3 Analytics Literacy

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Before you read this chapter, just some advice – you may never be asked to program a neural network or an unsupervised machine learning algorithm, but it is essential that you have a rough idea as to how these algorithms work and what these algorithms are good at doing. The goal of these next few pages is to demystify data science and these different algorithms so that everyone of every age is prepared to have an everyday analytics conversation.AI and analytics literacy is understanding how advanced analytic algorithms and concepts work – including machine learning, reinforcement learning, and deep learning – and what types of problems these analytical algorithms can be applied to. This is the most challenging chapter of the book as it will provide a broad overview of the different types of advanced analytic algorithms and concepts. Think of this as a family of additional...

Understanding the data science development process

Data science uses a highly iterative, collaborative development process to identify those variables and metrics that might be better predictors of performance. This development process supports testing, experimenting, failing, learning, unlearning, and retrying using a combination of advanced analytic algorithms, data transformation, and data enrichment techniques necessary to discover those variables and metrics that might be better predictors of performance.The data science development process is a non-linear framework of rapid exploration, discovery, learning, testing, failing, and learning again. The data science development process drives collaboration between data scientists and data engineers and business and operational subject-matter experts and domain experts to ideate, explore, and test those variables and metrics that might be better predictors of performance.Figure 3.4 highlights the highly non-linear, recursive approach...

Navigating the analytics maturity index

I struggled with this section, but not because explaining the different analytic algorithms and concepts was hard. For me, the complicated problem was how to classify and group these different algorithms in a way that makes it easier for the average Citizen of Data Science to understand where and how to apply these algorithms.

Leading organizations are cultivating a culture of continuous exploration, learning, adaptation, and re-learning. This iterative process involves gathering insights from every customer engagement to understand their preferences and behaviors better. Similarly, interactions with products enable organizations to gain knowledge about product performance and behaviors while engagements with employees, suppliers, and partners provide valuable opportunities to improve the effectiveness and efficiency of business operations. To create a continuous learning culture, organizations need to understand the Analytics Maturity Index ...

Summary

While you may never be asked to program a neural network or an unsupervised machine learning algorithm, Citizens of Data Science must have a rough idea of how these algorithms work and what these algorithms are good at doing. Hopefully, the chapter helped to demystify data science and advanced analytics and provided the foundation for everyone to feel comfortable and empowered to participate in these analytics conversations. Finally, there is a dramatic conceptual difference between level-3 and level-4 analytics. The primary goal of level-3 analytics is to help improve, or optimize, human decision-making. However, level-4 analytics introduces an entirely new concept around learning analytics – analytics that can learn and adapt with minimal human intervention. The next chapter will focus on understanding how AI works and the critical role of the AI utility function in guiding the AI model's workings. I will also explain the vital role that you as humans play in defining...

Summary

AI is the ultimate goal of an organization’s analytics maturity. AI builds upon the analytic maturity levels of operational reporting, insights and foresights, and augmented human intelligence to create autonomous analytics that power the products, services, policies, and processes that can continuously learn and adapt to the changing environment in which it operates. This continuous learning and adapting of AI-powered products, services, policies, and processes can happen with minimal human intervention, which makes it unlike any technology that we have ever seen.

To become a Citizen of Data Science, everyone must understand how AI and the AI utility function collaborate and hopefully, this chapter has helped with that. That’s fundamental to participating in the AI conversation and debate. But everyone also needs to understand their role in ensuring the development of a healthy AI utility function that guides the AI model to deliver meaningful, relevant...