An Introduction to Decision Automation vs Decision Management

Explore Decision Automation vs Decision Management in a quick overview and understand differences and benefits to choose the right strategy for your business.

Prabhat Gupta

15
 min read
An Introduction to Decision Automation vs Decision Management
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15
 min read
Table of Contents

Every organization operating in different devoted business and technology sectors needs the ability to make effective decisions. In this context, two terms that frequently emerge and create a dilemma are "Decision Automation vs Decision Management," each representing a distinctive approach with specialized roles in shaping and orchestrating the decision-making fabric within an organization.The dynamic business environment demands precision, efficiency, and adaptability in decision-making processes to navigate challenges and capitalize on various opportunities.

In simple terms, Decision Automation is like a quick and efficient helper for specific decisions, using technology to make things run smoothly. On the contrary, Decision Management is like the big picture thinker, looking at all aspects of decision-making in a business.

When they work together, they create a strong team that can handle the different needs of a complex business world. It's like having the right tools to make decisions better and faster.

If you want to understand both these concepts in detail and decide what can be better for your business, commence by reading this article. By the end of the article, you will be able to make better decisions and choose the best tool to enhance your business's productivity and efficiency.

What is Decision Automation?

Decision Automation involves the use of technology to streamline and execute decisions without direct human intervention. It replaces manual decision-making with automated processes, reducing the time and effort required for routine or repetitive choices. This approach relies on predefined rules, algorithms, and data to make decisions efficiently and consistently.

Imagine a scenario where a company needs to approve or deny a large number of simple requests, like processing customer returns. Decision Automation in this case could involve setting up rules that automatically determine whether a return request meets specific criteria, allowing for quick and standardized decision-making.

Key Aspects of Decision Automation

  • Technology-driven: Decision Automation relies on technological tools and systems to carry out decision-making processes.
  • Efficiency: The primary goal is to increase efficiency by automating routine decisions, allowing organizations to handle high volumes of tasks with minimal manual intervention.
  • Predefined Rules: Decision Automation operates based on predefined rules and algorithms, ensuring consistency in decision outcomes.
  • Speed: Automated decisions can be made quickly, contributing to faster response times in scenarios where timely decisions are crucial.

Understanding Decision Automation Platform with A Real World Example

Consider an eCommerce company that receives a large number of online orders daily. Decision Automation could be implemented to automatically approve orders that meet certain criteria, such as order value, customer history, and product availability.

Rules can be set to automatically flag and review orders that fall outside these criteria, streamlining the order processing workflow and reducing the need for manual order reviews.

What is Decision Management?

Decision Management is a broader concept that encompasses the entire decision-making lifecycle. It involves the strategic planning, monitoring, and optimization of decisions within an organization. Decision Management improves the quality and effectiveness of decision processes by integrating various factors, such as business rules, analytics, and human expertise.

Consider a complex business decision, like determining the pricing strategy for a product. Decision Management would involve not only automating certain aspects of this decision but also incorporating analytics to analyze market trends, assessing risks, and utilizing human input to ensure a well-rounded and informed decision-making process.

Check this table to understand types of Decision Management Software and their use cases -

Software Types

Description

Key Features

Use Case Example

Business Rule Engines

Enable organizations to define, manage, and execute business rules.

Rule authoring, real-time execution, integration with business processes.

An insurance company automates underwriting with predefined risk rules.

Predictive Analytics Software

Predicts future outcomes based on historical data utilizing statistical and ML.

Data modeling, predictive algorithms, data visualization.

An e-commerce platform forecasts demand using predictive analytics.

Machine Learning Platforms

Designed for developing and deploying machine learning models.

Data preparation, model deployment, and supervised or unsupervised learning, 

A healthcare provider assesses patient disease risk with a Machine Learning Platform.

Workflow Automation Software

Automates tasks and aligns decisions with established workflows.

Visual workflow design, task automation, integration with databases.

A financial institution streamlines loan approval and ensures compliance with WAS.

Decision Management Systems

Tailored in-house or with software firms for unique organizational needs.

Decision logic, integration with existing infrastructure, scalability and adaptability.

A government agency handles citizen benefit claims with a custom DMS.

Key Aspects of Decision Management

  • Lifecycle Approach: Decision Management addresses the entire decision-making lifecycle, including planning, execution, monitoring, and optimization.
  • Integration of Factors: It involves the integration of various elements, such as business rules, analytics, and human expertise, to enhance the quality and effectiveness of decisions.
  • Strategic Planning: Decision Management includes strategic planning to align decision-making processes with organizational goals and objectives.
  • Continuous Improvement: It emphasizes continuous monitoring and optimization of decision processes to adapt to changing conditions and improve outcomes.

Check this table to understand what a decision-making software consists of.

Key Components

What Do They Mean?

Rules Engine

Enables precise control through the definition of complex decision rules and conditions.

Data Integration

Aggregates and analyzes data from diverse sources, including databases and external repositories.

Analytics

Leverages analytical tools for processing and analyzing data, incorporating advanced analytics for informed decisions.

Workflow Automation

Ensures efficient execution of decisions, reducing manual interventions and minimizing human errors.

Scalability

Designed to handle large volumes of data and decisions, making it adaptable for organizations of varying sizes.

Adaptability

Accommodates changes in decision rules, criteria, and processes to meet evolving business needs.

Integration with Other Systems

Seamlessly integrates with enterprise systems such as CRM, ERP, and BPM, aligning decisions with broader business processes.

Audit and Compliance

Features auditing and compliance tracking functionalities, crucial for industries with stringent regulatory requirements.

Key Difference: Decision Automation vs Decision Control Management

To certain people, these two terms can be thought to be similar, but in reality, they are different and have unique frameworks and principles. Check this table to understand their primary differences and benefits.

Criteria

Decision Automation

Decision Management

Focus

Tactical implementation of technology for specific decisions

Strategic approach addressing the entire decision lifecycle

Scope

Streamlining and executing individual decisions efficiently

Encompasses strategic planning, continuous monitoring, and optimization

Method

Relies on predefined rules, algorithms, and data

Integrates various factors such as business rules, analytics, and human expertise

Benefits

Enhances efficiency, reduces manual intervention, speeds up decision-making in specific tasks

Improves overall quality and effectiveness of decisions, aligns with organizational goals, and ensures adaptability

How the Decision Management Software Works? - The Basic Understanding

Decision Management Software (DMS) operates through a systematic process that involves collecting, analyzing, and executing decisions. It all begins by gathering relevant data from various sources like databases, external providers, and customer interactions. This data is then analyzed using powerful tools like Nected to extract valuable insights.

DMS uses a rules engine to define decision rules based on historical data and business policies. These rules set the criteria and conditions influencing decision outcomes. The decision logic, evaluated against these rules, can be as simple as approving a loan application or as complex as considering factors like credit score and income.

Once the decision logic is applied, DMS executes decisions, such as approving a loan or flagging a potential fraud. It often integrates with workflow automation to ensure smooth execution of subsequent actions, like sending notifications or updating records.

Continuous monitoring of decision outcomes is crucial for DMS. This feedback loop helps refine decision rules over time. The software is designed to scale with organizational needs and seamlessly integrates with other systems.

The following diagram shows how DMS works:

To better understand how decision management works, you must grasp the concept of decision automation and its functioning. To delve deeper into this topic, read this blog.

Nected: The Best Performing Decision Management Software

Nected, an innovative Decision Automation Platform, is at the forefront of simplifying decision automation. The platform provides users with a seamless experience in designing, managing, and optimizing decision logic. With Nected’s visual decision modeling feature users can intuitively create and refine decision processes.

One of Nected's key strengths lies in its collaborative capabilities, facilitating efficient teamwork for cross-functional groups. The inclusion of collaboration tools enhances communication and coordination among team members involved in decision-making processes.

Nected operates as a cloud-based solution, emphasizing scalability and accessibility. This cloud infrastructure enables users to scale their decision automation processes according to their evolving needs. The accessibility aspect ensures that decision-makers can engage with the platform from virtually anywhere, fostering flexibility and responsiveness in managing decision logic.

Check this table to understand  Nected’s core functionalities, which make it different from other decision management tools in the market.


Core Functionalities

Availability in Nected 

Decision Management Software

Real-time Decision-Making

Business Rules Management

Policy Automation

Custom Decision Models

Language Agnostic

API-Driven Flexibility

Understanding Nected's Approach to Decision Management with A Real World Example

Nected offers a powerful set of solutions to handle different decision management scenarios. Whether it's,

  • Enterprise Decision Management, 
  • Global Decision Management, 
  • Accounting Decision Management, 
  • Credit Decision Management,
  • Customer Decision Management,
  • Automated Decision Management,
  • Business Decision Management,
  • Business Decision Management Software, or
  • Business Decision Management System.

Nected provides a unified approach that meets the specific needs of each category.

Consider a financial institution deciding on loan approvals. Decision Management in this context would involve automating certain aspects, like credit scoring based on predefined rules. Additionally, analytics could be employed to assess economic trends and risks.

Code Sample (Credit Decision Management)

import requests

# Define the Nected Decision Engine API endpoint
api_url = "https://api.nected.com/decision-engine"

# Define the Credit Decision Management rules
credit_rules = {
    "low_credit_score": {
        "condition": "credit_score < 600",
        "decision": "Reject"
    },
    "medium_credit_score": {
        "condition": "600 <= credit_score < 700",
        "decision": "Review"
    },
    "high_credit_score": {
        "condition": "credit_score >= 700",
        "decision": "Approve"
    }
}

# Applicant's Data
applicant_data = {
    "credit_score": 620,
    "income": 55000,
    "existing_debt": 15000
}

# Define the API request payload
payload = {
    "rules": credit_rules,
    "data": applicant_data
}

# Make a Credit Decision using Nected's Decision Engine API
response = requests.post(api_url, json=payload)

if response.status_code == 200:
    decision_result = response.json()["decision"]
    if decision_result == "Approve":
        print("Congratulations! Your credit application is approved.")
    elif decision_result == "Review":
        print("Your credit application is under review.")
    else:
        print("Sorry, your credit application is rejected.")
else:
    print("Error:", response.status_code)

Nected relies on its flexible decision engine, which can handle various decision logic, rule sets, and data sources. This engine utilizes advanced algorithms, machine learning, and data analytics to analyze information and quickly produce actionable decisions in real-time.

Serving as the core hub for decision management, it coordinates decision-making processes across different business functions. In Credit Decision Management, for example, Nected's decision engine evaluates credit applications by analyzing credit scores, financial histories, and risk factors. It automates decision-making, delivering instant approvals or suggesting further review, all while ensuring regulatory compliance.

To demonstrate Nected's methodology, here is an elaborate diagram showcasing how Nected seamlessly integrates into the decision management landscape. The diagram illustrates the connection between data sources, rule sets, and decision logic, working together to facilitate accurate and efficient decision-making.

The diagram showcases how the Decision Engine API seamlessly orchestrates integration, facilitating real-time decision-making and customization. For credit decisions, a credit card approval engine looks at a buyer's income, credit history, and other factors to decide if they qualify for credit.

As seen above, integrating APIs and third-party data sources with Nected.ai is effortless, allowing you to leverage the rule engine for financial domains.

Creating new data sources and finding connectors is user-friendly and doesn't demand extensive development expertise. In the image below the "data sources" section, you can simply click on "+ create data source" to locate the desired connector.

Nected's Rule Engine empowers businesses to access advanced functionalities seamlessly, eliminating the complexity of intricate issues. Moreover, it constantly evolves, moving towards becoming an autonomous decision-maker once implemented and configured.

Which is Better? - Decision Automation vs Decision Management

The decision between opting for Decision Automation or Decision Management hinges on the particular needs and objectives of an organization. 

If your organization frequently encounters decisions that follow a set pattern or are repetitive in nature, Decision Automation is a suitable choice. This approach excels at handling tasks that can be standardized, allowing for quick and consistent outcomes.

Decision Management is ideal if your organization places a high value on strategic planning, continuous monitoring, and ongoing optimization of decision processes. It ensures that decision-making aligns with long-term organizational goals and allows for adaptability to changing conditions.

Decision Management Vs Decision Control Automation? - It’s Time to Decide

The decision between using Decision Automation or Decision Management isn't simple. Decision Automation is great for quickly handling routine decisions using technology and predefined rules. On the flip side, Decision Management takes a big-picture view, considering the entire decision process and incorporating various factors like business rules, analytics, and human expertise for well-informed and strategic decision-making.

Many organizations find success in combining elements of both approaches, creating a hybrid strategy. Each has its strengths, and the "better" choice depends on the specific needs of the organization. Together, they significantly boost efficiency and effectiveness, tackling various decision-making challenges in the diverse landscape of modern enterprises.

Simply book a demo, and Nected experts will assist you in creating the functionalities you desire.

Start using the future of Development, today