Predicting Prediction’s Impact on Analysis
Although I do like to have a variety of books that I enjoy reading from across fiction to non-fiction, it only rings true that I hold a strong emphasis on non fiction books with an even stronger focus on analytics and the future of AI and predictive models. Power and Prediction: The Disruptive Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, and Avi Goldfarb offers a fascinating exploration of how AI is reshaping industries and decision-making for all. It may seem at times that prediction needs to be demystified and interpreted like tarot cards, but the reality is that prediction is becoming more simplified and accessible which will lead to better and stronger stills to be enhanced by analysts. While the book covers a broad range of topics, its insights have particular relevance for the fields of reporting and analytics. There are important and relevant themes from the book that examine how they may impact the future of data-driven decision making.
Impact on Analytics
At its core, the prevalent and constant theme of "Power and Prediction" argues that the fundamental capability that AI brings to the table is vastly improved prediction for users. The authors contend that as AI systems become more sophisticated and data-rich, they transition from mere prediction machines to full-fledged decision engines. This evolution has profound implications for reporting and analytics. It can do so through automated insights, predictive analysis and prescriptive analysis.
Automated Insights: Traditional reporting often involves humans sifting through data to spot trends and anomalies. AI-powered systems can automate this process, surfacing key insights without human intervention. This shift allows analysts to focus on higher-level strategy and decision-making rather than data wrangling which can be immensely time consuming as many data analysts spend a good chunk of their time cleaning and organizing data before they are properly analyzed.
Predictive Analytics: While predictive analytics isn't new, AI enables predictions to be made faster, more accurately, and at a much larger scale. This could lead to real-time forecasting and "What if?" scenario modeling becoming standard features in reporting dashboards.
Prescriptive Analytics: As AI systems evolve into decision engines, we may see a rise in prescriptive analytics – where the system not only predicts outcomes but recommends specific actions to achieve desired results.
The New Way to Make Decisions
The book also emphasizes that AI can and will lead to a fundamental restructuring of how decisions are made within organizations. This restructuring will likely manifest in reporting and analytics through the following methodologies and processes:
Decentralized Decision-Making: With AI providing predictions and recommendations at all levels of an organization, decision-making may become more decentralized. This could lead to a shift from centralized reporting structures to more distributed, embedded analytics throughout the organization.
Human-AI Collaboration Instead of Replacement: The authors stress that AI will augment rather than replace human decision-makers. In the context of reporting and analytics, this might mean interactive dashboards where AI suggestions are presented alongside human-generated insights, fostering a collaborative decision-making process.
Emphasis on Judgment and Strategy: As AI takes over more routine analytical tasks, human analysts have more capacity to focus on areas where their judgment and strategic thinking add the most value. This could lead to an evolution in how analytics teams are structured and the skills they prioritize.
The Future Economics of AI-Driven Analytics
Power and Prediction highlights how AI can and will likely change the economics of various industries by upending them entirely or by altering how they operate and reallocating resources. When it comes to reporting and analytics, this may play out in several ways:
Prediction Costs Reduced: As AI makes prediction cheaper and more accessible, we may see an explosion in the use of predictive analytics across organizations of all sizes. This democratization could lead to more data-driven decision making at all levels.
Complementary Skills Get (Harder)/Better/Faster/Stronger: As prediction activities becomes cheaper and more widely used, complementary skills become more valuable. In relation to analytics, this might translate to an increased premium on data visualization, storytelling, and the ability to translate analytical insights into strategic action. As the automated processes in prediction alleviates analysts, it opens up opportunities
Emergence of New Analytics Business Models: The authors discuss how AI enables new business models. In the realm of analytics, we might see the rise of AI-as-a-Service offerings that provide turnkey predictive and prescriptive analytics capabilities to organizations without the resources to develop them in-house.
The Future of Reporting and Analytics
Applying the learnings and insights from the book, I was able to draw on some insights that I found relevant to the future of reporting and analytics that can lead to a successful adaptation and integration of predictive models and AI:
Ubiquitous Predictive Capabilities: AI-driven predictions will be seamlessly integrated into all levels of reporting, from operational dashboards to strategic planning tools.
Dynamic, Adaptive Reporting: Rather than static reports that need to be maintained regularly and change over time as the needs of the organization grows. We'll see more dynamic, AI-powered dashboards that adapt in real-time to changing conditions and user needs.
Moving from Insights to Decision Making: Analytics tools will evolve and move beyond simply presenting data insights to actively suggesting decisions and simulating their potential or guaranteed outcomes.
Human-AI Synergy FTW: The book covers the concept of the necessity that successful analytics teams will be those that effectively combine AI capabilities with human judgment, creativity, and domain expertise. The symbiosis of the two are what will lead the growth and success in the space.
Ethical Data and AI Governance: Organizations will need robust and well thought out frameworks for ensuring their AI-driven analytics are transparent, fair, accurate, and aligned with organizational values.
Conclusion
Power and Prediction offers a compelling vision of how AI will reshape industries and decision-making processes. For professionals in reporting and analytics, the book provides wonderful and thorough insights that suggest both exciting opportunities and significant challenges ahead. By embracing AI's predictive power while thoughtfully addressing its limitations and ethical considerations, we can usher in a new era of more intelligent, impactful, and responsible data-driven decision making with our analysis and reporting.
As we move forward, it will be crucial for analytics professionals to stay informed about AI advancements, develop new skills, and actively shape how these technologies are integrated into their organizations. The future of reporting and analytics promises to be both transformative and profoundly important in guiding the decisions that will shape our world.
One other thing I love about this book is how it highlights how influential and present Canadian entities are in the AI and prediction space leading the way on developments and innovations in the space. But that could just be me and my bias as a Canadian!
