Proven Strategies to Becoming Data Analytical by Jordan Morrow

How to Use Analytics to Turn Data into Value. In “Be Data Analytical,” data literacy expert Jordan Morrow provides an empowering roadmap for unleashing the full potential of data analytics. This groundbreaking book is a must-read for anyone looking to harness the power of data to drive better decision making and achieve remarkable results. Keep reading to discover the key insights and strategies that will transform the way you approach data.

Dive into the pages of “Be Data Analytical” and embark on a transformative journey that will elevate your data skills to new heights. Don’t miss out on this opportunity to gain a competitive edge in today’s data-driven world.

Genres

Business, Non-fiction, Self-help, Technology, Data Science, Analytics, Professional Development, Leadership, Decision Making, Personal Growth

“Be Data Analytical” is a comprehensive guide that demystifies the world of data analytics and equips readers with the tools and mindset needed to leverage data effectively. Jordan Morrow, a renowned data literacy expert, breaks down complex concepts into easily digestible lessons, making data analytics accessible to readers from all backgrounds.

The book begins by establishing the fundamental principles of data literacy and emphasizing the importance of developing a data-driven mindset. Morrow introduces the Data Literacy Framework, a structured approach to understanding and applying data analytics. He guides readers through each stage of the framework, from identifying relevant data sources to interpreting results and making data-informed decisions.

Throughout the book, Morrow shares real-world examples and case studies that illustrate the transformative power of data analytics across various industries. He provides practical tips and exercises that enable readers to apply the concepts directly to their own projects and challenges.

One of the strengths of “Be Data Analytical” is its focus on the human aspect of data analytics. Morrow emphasizes the importance of effective communication, collaboration, and storytelling when working with data. He provides strategies for presenting data insights in a compelling and persuasive manner, enabling readers to influence decision-making at all levels of an organization.

The book also addresses common challenges and pitfalls in data analytics, such as data bias, privacy concerns, and ethical considerations. Morrow offers guidance on navigating these issues and ensuring that data is used responsibly and ethically.

In the later chapters, Morrow delves into advanced topics such as predictive analytics, machine learning, and data visualization. He provides a solid foundation for readers to explore these areas further and incorporates practical examples to demonstrate their application.

Review

“Be Data Analytical” is an exceptional resource for anyone seeking to enhance their data literacy and harness the power of data analytics. Jordan Morrow’s engaging writing style and clear explanations make complex concepts accessible to readers from all backgrounds.

The book’s structured approach, based on the Data Literacy Framework, provides a solid foundation for understanding and applying data analytics. Morrow’s emphasis on the human aspect of data analytics sets this book apart, as he recognizes that effective communication and collaboration are essential for driving data-informed decision-making.

The real-world examples and case studies included throughout the book bring the concepts to life and demonstrate the practical applications of data analytics across various industries. The exercises and tips provided allow readers to immediately put their learning into practice.

While the book covers a wide range of topics, it strikes a good balance between breadth and depth. Morrow provides enough detail to give readers a comprehensive understanding of each concept without overwhelming them with technical jargon.

One potential improvement could be the inclusion of more visual aids, such as diagrams or infographics, to enhance the learning experience. However, this does not detract from the overall quality and value of the book.

In conclusion, “Be Data Analytical” is a must-read for anyone looking to enhance their data literacy and leverage the power of data analytics. Jordan Morrow’s insightful and pragmatic approach makes this book an invaluable resource for professionals, students, and decision-makers alike. It provides a solid foundation for understanding data analytics and offers practical strategies for driving data-informed decision-making in any organization.

Recommendation

Perhaps you have the skills to spot problems at your organization, but you don’t know why they’re happening or how to change them. In this useful guide to data analytics, you’ll learn the basics of fostering an iterative data-driven culture at your business, and how to take your analytics to the next level by building descriptive, diagnostic, predictive, and prescriptive capacities. Data expert Jordan Morrow guides leaders to transform their companies by elevating data literacy levels and supporting an ethos of curiosity and experimentation.

Take-Aways

• Human intuition and data analytics should work together to inform decision making.
• Nurture a data-driven culture that supports curiosity and experimentation.
• Use descriptive analytics to 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.
• Harness the power of prescriptive analytics without abandoning the human element.
• Build better descriptive, diagnostic, predictive, and prescriptive analytics in six steps.
• Apply your data and analytics mindset to your life.

Summary

Human intuition and data analytics should work together to inform decision making.

People tend to overcomplicate what being “data-driven” truly means. Data-driven activities are those that leverage data and analytics to assist in making decisions. Some may incorrectly assume that data-driven decisions means cutting humans out of the process. But you’d be wrong to try to replace the human part of decision-making processes entirely with mechanical data analysis, as your intuition can help guide you in making choices that best serve you and your organization. Ultimately, both human and data elements should function together to help individuals and entities make better data-informed decisions.

“What is the purpose of data and analytics? It is to help us make better decisions and to achieve our personal goals, or to help organizations achieve their goals.”

Improve your data-driven decision capabilities by progressing through four levels of analytics:

1. Descriptive analytics — This refers to the use of data to simply describe a problem. When making decisions, descriptive analytics can help you identify your starting point.
2. Diagnostic data analytics — In this stage, you identify the root causes of the problem.
3. Predictive data analytics — You describe what will likely occur if things continue along the same or a different course, helping people visualize or conceptualize the impacts of different possible actions.
4. Prescriptive data analytics — Leverage prescriptive analytics to suggest the best course of action to take to arrive at your desired outcome.

Nurture a data-driven culture that supports curiosity and experimentation.

Aspire to build a culture of data literacy in which team members develop a “data and analytics mindset.” Such a mindset is inquisitive, questioning, and iterative. To glean better data insights and support data-driven innovation, people need to feel that it’s okay to experiment and make mistakes. Each phase of the data analytics process requires a questioning approach. In the descriptive phase, you’re asking what happened; in the diagnostic phase, you’re asking why it happened. The predictive phase leads to querying what could happen, and the prescriptive phase gets you to understand what you should do to obtain your desired outcome.

“Data literacy and being data driven are not just about the data and technology. On the contrary, mindset and dealing with people probably matter more.”

Data-driven cultures should proactively align with data ethics, as opposed to blindly trusting machine learning, AI, and algorithms. Embrace transparency, which entails understanding the mechanics behind data predictions, rather than simply treating algorithms as “black boxes.” It’s vital that you become aware of, and remove, your own biases from data analytics, questioning data rigorously in the pursuit of objectivity.

Use descriptive analytics to capture and communicate meaningful patterns and trends.

You can conduct descriptive analytics in many ways, ranging from identifying your monthly average users to examining the weather. People play multiple roles in generating descriptive analytics: End or business users, such as marketing analysts, are “non-data professionals” who receive data analytics and use them to share insights and creative ideas with organizations. Data analysts build visualizations and dashboards for the end-users, helping people spot meaningful patterns and trends. Data scientists use the scientific method to approach data from a more complex perspective, communicating their findings via simplified descriptive analytics. Data architects and engineers build the back-end structures that enable people on the front-end to perform descriptive analytics. And leaders guide people in performing all data-related roles, enabling information sharing of key performance indicators (KPIs).

“If we want to problem-solve effectively with data, we must have a solid understanding and foundation of what is happening.”

To create a data-driven culture, embrace the democratization of data, giving everyone access to the information they need to perform their roles, with a strong data governance program in place. Improve your descriptive analytics by prioritizing the “tridata,” which refers to the elements required for better problem solving, decision making and execution. Providing data visualizations can help with problem solving, while capturing descriptive analytics can support decision making, and sharing data and metrics in a concise, clear way can aid your team in implementing data-informed decisions. Focus on sharing only the most relevant information by bullet-pointing stories and highlighting important details, and remember to always consider the needs and data literacy levels of your audience.

Outperform your competition with diagnostic analytics to uncover root causes.

Most organizations are stuck doing descriptive analytics. People can view dashboards featuring KPIs, but nobody’s providing actionable analytics. Imagine how frustrating it would be if a doctor were to behave this way, telling you only that you are sick but not explaining why you are sick, what you should expect, and how to treat your illness. It’s essential that you move on to the stage of building diagnostic analytics, because if you don’t understand why something is happening, how can you make predictions? Diagnostic analytics have a number of industry-specific uses.For example, in the health care industry, diagnostic analytics can help you understand the rate of patients filling hospital beds during a pandemic, to better make predictions about the number of future beds you’ll need to have available.

“Diagnostic analytics is power, without a doubt. Knowing ‘why’ things are occurring or having good ideas on it should empower us to make smarter decisions.”

You can use the same business intelligence tools when conducting diagnostic analytics that you use for descriptive analytics, which include Tableau, Microsoft Power BI, and Qlik. Coding languages, such as R and Python, can help you dig even deeper into your data to gain insights. For example, an engineer might write code to pull data in response to requests for descriptive analytics, which may trigger follow-up questions and lead to diagnostic insights. Your organization can make inferences into broader population trends by harnessing probability statistics. Remember, though, that correlation and causation aren’t the same.

Explore multiple outcomes with predictive analytics to improve strategic decision making.

The uses of predictive data analytics are many, including predictive modeling to anticipate supply-chain challenges and to forecast credit card delinquency rates. Leaders have a particularly big role to play in driving better predictive analytics. As major decision makers steer their organizations, they must be data-literate enough to both act on predictions and make predictions themselves. Business or end users must have the capacity to ask the right questions, prompting those in more technical roles to work toward building better predictions. Data scientists should aspire to create the best statistical models to enable organizations to make predictions, while those working in back-end roles can ensure that those on the front-end offer more accurate forecasts by sourcing quality data. Be sure to integrate your predictive analytics with your descriptive and diagnostic analytics to create relevant, powerful predictions.

“We don’t want to just build predictive analytics for the sake of predictive analytics. Instead, we want to build targeted predictive analytics when we have utilized the descriptive analytics to understand the ‘what’ and then diagnostic analytics for the ‘why’.”

You don’t need to know how to code to perform predictive analytics. Data science platforms such as RapidMiner enable users to understand data in a visual manner, and the platforms are suitable for individuals with varying levels of data fluency. While not everyone at your business will build predictive analytics, democratizing predictions themselves can ensure the appropriate parties within your organization have access to the information they need. Use predictive analytics to explore the potential rewards and consequences of different choices. When sharing data-driven decisions and predictions, work to ensure that your organization trusts that you built your predictions well and that you communicate decisions with clarity. Confirm that people understand the different potential outcomes you’ve identified. It’s important, however, to remember that predictions are not prophecies.

Harness the power of prescriptive analytics without abandoning the human element.

Machines are integral to the prescriptive analytics process. Machine learning makes recommendations and creates action steps based on patterns and trends. But it’s crucial to maintain the human element in analytics. For instance, feeding your health and workout data into a program that tells you exactly what to eat, how much to work out, and how much water to drink could be helpful, but you’d probably like to maintain the freedom to change your workout regimen. Likewise, on a business level, prescriptive analytics might suggest you downsize your company. While you may feel relieved to be able to justify this difficult decision with machine-generated analytics, don’t forget that you have agency in choosing how to act on data insights.

“Curiosity is one of the most important aspects of data and analytical work. Questions spark the race. Questions spark the ideas.”

Even though prescriptive analytics are an advanced technical field, there’s a role for everyone at your company in building them: C-suite executives and business users may leverage prescriptive analytics to make decisions. Data analysts combine descriptive and prescriptive analytics to create powerful data stories. And engineers and data scientists can monitor machine learning and tweak the algorithms as needed. Embrace the power of data storytelling, presenting prescriptive analytics within a holistic picture, integrating all four levels of data analysis. Create space for two crucial forms of questioning when sharing prescriptive analytics: Invite team members to partake in “predecision questioning,” creating open discussion forums in which individuals feel comfortable asking questions about the inner workings of your algorithms and your leadership’s approach to data-driven decision making. And make time for “postdecision questioning,” communicating openly with your team after you’ve made choices that required speed.

Build better descriptive, diagnostic, predictive, and prescriptive analytics in six steps.

Put all four levels of data-driven decision making together with the following six steps of “analytical progression.”

1. “Awareness” — Ensure that staff at your organization are familiar with the four levels of analytics, as well as the problems you’re trying to tackle and the solutions you’re looking for.
2. “Understanding” — Team members should comprehend how each phase of data analytics fits within the bigger picture, helping you achieve your broader goals. Work toward an aligned understanding about what descriptive analytics you used, why you used them, and the predictive and prescriptive process behind your decision making.
3. “Assessing” — Individuals should evaluate their personal skills, and leaders should assess the skills of the organization as a whole, identifying gaps to fill to build better data analytics. Aspire to increase data literacy, so that it permeates throughout your entire organization.
4. “Questioning” — Each phase of analytics is improved by questions such as: “Do we have enough data to help us gain a better understanding?”; “Why do we even care about this data, what is the point?”; “What does this mean for the future?”; and “How can I bring my thoughts and gut feel to this prescriptive analytic?”
5. “Learning” — Embrace the two facets of learning: to gain data literacy and build knowledge, and to improve your problem-solving abilities.
6. “Implementation” — Don’t waste valuable insights. Be sure to execute your data-informed decisions.

Apply your data and analytics mindset to your life.

When building data analytics, remember that you aren’t always going to get it right. Sometimes, your predictive analytics may contain errors, and you may get it totally wrong — and that’s okay. Commit to an iterative process of continuous learning, understanding that every failure presents you with an opportunity to improve and refine your approach to data analytics.

“Fail often and learn from it, keep the journey rolling.”

Once you develop your own data literacy and understanding of data analytics, you may begin thinking more analytically about your life in general. You may find yourself questioning why the aspects you encounter in life are the way they are. Take the time to explore situations more deeply and with more clarity. Start applying an iterative approach to your everyday life, understanding that while you may not always like the experiences you’re having, you can still learn and grow from them.

About the Author

Jordan Morrow is the author of Be Data Literate and Be Data Driven, as well as the vice president and head of data and analytics at BrainStorm. He previously served as the Data Literacy Project’s advisory board chair.