithms easily available to integrate them within the decision process context.


Process: This pillar enables incorporating insights, recommendations, and data into the context of decision-making processes. You get a comprehensive view carefully collected from numerous data channels and easy to act upon. Not only do you have a comprehensive view of the health of your business, you have real-time, actionable recommendations generated by the platform. Once decisions are made, the changes are automatically implemented in the underlying systems.

Engagement: The interactive nature of a cognitive automation platform aims at creating a personalized experience for everyone. The system simplifies complex solutions to one click, revealing the sophisticated logic behind each recommendation on request. This engagement speeds adoption, accelerating the time-to-value of leveraging a cognitive automation platform.

A cognitive automation system requires an integrated platform to truly augment and automate decision making. And the data, science, process, and engagement elements provide all the needed capabilities to make this system work. It really is the only way to introduce high-quality decision making at scale in your enterprise.


By
Taylor Wills
,
Staff Writer
Published:
December 8, 2021
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The Four Pillars of Cognitive Automation: A Guide for Enterprises

Cognitive automation is a proven platform to achieve agility at scale for global businesses and supply chains, here's how it works

Cognitive automation is not simply about introducing a new platform type into your enterprise. It’s about getting a machine that establishes a better balance of what people are doing and detecting the areas where they bring real value. And to make this happen, cognitive automation systems rely on sophisticated data collection and analysis algorithms that people use to help them augment and automate their decision making.

Cognitive automation requires four foundational pillars to work: data, science, process, and engagement. Precisely, the platform relies on the right data, applies data science to create recommendations, links them to the decision-making context, and presents them in an engaging and actionable manner.  

Data: Data is the heart of the cognitive automation platform. The collection process starts by getting information from various sources in real-time, including logistics, inventory, financing, planning, sourcing, orders, sales, and demand. It then incorporates external and physical data, such as weather information, commodity pricing, and IoT sensor data. All the enterprise information becomes stored in one place, easily managed, updated, and actionable.

But it’s more than just an aggregation and harmonization of data. It also holds a permanent memory of all the decisions made on the platform, along with the context and results of those decisions. The cognitive automation system uses this information to learn and optimize future recommendations.

Science: Making sense of the data requires an integrated data science capability as a part of the platform. Today, many data scientists are on their own island, trying to collect clean data, train their models and show their results to their internal stakeholders. By integrating data science natively in a platform, data scientists not only have access to enterprise-wide harmonized data, but also make their trained models and AI algorithms easily available to integrate them within the decision process context.


Process: This pillar enables incorporating insights, recommendations, and data into the context of decision-making processes. You get a comprehensive view carefully collected from numerous data channels and easy to act upon. Not only do you have a comprehensive view of the health of your business, you have real-time, actionable recommendations generated by the platform. Once decisions are made, the changes are automatically implemented in the underlying systems.

Engagement: The interactive nature of a cognitive automation platform aims at creating a personalized experience for everyone. The system simplifies complex solutions to one click, revealing the sophisticated logic behind each recommendation on request. This engagement speeds adoption, accelerating the time-to-value of leveraging a cognitive automation platform.

A cognitive automation system requires an integrated platform to truly augment and automate decision making. And the data, science, process, and engagement elements provide all the needed capabilities to make this system work. It really is the only way to introduce high-quality decision making at scale in your enterprise.


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