Redefining Decision-Making Process with Decision Intelligence and AI

Hold tight! You are about to ride on the ever-evolving data & analytics world!

Often, enterprises encounter challenges in effectively managing their colossal volumes of data and struggle to extract meaningful insights. Gone are those days when humans performed data analysis manually, resulting in error-prone insights for decision-making.

Guess what?

We are in an era where AI takes center stage in decision-making, providing necessary support to ensure the right outcomes are accurately predicted.

Here comes the buzzword “Decision Intelligence,” which has taken the data world by storm. This innovative approach strengthens the data and analytics team, fostering effective collaboration and collective decision-making. It also ensures that organizations can confidently choose the right data for analysis, paving the way for more informed and strategic decision-making.

While artificial intelligence reached its pinnacle on Gartner’s hype cycle, the below time-critical predictions on data and analytics initiatives help enterprises equip themselves in the evolving data world.

Prediction 1: Gartner predicts that more than one-third of large organizations will have analysts practicing the discipline of decision intelligence, which includes decision modeling, by the end of 2023.

Prediction 2: Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated.

In continuation of our "second edition," our experts share their comprehensive analysis of the above predictions to help enterprises practice and adapt swiftly to the new decision-making approaches.

Meet Our Experts:

1. Kalyan Allam (Senior Technical Manager - BI) is delving deep into decision intelligence (Prediction 1).
2. Rajkumar Purushothaman (Director of BI & Analytics) shares his experience working on creating an AI-powered decision-making engine (Prediction 2).

Hello all, I am Naresh Kumar , Lead Marketing Strategist at Zuci Systems. Let’s delve into the predictions that help shape organizations' decision-making process!

Moving to Kalyan

Prediction 1: Gartner predicts that more than one-third of large organizations will have analysts practicing the discipline of decision intelligence, which includes decision modeling, by the end of 2023.

Naresh: We observe that the decision intelligence approach has been booming of late. How does decision intelligence differ from traditional data analysis and decision-making processes?

Kalyan:  The conventional decision-making process, as encapsulated by business intelligence and analytics, usually follows a linear progression such as:

·       Data Engineering

·       Business Intelligence

·       Data Science

·       Predictive Analytics

·       Decision Management

While this sequential approach serves the purpose, it comes with inherent drawbacks. The delayed feedback loop between stages and the lack of interdependence among decisions at different levels often lead to inefficiencies and hinder agility.

Naresh: Could you address the challenges organizations face when adapting traditional decision-making processes and how decision intelligence paves the way for organizations to resolve those challenges?

Kalyan: Decision intelligence challenges the traditional siloed approach by fostering collaboration among the various components of the analytics process.

A pivotal concept within decision intelligence is the shift from a 'Data to Decision' approach to a 'Decision to Data' mindset. This shift doesn't downplay the significance of data; rather, it emphasizes enriching data by focusing on what is pertinent to achieving organizational goals. By putting decision-making at the forefront, organizations can streamline their analytics processes and avoid the pitfalls of data overload.

• Decision intelligence urges organizations to define their ultimate objectives before delving into data analytics. Concentrating on the end goals can help develop a more purposeful roadmap with a clear understanding of the desired outcomes. This approach ensures that data, tools, and technologies support decision-making rather than the other way around.
• Decision intelligence addresses the challenge of data overload by advocating for a more focused and strategic approach. Rather than drowning in vast datasets, organizations can extract meaningful insights by aligning analytics projects with defined objectives. This process not only enhances the relevance of data but also ensures that decisions are well-informed and purpose-driven.
•  Instead of tackling analytics in disconnected fragments, organizations are encouraged to adopt a holistic view that integrates decision-making with analytics efforts. This integration ensures that decisions at each step resonate throughout the process, fostering a more dynamic and responsive analytics framework.

Embracing this paradigm shift empowers organizations and ensures that every analytical endeavor is a strategic step toward achieving overarching business objectives.

Naresh: What could be the significant benefits of adopting decision intelligence in organizations?

Kalyan: Among many other benefits, these 4 are the most pivotal advantages:

1.       Accountability

2.       Resource Optimization

3.       Cost Saving

4.       Continuous Learning

• Decision intelligence aligns decisions with organizational objectives. It promotes transparent decision-making and data-driven processes, establishing clear roles and responsibilities that facilitate attributing outcomes to specific decisions.
• By focusing on the most critical decisions and their impact, decision intelligence helps allocate resources more effectively. This includes optimizing budget allocation, personnel deployment, and technology investments.
• With better decision-making and resource allocation, organizations can reduce unnecessary expenses and increase operational efficiency, resulting in cost savings.
• Decision intelligence promotes a culture of continuous learning and adaptation. Organizations can learn from both successful and unsuccessful decisions to refine their strategies and processes.

Naresh: How can organizations plan to train and upskill their current analytics team to embrace the discipline of decision intelligence?

Kalyan: To fully embrace decision intelligence, organizations need to focus on upskilling their employees' soft skills and fostering an organization-wide commitment to some key principles:

1. Prioritizing outcomes.

2. Relying on data-centric approaches for informed decisions.

3. Accountability for decisions.

4. Collaboration amongst teams.

5. Transparency in the decision-making process.

To instill the above 5 principles in organizations and facilitate the successful adoption of decision intelligence, the following training initiatives can be instrumental:

1. Cross-Functional Training Programs: These programs are designed to ensure that everyone involved in the decision-making process, from data engineers to BI analysts, data scientists, and senior executives in various departments, comprehends the entire decision management life cycle. Employees understand the value of collaboration by understanding how individual roles impact the decision-making process.
2. Interdisciplinary Collaboration Training Exercises: Given that decision intelligence relies on cross-functional teamwork, it is essential to encourage collaboration between analytics teams and other departments such as data engineering, business strategy, and IT. Organizations can implement group case study exercises and conduct brainstorming sessions. These exercises enable agility in responding to insights and inputs from other departments.

Naresh: Are there any potential challenges or obstacles that organizations should anticipate when implementing decision intelligence, and what strategies do you recommend for addressing them?

Kalyan: Sure, Naresh. Though every organization encounters unique challenges depending upon their industry, the following are some of the critical challenges that need to be addressed:

1) Cultural Shift:

1. Challenge: A cultural shift may be required to prioritize data-driven decision-making over gut feelings.
2. Strategy: Promote a data-centric culture, reward data-driven decisions, and celebrate success stories related to decision intelligence.

2) Change Management:

1. Challenge: Employees' resistance to change from making traditional decision-making methods.
2. Strategy: Create a change management plan that includes communication, training, and incentives to encourage a smooth transition. Highlight the benefits and successes of decision intelligence.

3) Resistance to Collaboration:

1. Challenge: Different departments or teams may be reluctant to collaborate on decisions.
2. Strategy: Foster a culture of cross-functional collaboration through training, clear communication, and demonstrating the benefits of working together.

4) Data Privacy and Compliance:

1. Challenge: Adhering to data privacy regulations and ensuring ethical data usage while democratizing data.
2. Strategy: Develop robust data privacy policies, comply with relevant regulations (e.g., GDPR, HIPAA), and establish ethical guidelines for data usage and sharing.

5) ROI Measurement:

• Challenge: Quantifying the return on investment (ROI) for decision intelligence
• Strategy: Develop clear metrics and KPIs to measure the impact of decision intelligence on your organization's goals and objectives.

Moving to Raj

Prediction 2: Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated.

Naresh: Could you help us understand how organizations can swiftly adapt to AI-driven decision-making?

Raj: I will like to share a use case on how AI-powered decision-making improved one of India's largest banks' secured loan issuance process. To successfully execute an AI project, there must be strong collaboration between the service provider's analytics team and the stakeholder's IT team to ensure easy access to datasets and seamless deployment of ML models to achieve the desired outcomes.

The bank was required to increase its secured loan portfolio by automating the issuance process, thereby helping the marketing team craft customized messages to target customers based on the machine learning model's insights. We followed a 4-step approach to create an AI decision-making engine that fits into their existing data infrastructure.

Step 1 - Ideation Stage:

• We collaborated with the bank's key stakeholders to understand the requirements for enhancing the secured loan portfolio, such as the desired percentage increase in loan issuance and challenges.
• We established KPIs to benchmark the project's success.
• We collaborated with the IT team to identify and access all relevant data sources, ensuring that data is accurate and up to date.

Step 2 - Data Preparation:

• We looked at the customer’s data sets to gather customer demographics, account information, collateral information, account transactions, and SMA history.
• Our data engineers performed data cleaning (handling missing values, outliers, and inconsistencies) to ensure accuracy and transformed the data into a reliable format for analysis using ELT tools.
• Our data scientists performed initial exploratory data analysis on the transformed data to identify patterns and spot anomalies.

Step 3 – Research & Development:

• We identified the most appropriate features/attributes that can influence the performance of the secured loan portfolio.
• We built sample regression models and integrated AI models into the existing loan issuance workflow.
• We evaluated the performance of initial models based on insights gained during the review. Upon review, we identified the most effective model based on its predictive accuracy and relevance to the business objectives.

Step 4 - Delivery & Monitoring:

• We ensured the predictive model's code was well-documented and deployed into the data infrastructure.
• Our data science team monitored the model's performance and made enhancements as required to ensure optimal results.
• We clearly communicated insights, model outcomes, and recommendations to stakeholders.
• We also implemented alerting mechanisms to notify stakeholders if the model's performance deviates from predefined thresholds, allowing room for proactive management.

Our cohesive human-AI collaborative approach enabled AI models to provide loan eligibility predictions. It paved the way for decision-makers to seamlessly make final decisions, and helped launch tailored marketing campaigns to targeted customers, resulting in a 73% increase in the secured loan portfolio.

Thank you for reading our experts’ insights. We are actively reviewing reputable reports on data and analytics to provide valuable insights that will enhance your organization's data capabilities.

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