Unveiling the Path to Decision Intelligence: A Five-Step Transformative Journey
AI is not a substitute for human intelligence; it is a catalyst for transforming it - Fei Fei Li
The evolution of AI has revolutionized various aspects of business and life, transforming the decision-making process along the way. In our previous post, we explored the four waves of AI and its impacts on business. Now, let's delve into how human decision making has been transformed throughout this evolutionary journey and the corresponding analytics maturity levels and data technologies involved.
Level 0: Instinct based decision making - No or Naïve Analytics before 1950s
In this early stage, decision making relied solely on gut feelings, with limited or ad hoc analysis in specific areas. It wasn't until 1854, when English physician John Snow used statistics and frequency counts to identify the root cause of a Cholera Disease Outbreak in London that data analysis started to make an impact. Later, in 1865, a banker utilized data and analysis for strategic initiatives, providing a competitive advantage . However, these analyses were based on small samples and presented in the simplest table format. There was no sophisticated data technology available at this level.
Level 1: Instinct and Insight - Descriptive Analytics (1950s - 1960s)
With the emergence of business intelligence in the 1950s, driven by companies like IBM and Microsoft, the introduction of computers made data analysis easier and enabled data-informed decision making. Nevertheless, most business decisions still relied heavily on gut feelings, with limited support from insights derived from data analysis. Descriptive analytics, which focused on understanding what happened by examining historical data, became the primary method of analysis. Dashboards and reporting tools were developed to visualize analysis results. Additionally, hierarchical Database Management Systems (DBMS) like IBM's IMS made data analysis more convenient, marking the transition to Level 1 in analytics maturity.
Leve2: Insight and Instinct: Diagnostic - Root cause analysis (1970s -1990s)
During this period, the first BI vendors, such as SAP and Oracle, emerged, providing tools to organize and access data more effectively. IBM developed the first Business Intelligence system, which transformed decision making from gut instinct-driven to data-informed insight-driven approaches.
The introduction of data warehousing in the 1980s solved the challenges of accessing data from different systems and lacking infrastructure for data exchange.
Diagnostic analysis, which explored the reasons behind events, complemented descriptive analysis. Techniques like A/B testing and experiments were employed to support diagnostic analysis.
Business intelligence applications, such as Crystal Reports and MicroStrategy, became essential for managers seeking insightful business strategies. The analytics maturity advanced to Level 2, where decision making involved understanding both what happened and why it happened, leading to more intelligent decision making.
Level 3: Analytics and Instinct - Predictive Analysis (1990s - 2005)
In the 1990s, data mining gained prominence as a crucial aspect of business intelligence (BI) operations. It utilized statistical techniques and algorithms to uncover patterns in large datasets. BI also made its way into the mainstream of business arena, and techniques became marketable through batch-processing reporting.
Predictive analytics, which involved businesses forecasting future outcomes based on historical data, gained popularity and became essential for strategic planning. It played a significant role in defining key performance indicators (KPIs) and objectives and key results (OKRs). Advanced analytics techniques, including machine learning, enabled accurate predictions and automated decision-making. Model fitting became widely adopted, greatly enhancing business performance. This period marked the maturity of analytics, reaching Level 3, characterized by a strong desire to accurately predict and automate decision-making processes.
To manage data availability, usability, integrity, and security within enterprise systems, data governance technologies were also employed.
During this era, decision-making became a blend of analytics and intuition. Decision intelligence emerged as a catalyst, combining descriptive analytics (what happened) with diagnostic analytics (why it happened) and inspiring the desire to know what will happen through predictive analytics. This integration allowed for smarter decision-making processes.
Level 4: Analytics + Limited Human Feedback - Prescriptive Analytics (2006 - 2014)
At this stage, decision making combined AI, limited human feedback, and gut feelings. The exponential growth of data generated by businesses due to the internet, social media, and other digital technologies led to the emergence of big data. To process and analyze large datasets, new tools and technologies were developed.
Business Intelligence 2.0, or Decision Intelligence 1.0, began taking shape, with major players like IBM, Microsoft, SAP, and Oracle dominating the BI landscape. Self-service BI gained traction, empowering non-technical users to create and analyze reports without relying on IT departments or data analysts. AI started automating decision making and offering prescriptive recommendations through simulation-driven analysis. Deep learning, unstructured data analysis, NLP, and image processing techniques gained prominence. BI tools became device-agnostic and incorporated visual analytics, enabling analytical reasoning through interactive visual interfaces. Decision intelligence made prescriptive analytics more accessible by providing insights into how to make the right decisions using data. The analytics maturity entered Level 4, positioning companies to compete effectively.
Level 5: Decision Intelligence - Level 5 Augmented Analytics (2015 - )
In the current era, AI and analytics act as trusted advisors to humans. Cloud computing has become prevalent, offering scalable and cost-effective solutions for managing and analyzing data. Advancements in AI and machine learning have automated various aspects of BI operations, including data processing, analysis, and reporting. Decision intelligence has taken center stage, embedding levels of data analytics in applications through augmented analytics. Data lake house technology has simplified data literacy, while data observability facilitates intelligent data governance by monitoring changes and recommending actions.
Reinforcement learning and generative AI have empowered large language models to engage in conversational interactions with humans and handle multiple tasks, accelerating the decision-making process. And humans can teach machines to learn so human and machine collaboration begins and that exponentially speeds up the decision making process.
Analytics now serves as a human assistant or trusted advisor, facilitating decision making. Cognitive computing has become a reality, supporting self-learning and augmenting all levels of analytics automatically. The analytics maturity reaches its peak at Level 4, gaining widespread adoption across organizations.
This movement towards augmented analytics will not only benefit businesses but also impact every aspect of life. Every company is becoming data and analytics-driven, and individuals are increasingly using data to make decisions. Collaboration with robots can generate innovative ideas, and product innovations can occur at lightning speed. The fusion of human intelligence with AI-powered decision-making systems holds the potential to shape a future where efficiency, accuracy, and strategic insight coexist harmoniously.
In conclusion, the evolution of AI has propelled human decision making to new heights. From instinct-based decision making to augmented analytics, each stage has brought about transformative changes. As analytics maturity has advanced, businesses have become increasingly data-driven, resulting in more intelligent and informed decision making. And every company has a well established data culture, analytics is widespread and not limited to a small portion of the workforce, and everyone leverages analytics to make decisions that unlocks the full potential of data.
The emergence of AI and machine learning has enabled automation, prediction, and prescriptive recommendations, further enhancing the decision-making process. With decision intelligence and augmented analytics, the collaboration between humans and AI is poised to unlock new frontiers of innovation and efficiency, benefiting both businesses and individuals alike.
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