Decision Intelligence: A Comprehensive Guide

What is Decision Intelligence?

Decision Intelligence (DI) is a framework that combines human and machine intelligence to improve decision-making outcomes for individuals, teams, and organisations. DI integrates AI, data science, and behavioural science to help people and organizations make better decisions. It uses advanced analytics and decision-making techniques to generate insights and recommendations grounded in data and aligned with human values and preferences. The goal of DI is to improve the quality and speed of decision-making, while minimizing risks and uncertainties.

The DI Landscape

The Decision Intelligence (DI) landscape is a rapidly evolving field that combines data science, management science, and social science to enhance decision-making potential using technology, particularly Artificial Intelligence (AI). Here’s a description of the key aspects of the DI landscape:

Definitions and Scope

• While various definitions exist, the core concept of DI is applying technology to augment human decision-making, leading to faster, more accurate, and more impactful decisions.
• DI can be viewed as both a broad approach integrating technology with decision-making theories from diverse disciplines and a specific technological capability combining AI, Machine Learning (ML), and automation to leverage data in decision-making.

Evolution and Drivers

• The practical demand for data-driven decisions and the hype surrounding AI have propelled DI to the forefront, with organisations increasingly seeking AI-powered tools to improve their decision-making processes.
• DI evolved from advancements in enterprise analytics, including self-service analytics, big data, and ML, offering a more decision-centric approach compared to traditional Business Intelligence (BI).

Forms and Levels of DI

• DI manifests in different forms, often described as levels of increasing machine autonomy:
  • Decision Support: Humans use basic machine tools like data exploration, analytics, and alerts to support their decisions.
  • Decision Augmentation: Machines analyse data and generate recommendations for human decision-makers.
  • Decision Automation: Machines autonomously perform both decision and execution steps, reducing human involvement.
• Some sources also identify additional levels like decision assistance, where AI technologies are minimally used, and decision automation levels where decisions are fully automated using capabilities like hyperautomation.

Benefits of DI

• DI offers numerous benefits, including:
  • Improved decision quality and accuracy: By leveraging AI and data analytics, DI systems can identify patterns, trends, and insights that humans might miss, leading to more informed decisions.
  • Faster decision-making: DI solutions automate data analysis and insight generation, allowing decision-makers to respond more quickly to changing market conditions and opportunities.
  • Increased efficiency: DI automates routine decision-making tasks, freeing up human decision-makers to focus on more strategic and complex issues.
  • Reduced risk: By simulating different scenarios and evaluating potential outcomes, DI systems help organizations assess and mitigate risks associated with various decision options.
  • Enhanced transparency and accountability: DI systems can provide auditable records of the decision-making process, enabling greater transparency and accountability.

Commercial Landscape

• The DI market is experiencing significant growth, with several companies offering DI solutions and platforms. These include established players like Google and Microsoft, as well as specialized companies like Peak, Tellius, Xylem, Noodle.ai, Aera Technology, Diwo, and Quantellia.
• The commercial opportunities in DI extend beyond software and platform development to include services like consulting, training, and infrastructure support.
• Analyst research firms predict substantial growth for the DI market, reaching $22.7 billion by 2027.

Future Trends

• DI is expected to see continued growth and integration with emerging technologies like generative AI and explainable AI, further enhancing decision automation, personalisation, and accessibility.
• There will be an increasing emphasis on ethical and responsible DI development and deployment, addressing concerns about bias, transparency, and accountability.
• Organisations will increasingly recognise DI as a strategic asset, investing in DI expertise and potentially treating decision-making frameworks and models as intellectual property.

Overall, the DI landscape is dynamic and promising. DI is poised to play a central role in shaping the future of decision-making across various sectors, empowering organisations and individuals to make better, faster, and more informed decisions in an increasingly complex and data-driven world.

What are the Characteristics of DI?

• Decision-centric approach: DI shifts the focus from data to decisions. The desired outcome determines which data, tools, and analytics are necessary. Instead of fishing for actionable insights in data, DI takes aim at actions supported by data and analytics.
• Human-machine collaboration: DI combines the strengths of humans and machines. While AI handles routine and programmable tasks, humans can focus on more critical, complex decisions that require unique human capabilities like creative problem-solving and critical thinking. DI systems often allow for human input and feedback, and they provide recommendations that can be evaluated and acted upon by human decision-makers.
• Action-to-outcome focus: DI focuses on understanding how actions taken today will affect future outcomes. It helps decision-makers understand the potential consequences of their choices and choose the actions most likely to achieve their goals.
• Continuous improvement: DI systems incorporate feedback and learning to continuously improve decision-making over time. This includes monitoring the outcomes of past decisions and using that information to refine future decision models.
• Wide range of applications: DI can be applied to various decision domains, including healthcare, education, governance, and business. Within businesses, it is used in sales and marketing, supply chain analytics, HR analytics, gaming analytics, and pricing analytics.

DI is a relatively new field that is rapidly evolving. It draws on a wide range of disciplines, including computer science, statistics, psychology, economics, and management science. DI systems use a variety of technologies, including machine learning, natural language processing, optimization algorithms, and simulation modeling.

DI is a valuable tool for organizations of all sizes that want to improve their decision-making capabilities. By using DI, organizations can make better decisions, faster, with greater confidence, and with a clearer understanding of the potential consequences of their choices.

Importance of DI

Decision Intelligence (DI) is crucial because it addresses the “insights gap” that exists in many organisations. While many strive to be data-driven and use data as an asset, they often fall short of effectively using data to understand their customers, employees, and make informed decisions.

Organisations that effectively use their data to make informed decisions often outperform their competitors. Many organizations find it challenging to translate data insights into practical actions and outcomes. They may excel at gathering and analyzing data but struggle to connect these insights to actual decisions that drive business value. This disconnect creates an “insights gap” where valuable data insights remain unused, hindering the organisation’s ability to achieve its full potential.

The inability to bridge the insights gap results in:

• Inefficiencies: This can manifest as long analytic queues, laborious handoffs between teams and tools, and a general sluggishness in the decision-making process.
• Missed Opportunities: Delays in decision-making due to the lack of timely analysis can lead to missed opportunities. Organizations may hesitate to seize valuable opportunities because they lack the confidence or clarity provided by robust data insights.
• Increased Business Risk: Perhaps the most concerning consequence is the increased business risk. Making decisions without the backing of thorough analysis, or worse, relying on biased analysis, can expose the organisation to unnecessary risk.

DI offers a robust solution to bridge this insights gap by:

• Supporting faster and better insights-driven decisions.
• Allowing for decision-making at cloud scale.
• Fostering an environment of continuous improvement in decision-making processes.

DI helps organisations avoid unnecessary costs associated with slow processes and high failure rates. It facilitates clear and measurable decision-making, which, over time, enhances the company’s knowledge management. The ability to consistently make optimal decisions based on logic and data insights provides a strong foundation for future success and expansion.

DI is also important because the mental state of decision-makers is a critical component in ensuring decision quality. For decisions that can’t be automated, human involvement is inevitable, and the cognitive performance of the team becomes crucial in achieving the desired results. DI recognizes the importance of human factors like psychological safety and intrinsic motivation, which neuroscience and behavioural science research have shown are vital for good decision making.

Furthermore, DI allows a deeper understanding of business performance, which naturally leads to increased performance. By providing a framework for integrating data insights with human expertise and judgement, DI empowers organizations to make decisions that are:

• More informed.
• More strategic.
• Better aligned with overall business goals.

Overall, Decision Intelligence is essential for organizations operating in today’s increasingly complex and data-rich environment. DI empowers organizations to move beyond intuition and gut feelings and towards a more robust and evidence-based approach to decision-making, leading to better outcomes and a stronger competitive edge.

Benefits of DI

Decision Intelligence (DI) offers several benefits for individuals, teams, and organisations aiming to improve their decision-making. DI bridges the “insights gap” by connecting data insights to actions and outcomes. This leads to numerous benefits, including:

• Better, Faster Decisions: DI supports faster, more informed decisions by automating data analysis, uncovering hidden patterns, and providing actionable recommendations. This allows organisations to respond quickly to changing market conditions and seize opportunities more effectively.
• Reduced Inefficiencies: By streamlining and automating decision processes, DI reduces inefficiencies in analytic workflows. It minimises bottlenecks, eliminates redundant tasks, and allows for seamless collaboration between teams and tools. This frees up valuable time and resources for higher-value tasks.
• Improved Business Outcomes and Efficiency: DI focuses on achieving specific business outcomes by aligning decisions with strategic objectives. Through consistent and data-driven decision-making, organisations can reduce costs, increase profits, and achieve better overall performance.
• Minimised Risks and Uncertainties: DI provides a structured framework for evaluating different decision options, assessing potential risks, and understanding the impact of uncertainty on outcomes. This empowers decision-makers to make more informed choices, minimising risks and maximising the likelihood of achieving desired outcomes.
• Enhanced Agility and Flexibility: DI empowers organisations to be more agile and responsive to change. It provides real-time insights and supports rapid decision adjustments, allowing organisations to adapt to dynamic market conditions and stay ahead of the competition.
• Increased Transparency and Trust: DI promotes transparency by providing clear visibility into the factors driving decisions and the rationale behind recommendations. This transparency fosters trust in the decision-making process, both within the organisation and with external stakeholders.
• Data-Driven Culture: DI helps cultivate a data-driven culture within organisations. By integrating data insights into decision-making processes, DI encourages a shift from intuition-based decisions to evidence-based decisions, leading to more informed and objective decision-making across the organisation.
• Continuous Improvement: DI incorporates feedback and learning to continuously improve decision-making over time. It enables organisations to monitor the outcomes of past decisions, analyse what worked and what didn’t, and refine future decision models for better performance. This iterative process of learning and improvement is crucial in today’s rapidly evolving business environment.

The benefits of DI are applicable across a wide range of industries and business functions, from strategic planning and forecasting to operational decisions and customer relationship management. DI is a powerful tool for organisations of all sizes that want to improve their decision-making capabilities and gain a competitive advantage. By leveraging the power of DI, organisations can navigate complexity, optimise resource allocation, and make better decisions that drive success and growth.

User Profiles for DI

Decision Intelligence (DI) is valuable for a wide range of individuals and roles within an organisation. Here’s a breakdown of who can benefit from DI:

For Business Users: DI empowers business users, even those without a technical background, to access and analyse data, uncover insights, and make informed decisions. It provides user-friendly tools and interfaces that simplify complex data analysis, enabling users to understand trends, identify opportunities, and make data-driven recommendations.

For Analysts and Data Experts: DI provides analysts and data experts with advanced tools and capabilities to expedite their analytical workflows. It automates repetitive tasks, provides AI-powered insights and recommendations, and enables them to focus on higher-value activities such as model building and strategic analysis.

Specific roles that benefit from DI include:

• Technology Professionals: These individuals can use DI to understand and apply its principles in developing and implementing DI solutions.
• Decision-makers: DI provides a framework and tools to help decision-makers plan and implement DI initiatives effectively.
• Academic and Industry Researchers: DI offers new avenues for research and exploration, leading to advancements in the field and innovative applications across various domains.

Overall, DI democratises access to data and insights, allowing individuals at all levels of an organisation to make more informed and effective decisions. It breaks down silos between technical and business teams, fosters collaboration, and empowers everyone to contribute to data-driven decision-making.

DI is not limited to large organisations. While it is true that DI can be particularly beneficial for large organisations dealing with complex data landscapes and intricate decision processes, it is also a valuable tool for small and medium-sized enterprises (SMEs). DI solutions can be scaled to fit the specific needs and resources of smaller organizations, providing them with the same advantages of data-driven decision-making enjoyed by larger enterprises.

What Value does DI bring?

Decision Intelligence (DI) enhances decision-making potential by providing a structured framework and advanced tools that empower individuals and organisations to make better, faster, and more informed decisions. DI achieves this by:

• Integrating AI into decision-making: DI leverages the power of AI, particularly Machine Learning (ML), to analyse vast amounts of data, uncover hidden patterns, and generate insights that would be difficult or impossible for humans to identify on their own. By automating data analysis and providing AI-driven recommendations, DI frees up human decision-makers to focus on higher-level strategic thinking and problem-solving.
• Bridging the gap between insights and actions: DI moves beyond traditional Business Intelligence (BI) by focusing on the decision-making process itself rather than just presenting data. It connects insights derived from data analysis to specific actions and outcomes, enabling organisations to translate data into tangible business value.
• Supporting all levels of decision autonomy: DI encompasses a range of capabilities that support different levels of decision-making autonomy, from decision assistance, where humans use basic tools to support their decisions, to decision automation, where machines perform decisions and execution steps autonomously. This flexibility allows organisations to tailor DI solutions to their specific needs and the complexity of the decisions they face.
• Providing a framework for understanding decision impacts: DI utilises tools like causal decision diagrams (CDDs) to visualise the relationships between actions, externals, intermediates, and outcomes. This structured approach helps decision-makers understand the potential consequences of different choices and identify the most effective actions to achieve desired outcomes.
• Facilitating collaboration and knowledge sharing: DI promotes a data-driven culture by providing a common language and framework for discussing and analysing decisions. This fosters collaboration between technical and business teams, enabling organisations to leverage the collective intelligence of their workforce.
• Enabling continuous improvement: DI incorporates feedback mechanisms and learning loops to continuously refine decision-making processes. By monitoring the outcomes of past decisions and analysing what worked and what didn’t, organisations can improve the accuracy and effectiveness of their decision models over time.

DI can be considered a “next-generation BI” because it is more focused on decision-making and improving business outcomes. By bridging the gap between insights and actions, DI provides a framework and tools for data-driven decision-making across all levels of an organisation.

How does DI improve the Analytics Workflow?

Decision Intelligence (DI) significantly improves analytics workflows by breaking down the silos that traditionally exist between people, tools, and processes. These silos often lead to inefficiencies, missed opportunities, and increased business risk.

Here’s a breakdown of how DI enhances analytics workflows:

Breaking Down Silos:

• Traditional analytics workflows often involve separate teams and tools for different stages of the process, such as data exploration, “why” analysis, and improvement recommendations. This leads to delays as insights are passed between teams and tools.
• DI platforms, on the other hand, unify the analytics layer of the modern data stack by offering tools that support the entire analytics process, from data exploration to insight generation to action recommendations. This streamlines workflows and enables faster, more efficient analysis.

Empowering Users:

• DI platforms provide a user-friendly interface that allows individuals with varying levels of technical expertise to participate in the analytics process.
• Natural language querying (NLQ) allows users to ask questions in plain English, eliminating the need for complex coding or SQL queries. This democratises access to data and insights, enabling business users, analysts, and data scientists to collaborate more effectively.

Automating Key Processes:

• DI platforms automate time-consuming aspects of analysis, such as data preparation, visualization, and insight generation. Automated data preparation tools can blend and clean data, suggest joins, and create data pipelines, freeing up analysts to focus on higher-value tasks.
• Automated insights tools use AI to identify key drivers, uncover root causes, and compare cohorts, accelerating the discovery of meaningful patterns and trends. These automation capabilities reduce the analytics backlog and allow organizations to get more done with less.

Enabling a Conversational Approach to Analytics:

• DI platforms encourage a conversational approach to analytics, turning data exploration into an interactive dialogue. Users can ask follow-up questions, drill down into specific areas, and iteratively refine their analysis based on the insights they uncover.
• This iterative process is much faster and more efficient than traditional methods, which often involve creating static reports or dashboards that may quickly become outdated.

Overall, DI transforms analytics workflows by:

• Bridging BI and AI
• Enabling true self-service ad hoc analytics
• Reducing the analytics backlog
• Supercharging analysis
• Democratizing predictive analytics

This leads to better, faster, and more continuously improving insights-driven decisions.

What are the Core Capabilities of DI?

The core capabilities of Decision Intelligence (DI) platforms simplify the complexities of decision-making. These platforms translate business questions posed in natural language, apply suitable algorithms (statistical, classification, regression) efficiently, and present understandable and actionable answers to users in natural language narratives and embeddings. Key capabilities include:

Connecting and Modelling Data: DI platforms connect to various data sources (databases, data warehouses, cloud storage) to create a unified view of the organisation’s data. They allow users to model and prepare data for analysis, including data cleansing, transformation, feature engineering, and creating relationships between different datasets.

Analysing Data: DI platforms provide a comprehensive suite of analytical tools, encompassing descriptive, diagnostic, predictive, and prescriptive analytics. This enables users to explore data, uncover insights, and generate forecasts. Users can ask “what, why, and how” questions about unaggregated data. These platforms may also incorporate techniques such as:

• Guided insights: These help decision-makers uncover the root causes of problems, key drivers of change, and underlying trends and patterns.
• Natural Language Processing (NLP): This facilitates interaction with the DI platform using natural language, making it accessible to users without technical expertise.
• Automated machine learning (ML): This empowers users to build and deploy predictive models without requiring deep data science expertise.

Visualising and Communicating Insights: DI platforms excel in presenting complex data insights in a clear and understandable way through interactive dashboards, reports, and visualisations. Users can interact with data, drill down into details, and share their findings with others, facilitating collaboration and informed decision-making. These platforms also offer:

• Explainable AI (XAI): This helps decision-makers understand the reasoning behind the insights and recommendations provided by the DI system, building trust and transparency.
• Push notifications: Users can receive alerts on significant changes to Key Performance Indicators (KPIs) and unexpected anomalies in their data.

These core capabilities of Decision Intelligence platforms empower organisations to move beyond traditional data analysis and reporting towards a more proactive, insights-driven approach to decision-making.

Decision Intelligence vs. Business Intelligence (BI)

Decision Intelligence (DI) and Business Intelligence (BI) share similarities in their use of data for decision-making, but they have key differences in scope, focus, and capabilities. Here’s a breakdown of the key differences:

1. Target Audience and User Focus:

• BI: Primarily targets data consumers like business users. It focuses on presenting historical data in accessible formats like reports and dashboards.
• DI: Designed for both data consumers and data creators, including analysts and data scientists. It caters to a broader audience with varying technical expertise.

2. Scope and Depth of Analytics:

• BI: Focuses on descriptive analytics, answering questions like “What happened?” and “Which KPI changed?” It helps understand past performance and trends.
• DI: Encompasses all forms of analytics – descriptive, diagnostic, predictive, and prescriptive. It goes beyond describing the past to explain why things happened, predict future outcomes, and recommend optimal actions.

3. Level of Automation and Intelligence:

• BI: Traditionally relies on manual analysis and data exploration. While modern BI tools offer some automation, they lack the sophisticated intelligence capabilities of DI.
• DI: Marked by intelligent automation features such as natural language querying (NLQ), automated visualizations, automated insights generation, automated data preparation, AutoML, and proactive intelligence. These capabilities expedite analysis and make DI accessible to a broader audience.

4. Focus and Objectives:

• BI: Data-centric, focusing on reporting and visualizing data to inform decision-making. It provides insights into historical data but may not directly guide actions.
• DI: Decision-centric, prioritizing the decision-making process itself. It aims to guide actions, enhance decision quality, and optimise outcomes. The desired outcome determines the necessary data, tools, and analytics. DI helps answer “why” KPIs changed and provides recommendations for improvement.

5. Business Value and Impact:

• BI: Helps understand past performance and identify trends, supporting informed decision-making. Its value lies in providing historical context and insights.
• DI: Aims to drive tangible business value by optimising decisions, improving efficiency, mitigating risks, and driving better outcomes. It moves beyond insight generation to directly influence actions and results.

In essence, BI provides the rear-view mirror while DI offers the navigation system. BI focuses on understanding past data while DI aims to guide future actions. BI provides information, while DI delivers actionable intelligence to help organisations achieve their goals. DI can be considered a next-generation BI that is more focused on decision-making and improving business outcomes and process efficiencies in terms of rapidity, productivity, and cost control.

Both DI and BI are valuable tools. While BI excels at providing historical context and data visualization, DI advances decision-making by incorporating predictive and prescriptive analytics, intelligent automation, and a decision-centric approach. DI helps bridge the gap between insights and actions by providing a framework and tools for data-driven decision-making across all levels of an organisation.

Decision Intelligence vs. Manual Analysis (SQL/ Python/Excel)

Decision Intelligence (DI) platforms offer several advantages over manual analysis using tools like SQL, Python, or Excel. The key differences lie in the target audience, the scope of analytics they support, and the level of automation and intelligence they offer.

• Target Audience and Accessibility: Manual analysis is typically performed by analytics creators with technical expertise, such as data analysts and data scientists. DI platforms, however, are designed for both data consumers (business users) and analytics creators. DI platforms democratise access to data and insights by providing a user-friendly interface and natural language querying (NLQ) capabilities, allowing individuals with varying technical skills to participate in the analytics process.
• Scope of Analytics: Manual analysis is generally suited for diagnostic analytics. While it effectively answers “why” KPIs changed, it can be time-consuming when identifying “what” changed and providing recommendations for improvement. DI platforms expedite the process of identifying both the “what” and the “why” behind changes and offer recommendations for improvement. They support all forms of analytics – descriptive, diagnostic, predictive, and prescriptive – enabling users to explore data, uncover insights, and generate forecasts more efficiently.
• Automation and Intelligence: Manual analysis often involves writing complex code or queries, which can be time-consuming and error-prone. DI platforms automate many time-consuming aspects of analysis, such as:
  • data preparation
  • visualisation
  • insight generation

Automated data preparation tools can blend and clean data, suggest joins, and create data pipelines, freeing up analysts to focus on higher-value tasks. Automated insights tools use AI to identify key drivers, uncover root causes, and compare cohorts, accelerating the discovery of meaningful patterns and trends. DI platforms also provide intelligent features such as automated machine learning (ML) and explainable AI (XAI), which further enhance the analytics process.

In essence, while manual analysis tools can be powerful in the hands of skilled users, they often lack the speed, accessibility, and intelligent automation capabilities that DI platforms provide. DI empowers a wider range of users to perform comprehensive analysis, leading to better, faster, and more informed decisions.

Decision Intelligence vs. Data Science Tools

Decision Intelligence (DI) platforms and data science tools (DSML) represent distinct approaches to data analysis and decision-making, each with its own strengths and target audience. Here’s a breakdown of their key differences:

Target Audience and Expertise

• DSML tools are primarily used by advanced analytics creators, such as data scientists, who possess deep technical expertise in areas like programming, statistics, and machine learning. DI platforms cater to a broader audience, including data consumers (business users) and analytics creators (analysts and data scientists) alike. DI platforms democratise data access and insights through user-friendly interfaces and natural language querying (NLQ), empowering users with varying technical skills to participate in the analytics process.

Scope of Analytics

• DSML tools excel in predictive and prescriptive analytics, focusing on modelling future outcomes and identifying ways to optimise results. DI platforms, however, support the full spectrum of analytics – descriptive, diagnostic, predictive, and prescriptive. This allows organisations to quickly understand the “what”, “why”, and “how” of their business using unaggregated data. DI platforms expedite the entire analytics process, from exploring data and uncovering insights to generating forecasts and prescribing actions.

Level of Automation and Intelligence

• While DSML tools are incorporating more automation through AutoML, they are still heavily reliant on manual processes. Users often need to write complex code or queries, which can be time-consuming and prone to errors. DI platforms are designed with intelligent automation at their core. They automate various aspects of the analytics workflow, including:
  • Data preparation: blending, cleansing, feature engineering, and pipeline creation
  • Visualisation: creating charts, dashboards, and reports
  • Insight generation: identifying key drivers, root causes, and cohort comparisons

DI platforms also incorporate intelligent features like:

• Automated machine learning (AutoML) which enables users to build and deploy predictive models without requiring in-depth data science expertise.
• Explainable AI (XAI) which provides transparency into AI-driven insights and recommendations, fostering trust among decision-makers.

DI platforms act as a bridge between traditional Business Intelligence (BI) and AI, enabling self-service ad hoc analytics, reducing the analytics backlog, and democratising access to predictive analytics. They unify the analytics layer of the modern data stack by providing a single platform to explore, analyse, and generate insights from data.

In conclusion, DI platforms and DSML tools serve different purposes and cater to distinct user groups. While DSML tools remain essential for advanced analytics and modelling, DI platforms provide a more accessible and automated approach to data analysis, empowering a broader range of users to make better, faster, and more data-driven decisions.

Where Does Decision Intelligence Fit in the Modern Data Stack?

Decision Intelligence (DI) occupies a crucial position within the modern data stack, unifying and transforming the analytics layer, which has traditionally been fragmented. Here’s how DI fits into the modern data stack:

• Data Sources Layer: The modern data stack begins with various data sources, including applications like Salesforce and Google Analytics, databases like Oracle and SQL Server, and diverse data formats such as CSV, JSON, and Avro files.
• Data Integration and Transformation Layers: These layers handle the ingestion, processing, and transformation of data from various sources into a usable format for analysis. Tools like data pipelines, ETL processes, and data warehouses play a vital role here.
• Data Storage Layer: The processed data is stored in cloud data warehouses or data lakes, providing a centralised repository for all relevant data.
• Analytics Layer (DI’s Domain): This is where DI platforms shine, unifying and transforming the analytics layer, which traditionally consists of siloed tools that answer “what happened” (dashboards, reports), “why it happened” (manual hypothesis testing and SQL slicing), and “how to improve” (manual analysis and data science tools). DI breaks down these silos, providing a single platform for all analytics needs.
• Insight Generation and Action: DI platforms leverage AI and machine learning to automate insight generation, identifying key drivers, uncovering root causes, and generating predictions. They present insights in an accessible manner through interactive dashboards, visualisations, and natural language narratives, enabling users to understand the “what”, “why”, and “how” of their business.
• Decision Support and Automation: DI platforms facilitate data-driven decision-making by providing decision support, augmentation, and automation capabilities. They bridge the gap between insights and action, enabling organisations to translate data into informed decisions and drive better business outcomes.

In summary, DI sits atop the modern data stack, unifying and transforming the analytics layer. It connects to various data sources, processes data, and leverages AI-powered insights to support better, faster, and more continuously improving data-driven decisions across the organisation.

What are the 4 levels of decision-making autonomy in DI?

Decision Intelligence (DI) encompasses a spectrum of autonomy levels, similar to the levels in autonomous driving. The sources describe three levels of decision-making autonomy within DI:

• Level 1: Decision Assistance: This is the most basic level of DI, relying minimally on AI technologies. It focuses on providing users with easy access to relevant data and tools to support their decision-making. Decision assistance tools can include search engines, mobile apps, spreadsheets, and basic analytics platforms. Examples include using internet search engines to gather information on a topic, leveraging mobile apps to track key performance indicators, or utilising spreadsheets to perform basic data analysis.
• Level 2: Decision Support: At this level, the focus is on providing users with the necessary data and analytical tools to support their decision-making. Humans are still responsible for making the final decision, but they are aided by the insights generated by the DI system. This level typically involves tasks that can be solved with experience or cognitive reasoning, using data visualisations and analyses. A common example is the use of Clinical Decision Support Systems (CDSS) that help doctors access collective expert knowledge and research findings.
• Level 3: Decision Augmentation: This level involves machines taking on a more active role by analysing data and generating recommendations for human decision-makers. Humans remain in control, reviewing the recommendations and making the final decision. Decision augmentation is particularly beneficial for complex decisions with multiple variables or when a rapid response is required. For example, a recommendation engine that suggests optimal pricing strategies based on real-time market data would fall under decision augmentation.
• Level 4: Decision Automation: At this highest level of DI autonomy, machines handle both the decision and execution steps autonomously, with minimal human intervention. This level is suitable for routine, repetitive decisions or decisions that require immediate action based on predefined rules and parameters. A good example is a system that automatically reorders supplies when inventory reaches a predetermined threshold.

The level of autonomy chosen for a specific decision depends on factors like the complexity of the decision, the desired speed of response, the level of trust in the AI system, and the potential consequences of an incorrect decision. It is crucial to note that even with increasing levels of automation, DI aims to keep humans in the loop to ensure ethical considerations, accountability, and transparency in decision-making.

7 Step Decision-Making Process

The sources detail a common seven-step decision-making process applicable to various situations, from everyday choices to complex business decisions. This process provides a structured approach to problem-solving and decision-making:

1. Identifying & Frame the Problem: The process begins with recognizing a problem or opportunity that requires a decision. This stage involves clearly defining the issue and understanding its potential impact. For example, a business might identify declining sales as a problem that needs to be addressed.
2. Gathering Information: Once the problem is defined, the next step is to gather relevant information to understand the situation thoroughly. This may include collecting data, researching industry trends, consulting experts, and seeking input from stakeholders.
3. Identifying Alternatives: After gathering sufficient information, the decision-maker should brainstorm and identify various potential solutions or courses of action to address the problem. Generating a diverse set of alternatives is crucial for effective decision-making.
4. Evaluating Alternatives: This step involves carefully analysing each alternative, considering its potential benefits, drawbacks, risks, and feasibility. Decision-makers can use various tools and techniques, such as decision matrices, SWOT analysis, and cost-benefit analysis, to evaluate the options.
5. Selecting the Best Alternative: Based on the evaluation, the decision-maker chooses the most suitable option that best aligns with the desired outcome, considering factors like potential impact, feasibility, and alignment with organizational goals.
6. Implementing the Decision: Once the decision is made, it’s time to put it into action. This involves developing an implementation plan, allocating resources, communicating the decision to stakeholders, and executing the chosen course of action.
7. Reviewing the Decision: The final stage involves monitoring the outcomes of the implemented decision, evaluating its effectiveness, and making adjustments as needed. This feedback loop is essential for continuous improvement and learning from past decisions. It helps to identify any unintended consequences and refine future decision-making processes.

Use Cases across different Industries

The sources provide various Decision Intelligence (DI) use cases across numerous industries, highlighting the versatility of this technology. DI is applicable to both industry-agnostic and industry-specific situations.

Industry-Agnostic Use Cases:

These use cases apply across various sectors and address common business challenges:

• Sales & Marketing:
  • Brand Performance: DI can analyse data on brand awareness, customer sentiment, and market trends to assess brand health and guide strategies for improvement.
  • Promotion/Incentive Optimisation: DI can help businesses optimise promotional campaigns by analysing customer data, predicting response rates, and identifying the most effective incentives.
  • Customer Segmentation/Customer 360°: DI enables creating detailed customer profiles by analysing data from various sources, leading to personalised marketing strategies and improved customer experiences.
  • CLTV (Customer Lifetime Value): DI can predict a customer’s lifetime value, enabling businesses to focus on high-value customers and tailor their offerings accordingly.
  • Market Share Insights: DI can analyse market data to provide insights into market share trends, competitor strategies, and opportunities for growth.
  • Ecommerce & Multichannel Analysis: DI can help businesses understand customer behaviour across various online and offline channels, optimising marketing and sales strategies for each channel.
• Supply Chain Analytics:
  • Automated Root Cause Analysis (of Defects, Delays, etc.): DI can quickly identify the root causes of supply chain disruptions, enabling businesses to take corrective actions and minimise downtime.
  • Inventory Management/Optimisation: DI can optimise inventory levels by predicting demand, identifying optimal reorder points, and reducing waste and stockouts.
  • Demand Forecasting: DI can accurately forecast future demand by analysing historical data, market trends, and external factors, improving production planning and resource allocation.
  • Supplier Performance Monitoring: DI can track supplier performance based on metrics like delivery time, quality, and cost, enabling businesses to identify and address potential issues proactively.
  • Quality Analytics: DI can analyse quality data to identify patterns and trends, improving product quality and reducing defects.
• HR Analytics:
  • Ad hoc analysis of HR metrics (headcount, attrition, salary, rewards, etc.): DI provides insights into workforce trends, enabling better talent acquisition, retention, and compensation strategies.
• Gaming Analytics:
  • Monetisation: DI helps game developers analyse player behaviour to optimise monetisation strategies and maximise revenue.
  • Game Experience/Design: DI provides insights into player preferences and engagement patterns, guiding game design and feature development.
  • Fraud Detection: DI can identify fraudulent activities within online games, protecting players and game operators.
• Pricing Analytics:
  • Elasticities: DI can determine price elasticity by analysing how demand changes in response to price fluctuations, allowing for optimised pricing strategies.
  • Seasonality Pricing Optimisation: DI can analyse historical sales data and seasonality trends to recommend optimal pricing strategies for different periods.

Industry-Specific Use Cases:

These use cases target specific sectors and their unique challenges:

• Communications:
  • Network Management and Optimisation: DI can monitor network performance, identify bottlenecks, and optimise resource allocation for improved service quality.
  • Capacity Planning and Forecasting: DI helps predict future network capacity needs, ensuring sufficient infrastructure to meet demand.
  • Preventive Maintenance: DI can analyse network data to predict potential equipment failures, allowing for proactive maintenance and reduced downtime.
• CPG (Consumer Packaged Goods):
  • Shopper Insights: DI can analyse consumer purchase data to understand shopping behaviour and preferences, informing product development and marketing strategies.
  • Capacity Planning: DI helps CPG companies predict production capacity needs based on demand forecasts and optimise resource allocation.
  • Promotion Optimisation: DI enables targeted and effective promotional campaigns by analysing consumer data and predicting response rates.
• eCommerce & Retail:
  • Customer Analysis: DI provides deep insights into customer behaviour, preferences, and purchase patterns for personalised recommendations and marketing strategies.
  • Multi-channel: DI analyses data from various online and offline channels to understand customer journeys and optimise channel strategies.
  • Marketing Analysis: DI helps assess the effectiveness of marketing campaigns, identifying key drivers of success and areas for improvement.
  • Operational and Supply Chain: DI can optimise various operational aspects, including inventory management, logistics, and delivery routes.
• Financial Services:
  • Credit Risk: DI helps assess creditworthiness, predict loan defaults, and manage risk exposure.
  • Fraud Prediction: DI can identify fraudulent transactions by analysing patterns and anomalies in financial data.
  • Rebate/Cash Flow Forecasting: DI accurately predicts future cash flow and manages rebate programmes effectively.
  • Loan Analysis: DI assists in loan origination, underwriting, and servicing, streamlining processes and improving efficiency.
• Insurance:
  • Policy Underwriting: DI helps assess risk profiles, set premiums, and automate underwriting processes.
  • Risk Modelling: DI creates sophisticated risk models to assess potential losses and manage overall portfolio risk.
  • Fraud Detection: DI identifies fraudulent insurance claims by analysing patterns and anomalies.
• Media:
  • Ad Pricing Optimisation: DI helps media companies set optimal pricing for advertising inventory based on audience demographics, viewer engagement, and market demand.
• Pharmaceuticals & Life Sciences:
  • Patient Identification: DI assists in identifying patients eligible for clinical trials based on medical records and specific criteria.
  • Trial Enrollment Monitoring: DI tracks patient enrollment in clinical trials, identifying bottlenecks and optimising recruitment strategies.
  • Market Access: DI analyses market data to identify opportunities for expanding product reach and access.
  • HCP Targeting: DI helps identify and target healthcare professionals for effective product promotion and education.
  • Commercial Effectiveness: DI assesses the impact of sales and marketing efforts on product performance.
  • Rebate Operations: DI manages rebate programs efficiently, ensuring compliance and optimizing costs.

These examples demonstrate how DI is transforming decision-making across various sectors. By harnessing the power of data and AI, organisations can leverage DI to gain a competitive advantage, improve efficiency, and achieve better outcomes. The specific use cases within each industry are constantly evolving as DI technology advances and new applications emerge.

What services are required for Decision Intelligence?

For effective decision intelligence implementation and adoption, various services are needed. These services may be provided in-house or via a third-party provider.

Data Services:

• Data Collection and Integration: This service is essential for gathering data from various sources and integrating it into a centralised system. The chosen DI tool should connect to all relevant data sources and handle diverse data formats, including structured, unstructured, and semi-structured data.
• Data Cleansing and Preparation: This involves cleaning, transforming, and preparing data for analysis. The DI solution should offer automated data quality checks, data cleaning, and transformation capabilities to ensure data accuracy and consistency.
• Data Modelling: Defining the relationships between data elements and creating a data model that supports the decision-making process.
• Data Governance: Implementing policies and procedures for data management, ensuring data security, privacy, and compliance with regulations.

Decision Intelligence Platform and Tools:

• Decision Intelligence Platform: Selecting and implementing a suitable DI platform that meets the organisation’s needs. This platform should offer capabilities for data analysis, visualisation, prediction, and recommendation.
• Integration with Existing Systems: Integrating the DI platform with existing business intelligence (BI), data warehousing, and other relevant systems to ensure a seamless flow of information.
• Custom Application Development: Developing custom applications or dashboards tailored to specific decision-making needs, providing a user-friendly interface for accessing insights and recommendations.
• API Integration: Leveraging APIs to connect the DI platform with external systems and services, enabling data sharing and automation.

Human Resources and Expertise:

• Decision Intelligence Team: Building a team with the necessary skills and expertise in data science, AI/ML, decision theory, and business domain knowledge. This team will be responsible for designing, implementing, and managing the DI system.
• Training and Education: Providing training and education to business users on how to effectively use the DI system and interpret insights. This ensures successful adoption and empowers users to make data-driven decisions.
• Change Management: Implementing a change management process to ensure smooth transition and adoption of the new DI system across the organisation.

Other Services:

• Decision Modelling and Analysis: This service involves creating decision models, defining decision criteria, and analysing potential outcomes using various techniques like decision trees, decision tables, and simulation.
• Consulting and Advisory: Seeking expert advice on DI strategy, implementation, and best practices from experienced consultants. This can help organisations avoid common pitfalls and maximise the value of their DI investments.
• Ethical Considerations and Responsible AI: Implementing ethical guidelines and frameworks to ensure responsible use of AI and data in decision-making.

These services collectively support the successful implementation and adoption of decision intelligence within an organisation. The specific requirements may vary depending on the industry, business needs, and the chosen DI solution.

Short term hype or long-term need?

The sources suggest that decision intelligence (DI) is more than just short-term hype and is likely to survive and thrive in the next 5 to 10 years. Several factors contribute to this prediction:

• Growing Market and Adoption: Analyst research firms indicate that almost every company is expected to introduce DI in the coming years, with the market projected to reach $22.7 billion by 2027. This widespread adoption suggests a sustained demand for DI solutions and services, indicating its long-term viability.
• Addressing Real Business Challenges: DI directly addresses the challenges of data-driven decision-making in a rapidly changing and complex business environment. It helps organisations move from intuition-based decisions (“I feel”) to knowledge-based decisions (“I know”) by leveraging AI and data to make more informed choices. As these challenges will likely persist, the need for DI will continue to grow.
• Transforming Industries and Markets: DI is not just about improving individual decisions; it has the potential to transform entire industries and markets by enabling organisations to make more strategic and impactful decisions. As more businesses adopt DI, it will likely contribute to wider industry shifts and changes in market dynamics, solidifying its role in shaping the future of business.
• Evolution of Decision-Making: DI represents a significant evolution in decision-making, moving beyond traditional data-driven approaches by incorporating human intuition, advanced analytics, and AI. By augmenting human intelligence with AI, DI enables more efficient, effective, and ethical decision-making processes. This evolution is not a temporary trend but a fundamental shift in how organisations approach decision-making, ensuring DI’s relevance in the long term.
• Integration of Emerging Technologies: DI is well-positioned to leverage the advancements in emerging technologies like generative AI (e.g., ChatGPT). These technologies enhance DI’s capabilities by automating tasks, providing insights, and facilitating more complex and nuanced decision-making processes. As AI technology continues to evolve, DI will likely adapt and integrate these advancements, further strengthening its potential for the future.
• Focus on Business Outcomes: DI prioritises delivering measurable business outcomes and ROI. By focusing on creating a tangible impact on business goals, DI demonstrates its value to organisations and reinforces its position as a critical business tool for long-term success.
• Addressing the Need for Speed and Agility: In today’s fast-paced business environment, organisations need to make decisions quickly and adapt to change effectively. DI provides the tools and frameworks to enable agility and rapid decision-making, addressing a critical need for modern businesses.

While the specific implementations and applications of DI may evolve over time, its core principles and ability to enhance decision-making are likely to remain relevant and valuable for years to come.

Furthermore, DI’s capacity to bridge the gap between human intuition and data-driven insights positions it as a key component of the future of work. As organisations increasingly rely on data and AI, DI will be essential for ensuring that these technologies are used ethically and effectively to support human decision-making.

Commercial Opportuinities in DI

The sources provide insights into various commercial opportunities within the Decision Intelligence (DI) landscape that have strong potential for profit. Here are some key areas:

• Decision Intelligence Solutions and Platforms: This is a burgeoning market with significant growth projected over the next decade. Companies like Peak, Tellius, Xylem, Noodle.ai, Aera Technology, Diwo, and Quantellia are already offering cutting-edge DI solutions and platforms. These platforms leverage various technologies like AI, ML, AutoML, computer vision, and MDP models to provide services for decision augmentation and automation. By developing and marketing such solutions, companies can capitalise on the increasing demand for DI across various industries and functional areas. The sources anticipate a $22.7 billion DI market by 2027, indicating substantial financial prospects.
• Decision Intelligence Service Providers (DISPs) – See Below As DI adoption grows, the demand for specialised expertise will create opportunities for DISPs. These providers offer tailored DI services to organizations, helping them implement DI strategies, build custom applications, and integrate DI into their existing systems. DISPs can leverage their expertise in specific verticals and decision-making domains to create niche offerings and attract clients seeking targeted solutions.
• Decision Intelligence Infrastructure Providers (DIIPs): Building and maintaining a DI system in-house can be complex and resource-intensive. This presents opportunities for DIIPs to offer infrastructure services like data management, cloud computing, and security, allowing organizations to focus on their core business operations. By providing reliable and scalable infrastructure solutions, DIIPs can cater to the growing need for robust and secure DI systems.
• Education and Training: As DI adoption increases, the demand for skilled professionals will rise. This presents opportunities for training providers to offer courses, certifications, and workshops on DI concepts, tools, and techniques. These educational offerings can cater to individuals seeking to enhance their DI skills and organizations looking to upskill their workforce. There are already graduate certificates in Decision Analysis (DA) offered by institutions like Stanford, indicating the growing academic interest in decision-making disciplines.
• Consulting and Advisory Services: Organizations may require guidance on developing their DI strategy, identifying suitable use cases, and implementing DI solutions effectively. This creates opportunities for consultants with expertise in DI to offer advisory services, helping clients navigate the complexities of DI adoption and maximize their ROI.
• Niche Applications and Vertical Specialisation: DI has broad applications across various industries, including logistics, retail, pricing and marketing, manufacturing, sustainability, and healthcare. Companies can identify niche opportunities within specific industries by developing tailored DI solutions that address the unique challenges and decision-making requirements of those sectors.
• Integration with Existing Technologies: Integrating DI with existing technologies like business intelligence (BI) and data warehousing creates opportunities for companies to enhance the value of their current offerings. By incorporating DI capabilities into these systems, companies can provide a more comprehensive and powerful decision-making solution to their existing clients.
• Ethical and Responsible AI Services: With the increasing focus on ethical considerations in AI, there is a growing market for services that ensure responsible and unbiased DI implementations. Companies can offer services like bias detection and mitigation, transparency audits, and ethical framework development to help organizations deploy DI systems in a socially responsible manner.

Overall, the commercial opportunities in DI are vast and multifaceted. By understanding the current trends, anticipating future needs, and focusing on delivering tangible business value, companies and individuals can capitalise on the growing demand for DI solutions and services to generate substantial profits. As DI evolves and becomes more integral to business success, the opportunities for innovation and growth within this field will continue to expand.

Decision Intelligence Service Providers (DISPs)

Decision Intelligence Service Providers (DISPs) represent a significant commercial opportunity within the expanding DI market. As more organisations adopt DI, the demand for specialised expertise to implement and integrate these systems will rise, creating a favourable market for DISPs.

Here’s a breakdown of the key aspects of DISPs and their role in the DI landscape:

• What They Offer: DISPs provide a range of services tailored to assist organisations in successfully implementing DI strategies. These services include:
  • Implementation Support: Helping organisations design and deploy DI systems, including selecting appropriate technologies, integrating with existing systems, and managing the implementation process.
  • Custom Application Development: Building bespoke DI applications tailored to the specific needs and challenges of individual organisations.
  • Integration Services: Seamlessly integrating DI solutions with existing data management, analytics, and business processes.
  • Training and Education: Providing training and workshops to upskill an organisation’s workforce on DI concepts, tools, and best practices.
  • Strategic Consulting: Advising organisations on developing their overall DI strategy, identifying suitable use cases, and measuring the impact of DI initiatives.
• Value Proposition: DISPs bring valuable expertise to organisations that may lack the internal resources or experience to effectively implement DI on their own. They bridge the knowledge gap and provide specialised skills, allowing organisations to focus on their core business operations while benefiting from DI capabilities.
• Specialisation and Niche Offerings: DISPs can differentiate themselves by focusing on specific verticals or decision-making domains. For instance, a DISP might specialise in DI for financial services, healthcare, or supply chain management. By developing deep expertise in a particular area, they can attract clients seeking targeted solutions.
• Competitive Advantage: The growing DI market presents a lucrative opportunity for DISPs to establish themselves as trusted partners for organisations embarking on their DI journey. By delivering successful DI implementations and demonstrating tangible business value, DISPs can build a strong reputation and capture a significant share of the market.
• Examples in the Market: Several companies, such as Data Innovation.AI, CModel, IntelliPhi, SatSure, and C-Plan.IT, are already operating as DISPs, demonstrating the viability of this business model.

In conclusion, the increasing demand for DI expertise fuelled by rising DI adoption presents a compelling opportunity for DISPs. By offering tailored services, specialising in niche areas, and delivering demonstrable value to organisations, DISPs are well-positioned to capitalise on the growth of the DI market and establish themselves as key players in the future of data-driven decision-making.

Links:

https://www.alteryx.com/glossary/decision-intelligence