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We all know data is growing at a rapid pace and our ability to store it has become cheap. But what do we do with all this data? Do you feel overwhelmed with the infinite amounts of decisions you could make?

Big data was supposed to improve how businesses run, though in most cases it has complicated process. We have become apathetic to the amounts of data that are bombarding us.

This talk aims to help overcome this apathy towards the sheer amount of information out there. It will teach you how to come up with methods to finding metrics, and new dashboards specifically for your business.

We will talk about:

Backward induction, or goal based metrics.
   Classification algorithms for making sense of data.
   Graphing in higher dimensions.

By the end of this session, you will know how to be effective with data. You will understand how to find what you are looking for, how to classify it, and how to visually make sense of it all.

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In the talk titled 'How to Overcome the Apathy of Big Data,' Matt Kirk addresses the growing issue of data overload that many individuals and businesses face. He argues that while big data was intended to enhance business processes, it often leads to confusion and inefficiency due to the sheer volume of information available. Kirk introduces various strategies to combat this apathy towards data and emphasizes finding relevant metrics to yield meaningful insights.

Key points discussed in the video include:

- **Data Hoarding and Apathy:** Kirk starts with an anecdote about his grandmother's tendency to hoard information, illustrating that many individuals and organizations struggle with managing excessive data due to the fear of missing something valuable.
- **Understanding Valuable Data:** He introduces the 80/20 principle, emphasizing the importance of identifying the critical 20% of data that drives 80% of outcomes.
- **Backward Induction:** A focus on this game theory tactic helps in setting goals before analyzing data. By considering desired outcomes first, organizations can streamline their data efforts effectively.
- **Visualization Techniques:** Kirk discusses the importance of data visualization, mentioning techniques such as color tables, scatter plot matrices, and Chernoff faces. These tools help interpret complex datasets by allowing clearer insights into relationships among variables.
- **Classification Trees and Variable Selection:** He explains how using machine learning concepts like classification trees allows users to identify which variables influence outcomes significantly, thereby reducing noise in data analysis.

Throughout the presentation, Kirk employs relevant examples, such as his personal experiences with Twitter data analysis, where he discovered key influences on retweeting behavior by applying classification tree techniques. He underscores the essence of focusing on how data is used rather than the amount of data collected.

In conclusion, Kirk encourages audiences to approach data with a mindset oriented towards utility and clarity, rather than accumulation. The ultimate takeaway is to prioritize effective data management techniques to harness the potential of big data, ensuring that it serves meaningful decision-making rather than overwhelming individuals and organizations.

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