This is a summary of the book titled “Be Data Analytical: How to use analytics to turn data into value” written by Jordan Morrow and published by Kogan Page in 2023. The author is a data expert who empowers organizations by elevating their data literacy levels and supporting an ethos of curiosity and experimentation. He argues that decision making must comprise of both human intuition and data analytics. A data driven culture that supports curiosity and experimentation must be nurtured. Descriptive analytics must capture and communicate meaningful patterns and trends. Outperform your competition with diagnostic analytics to uncover root causes. Explore multiple outcomes with predictive analytics to improve strategic decision making. Build better descriptive, diagnostic, predictive and prescriptive analytics in six steps. Apply your data and analytics mindset to your life.

Data-driven activities involve leveraging data and analytics to assist in decision-making, allowing individuals and organizations to make better data-informed decisions. To improve decision capabilities, progress through four levels of analytics: descriptive, diagnostic, predictive, and prescriptive. Nurture a data-driven culture that supports curiosity and experimentation, aiming to build a "data and analytics mindset" that encourages experimentation and making mistakes.

Data-driven cultures should align with data ethics, embracing transparency and questioning data rigorously. Descriptive analytics can be used to capture and communicate meaningful patterns and trends, with various roles playing in generating data. Data analysts, data scientists, data architects, and leaders can all contribute to generating descriptive analytics.

To create a data-driven culture, embrace the democratization of data, giving everyone access to the information they need. By embracing data ethics, embracing transparency, and fostering a culture of data literacy, organizations can effectively problem-solve effectively with data.

Diagnostic analytics is a crucial tool for organizations to uncover root causes and make informed decisions. It helps organizations understand the reasons behind various phenomena, enabling them to make more informed decisions. This can be achieved using tools like Tableau, Microsoft Power BI, and Qlik, as well as coding languages like R and Python. Predictive analytics is another powerful tool for strategic decision-making, allowing organizations to anticipate supply-chain challenges and forecast credit card delinquency rates. Leaders play a significant role in driving better predictive analytics, requiring data literacy and data-driven decision-making. Data science platforms like RapidMiner can be used to perform predictive analytics, allowing users to understand data visually. While not everyone in the organization will build predictive analytics, democratizing predictions can ensure the right parties have access to the necessary information. Prescriptive analytics, which uses machine learning to make recommendations and create action steps, can also be beneficial. However, it's important to remember that predictions are not prophecies and should be communicated clearly.

Prescriptive analytics is a powerful tool that can be used to make decisions based on patterns and trends. However, it is essential to maintain the human element in analytics, as it allows for the freedom to change your workout regimen and downsize your company. Everyone at your company plays a role in building these analytics, from C-suite executives to data analysts, engineers, and data scientists. To build better analytics, follow six steps:

1. Awareness: Ensure staff are familiar with the four levels of analytics, their problems, and solutions.

2. Understanding: Understand how each phase of data analytics fits within the bigger picture, helping you achieve broader goals.

3. Assessing: Evaluate personal skills and the organization as a whole, identifying gaps to fill.

4. Questioning: Improve each phase of analytics by asking questions about data quality, purpose, and future implications.

5. Learning: Gain data literacy and improve problem-solving abilities.

6. Implementation: Don't waste valuable insights and execute data-informed decisions.

Applying a data and analytics mindset to your life is crucial, as failures present opportunities to improve and refine your approach to data analytics.

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