Book SummaryHBR Guide to Data Analytics Basics for Managers, by Harvard Business Review

1-Page Summary1-Page Book Summary of HBR Guide to Data Analytics Basics for Managers

Beginning Data Analytics

This section introduces the importance of analytics for managers and provides an overview of working effectively with data scientists to become more data-driven.

Understanding Why Managers Must Learn About Analyzing Data

This section breaks down the necessity for people in managerial roles to go beyond simply relying on data experts.

Leaders Should Effectively Employ Data for Better Decisions

According to the authors of the HBR handbook on foundational data analytics for managers, companies are accumulating vast amounts of data that offer a range of opportunities for managers. Data enables improved forecasting of future events, identifying the reasons behind particular incidents, and developing understanding of elements influencing your sector or market. It can help guide choices about topics like hiring and developing new products.

However, the authors caution that simply having data is not enough; managers need to understand how to leverage it to inform their decisions. If you can't use data to guide your choices, then you'll only have numbers. It's swiftly becoming essential for all decision-makers to have foundational knowledge of analytics. It's not necessary to be a data scientist, but you need to understand how specialists employ data to arrive at their conclusions and how to optimally utilize that information to make decisions. You must be capable of posing the appropriate questions about datasets and conveying the outcomes to your peers and stakeholders convincingly and persuasively.

Practical Tips

• You can leverage data to make informed career decisions by tracking industry trends and job market analytics. Use free online tools like Google Trends to monitor the popularity of job roles and skills in your field. This can guide you in choosing which skills to develop or certifications to pursue, increasing your employability in areas where there's a growing demand.
• Create a personal incident diary to understand the reasons behind recurring problems in your daily life. Whenever an issue arises, such as consistently being late or forgetting important tasks, make a note of it along with the circumstances that led to it. Over time, analyze the entries to find common factors and develop strategies to prevent these incidents in the future.
• Use social media polls to gather consumer preferences before launching a new product. By posting questions related to potential features, design, or pricing on platforms like Instagram or Twitter, you can collect data that reflects what your audience is looking for. For example, if you're thinking of releasing a new line of eco-friendly water bottles, ask your followers to choose their preferred materials or design elements.
• Create a simple spreadsheet to analyze your monthly expenses and income. Use basic functions to categorize spending, calculate totals, and visualize the data with charts. This hands-on approach will familiarize you with data manipulation and interpretation, which are key analytics skills that can be transferred to decision-making in other areas.
• Engage with interactive online platforms that simulate data-driven decision-making in a gamified environment. These platforms often present you with scenarios where you must make choices based on the data provided, helping you understand the impact of data on real-life decisions without having to gather and analyze the data yourself.
• Develop a habit of asking "What does this data not show?" when analyzing datasets to uncover potential biases or gaps in information. This question prompts a deeper look into what might be missing from the data, leading to a more comprehensive understanding. For example, if you're reviewing sales data, consider what external factors, like seasonal trends or market changes, aren't represented that could...

HBR Guide to Data Analytics Basics for Managers Summary Collecting the Correct Data and Information

This section delves into the process of identifying the appropriate information needed for the analysis, emphasizing the importance of asking focused questions and understanding the various methods of data collection, including tests and split testing.

Identify the Questions Data Will Address

Before collecting data, you must figure out what information is required and how to acquire it.

Ask Actionable Questions For Insights, Not Just Information

Li, Perkins, and Kassengaliyeva recommend beginning with a clear understanding of your goals. Consider what you aim to accomplish by analyzing data and how you will use the insights to enhance your decision-making. Avoid ambiguities in your objectives, as even subtle ones can misdirect your analysis. For example, rather than inquiring, "How can we most effectively leverage advertisements to boost revenue?" you should ask “How do we maximize profits using ads?”, since driving sales might not always translate to higher profits.

Practical Tips

• Set up a 'goal accountability' partnership with a friend or family member. Share your goals with them and ask them to check in with you weekly on your progress. This creates a...

HBR Guide to Data Analytics Basics for Managers Summary Data Analysis

This section dives deeper into the heart of analyzing data. It discusses applying predictive analytics, understanding and using regression techniques, and navigating the common cognitive pitfalls that can distort decision making.

Understand the Basics of Forecasting Analysis

Predictive analysis helps anticipate future results by using past data, but relies heavily on the accuracy of data, statistical models, and model assumptions.

Importance of Accurate Data, Models, and Assumptions in Predictions

Davenport explains that predictive analytics uses historical data to forecast the future. This approach relies on high-quality data along with appropriate statistical models. However, one must be mindful of the assumptions underlying the frameworks. The most crucial presumption is that future patterns will mirror historical ones. Real-world behaviors are dynamic and may evolve over time, decreasing the reliability of older models.

Context

• The accuracy of predictions heavily depends on the quality of the data used. Clean, relevant, and up-to-date data is crucial for building reliable predictive models.
• Various software and tools, such as Python, R, and...

HBR Guide to Data Analytics Basics for Managers Summary Communicating Data Findings

Once you've evaluated the information, effectively conveying what you've discovered to key players is essential for driving change and securing buy-in for your decisions.

Make Data Meaningful With Successful Storytelling

Data by itself rarely convinces. To convince people, weave what you discover into compelling narratives they can resonate with.

Numbers Lack Voice—Craft a Compelling Story

Morgan argues that data alone cannot affect people's choices. To be persuasive, you must reach the subconscious, which is significantly influenced by emotions. While data provides valuable supporting evidence, it’s essential to incorporate it into emotionally powerful narratives to engage people on a subconscious level. People tend to be swayed more by engaging narratives than by dry, data-filled presentations.

Context

• Human decision-making is often influenced by cognitive biases, which are systematic patterns of deviation from norm or rationality in judgment. These biases can cause people to rely more on stories and emotions than on objective data.
• Neuroscience research shows that emotional responses can activate the brain's reward system, reinforcing certain...

HBR Guide to Data Analytics Basics for Managers Summary Hiring and Working With Data Scientists

This section focuses on understanding the unique skills data scientists have and how to effectively harness their capabilities within organizations.

Unique Skills and Mindset of Analytics Professionals

Data professionals possess a unique blend of analytical, technical, and communication skills that make them valuable assets for navigating the complexities of massive datasets.

Data Professionals Combine Coding, Analytics, Communication, and Savvy Business Skills

Davenport and Patil say data scientists are a blend of a data hacker, an analyst, a communicator, and a trusted adviser. Their core skills involve data collection, analysis, visualization, and communication. They excel at uncovering and interpreting patterns in large and often unstructured data sets. Beyond technical skills, they possess a strong sense of curiosity, the ability to think associatively, and a knack for using data to tell stories.

Other Perspectives

• The emphasis on combining these skills might overlook the importance of teamwork and collaboration, where different team members contribute different expertise.
• Being a "trusted adviser" suggests a level of seniority and experience...