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Behind Every Good Decision Lib/E: How Anyone Can Use Business Analytics to Turn Data Into Profitable Insight
There is a costly misconception in business today--that the only data that matters is BIG data, and that complex tools and data scientists are required to extract any practical information. Nothing could be further from the truth.
In Behind Every Good Decision, authors and analytics experts Piyanka Jain and Puneet Sharma demonstrate how professionals at any level can take the information at their disposal and leverage it to make better decisions. The authors' streamlined frame work demystifies the process of business analytics and helps anyone move from data to decisions in just five steps...using only Excel as a tool. Readers will learn how to:
* Clarify the business question * Lay out a hypothesis-driven plan * Pull relevant data * Convert it to insights * Make decisions that make an impact
Packed with examples and exercises, this refreshingly accessible book explains the four fundamental analytic techniques that can help solve a surprising 80% of all business problems. Business analytics isn't rocket science--it's a simple problem-solving tool that can help companies increase revenue, decrease costs, improve products, and delight customers. And who doesn't want to do that?
In Behind Every Good Decision, authors and analytics experts Piyanka Jain and Puneet Sharma demonstrate how professionals at any level can take the information at their disposal and leverage it to make better decisions. The authors' streamlined frame work demystifies the process of business analytics and helps anyone move from data to decisions in just five steps...using only Excel as a tool. Readers will learn how to:
* Clarify the business question * Lay out a hypothesis-driven plan * Pull relevant data * Convert it to insights * Make decisions that make an impact
Packed with examples and exercises, this refreshingly accessible book explains the four fundamental analytic techniques that can help solve a surprising 80% of all business problems. Business analytics isn't rocket science--it's a simple problem-solving tool that can help companies increase revenue, decrease costs, improve products, and delight customers. And who doesn't want to do that?
Audio CD
First published January 1, 2014
165 people are currently reading
575 people want to read
About the author
Piyanka Jain
5 books3 followersPIYANKA JAIN is President and CEO of Aryng, a management consulting company focused on analytics for business impact.
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Displaying 1 - 14 of 14 reviews
October 22, 2015
This does not try to cover all aspects of analytics use in business. Instead, it does a reasonable job of defining the basic types of analytics, then focuses on the work processes involved in doing analysis using analytics. The book provides useful suggestions, many concerning the political environment involved in the use of analytics within a company. The final chapter provides a number of short examples, almost like miniature case studies, where analytics are used. Company names are provided, along with the type of work accomplished and the results. Overall, this short book reads like a popular business magazine in terms of level of writing and tone. This is not a technical book – if you are looking for technical how-to’s, look elsewhere. I listened to this on audio. There are quite a few spoken lists of terms that really are not explained. I assumed these were charts in the book that were read out, but they really just sounded like a collection of buzzwords and did not translate into the audio environment. Given this, I would suspect a written version would be more intelligible than the audio. Overall, good for an introduction, good for those tasked with building an analytics team within a company, and contains a good but short section of case studies.
November 29, 2019
I give this book 3.5 stars. The premise is using the BADIR (TM) model:
* Business Question
* Analysis Plan
* Data Collection
* Insights
* Recommendations
The first half of the book provided a good overview of data analytics and application of a structured method (like conducting a scientific study where a hypothesis is formed before collecting data and making a conclusion). The second half was a mix of examples and actual business cases from the author. The transition was poor and the switch back and forth to different examples made it confusing to follow.
* Business Question
* Analysis Plan
* Data Collection
* Insights
* Recommendations
The first half of the book provided a good overview of data analytics and application of a structured method (like conducting a scientific study where a hypothesis is formed before collecting data and making a conclusion). The second half was a mix of examples and actual business cases from the author. The transition was poor and the switch back and forth to different examples made it confusing to follow.
July 27, 2025
If you are my friend and seeing this review, it’s important to mention that I picked this up as part of the 75 Hard challenge requirement to read a non-fiction book. I don’t recommend reading unless you are interested in going into a field where business analytics will be the focus. Otherwise, only the last few chapters were beneficial in covering case studies and how to implement analytics into different leadership situations.
July 10, 2021
Main Highlights:
- Page 3 Section I is an introduction to analytics: the what, why, and common methodologies. Section III is about building a data-driven organization. Section IV contains real-life stories on how analytics has catapulted typical business environments—from politics to sports to law enforcement to technology.
- Page 17 Here’s a bold statement. Every company is exposed to the very same factors to virtually the same degree. It is not the challenge that distinguishes the success of an organization, but its response to the challenge.
- Page 52 This chapter introduces the BADIR framework that can take you from Data to Decisions using a set of five lean, streamlined steps that can address 80 percent of business problems using simple, yet powerful, analytics. BADIR stands for Business question, Analysis plan, Data collection, Insights, and Recommendations
- Page 63 People often think that hypotheses come from data, but that is not true. Hypotheses are best generated through a brainstorming session with all the key stakeholders based on what they think may be driving the situation.
- Page 64 Two separate meetings are recommended: 1. The first should generate the hypotheses. 2. The second should prioritize them.
- Page 191 Overreliance on external consultants also signals to your internal resources that you do not trust their capabilities, which, needless to say, does not help morale.
- - Page 224 Z-score This is the technical term for describing how many standard deviations a data value is from the mean. It is used to evaluate whether a particular point is typical or atypical.
- - Page 224 Since she is more than two standard deviations away from the mean, she is not your typical customer. Let’s say Mary is willing to splurge and asks for extra amenities that you don’t currently have. Just because you know she is not a typical customer, you may not need to start investing in those extra amenities unless many of your typical customers start asking for it.
All Highlights:
- Page 1 Not all analytics problems need to be megacomplex projects with complex models built and read by a data scientist. In fact, 80 percent of these problems can be addressed day to day by managers and decision makers who have had the right exposure to simple tools and methods. They will know when and how best to leverage data scientists and analysts to solve more complex business issues. This book is designed to prime these business professionals with the basics and help data scientists deliver at their best. It seeks to marry the best of both worlds to drive results.
- Page 3 Section I is an introduction to analytics: the what, why, and common methodologies. Section III is about building a data-driven organization. Section IV contains real-life stories on how analytics has catapulted typical business environments—from politics to sports to law enforcement to technology.
- Page 10 Analytics is the science of applying a structured method to solve a business problem using data and analysis to drive impact.
- Page 10 Intuition + Data = Powerful insights → Good decision
- Page 12 With 127,000 employees worldwide and 300 brands sold in 180 countries, how has P&G managed to lead the household goods industry over the 177 years of its existence—since 1837? This $140 billion conglomerate consumer goods company has contextually reinvented itself over time using a hypothesis-driven analytics strategy to stay relevant and fuel the edge.
- Page 14 P&G’s analytics is driven by a cultural mindset to ask questions and propose hypotheses before delving into complex data analytics.
- Page 17 Here’s a bold statement. Every company is exposed to the very same factors to virtually the same degree. It is not the challenge that distinguishes the success of an organization, but its response to the challenge.
- Page 25 You’ll also be surprised to know that it is actually the people skills needed for bridging the gap between business and math that make or break the deal.
- Page 27 The truth, however, is that only 20 to 30 percent of decisions really require the use of advanced techniques like predictive analytics. Seventy to 80 percent of business decisions can be judiciously addressed with business analytics, or simple analytics techniques, which can be learned by any professional and executed in an Excel spreadsheet.
- Page 28 Analytics, on the other hand, is performed on data delivered by business intelligence. Analytics then convert the data to insights, decisions, actions, and, eventually, revenue or other impact.
- Page 29 Analytics looks at a business event and analyzes the historical data to come up with insights. Testing, on the other hand, is a controlled experiment conducted when you do not have historical data on which to base a decision.
- Page 30 Additionally, analytics can only prove a relationship (AÑÒB), whereas testing can prove causation (AÒB).
- Page 32 Big Data is often explained using three Vs, where a very high Volume of data with lot of Variety is flowing at a high Velocity.
- Page 36 EXHIBIT 3-1. Seven Most Common Analytics Methodologies (Top 4 are the Most Commonly Used) METHODOLOGY DESCRIPTION APPLICATION Aggregate Analysis Used to describe a population or a segment or to compare two segments. Descriptive analysis, profiling, campaign analysis, winner-loser analysis. Correlation Analysis Looks for the relationship between two or more things with the prospect of being able to explain or drive one with other. Pre and post, test-control, drivers, dashboard. Trends Analysis Aggregate or correlation analysis over time, that is, trends over a period of time. Trends of sales, revenues, breaks in trend and segments or drivers over a period of time. Sizing/Estimation Structured approach to make a near-accurate guesstimate in the absence of historical data. Business case with limited internal data or one that is dependent on external data and assumptions. Predictive Analytics/Time Series Looks at both current and historical data to make predictions about future events. Drivers of conversion or consumer engage- ment, forecasting. Segmentation Groups customers or products into meaningful segments, usually to enable better targeting for the purpose of driving higher value through customization. Grouping customers or products for targeting and customization. Customer Life Cycle Looks at the different stages of the purchase process to determine what stage a group of customers is at and decide how to move them up to the next stage in the purchase process. Customer progress stages from consideration through purchase to use, sales funnel.
- Page 45 While this may seem like a compelling case for predictive analytics to some, we think a CMO can realize a better ROI using simple business analytics techniques to arrive at insightful and informed decisions.
- Page 48 In most cases, simple business analytics can give you a scale of efficiency, instead of waiting for the analytics team to get around to answering your question. The data you will need, most likely, is already at hand; you just have to dig it out and look at it.
- Page 52 This chapter introduces the BADIR framework that can take you from Data to Decisions using a set of five lean, streamlined steps that can address 80 percent of business problems using simple, yet powerful, analytics. BADIR stands for Business question, Analysis plan, Data collection, Insights, and Recommendations
- Page 54 You may notice right away that even though BADIR is a process about data analytics, it does not start with data. This framework starts with an understanding of the real business question that the data needs to answer.
- Page 57 If you find that the stakeholders are unable to take action based on the findings, then this analysis doesn’t need to be done.
- Page 63 People often think that hypotheses come from data, but that is not true. Hypotheses are best generated through a brainstorming session with all the key stakeholders based on what they think may be driving the situation.
- Page 64 Two separate meetings are recommended: 1. The first should generate the hypotheses. 2. The second should prioritize them.
- Page 94 Without question, you need to know the details, but keep them to yourself unless asked.
- Page 126 Learn to program in R (a free statistical package) with free online courses by portals like Coursera.
- Page 137 Our tool of choice for simple analytics is, drumroll, Microsoft Excel. It is the spreadsheet application in the Microsoft Office package. It is by far the most widely used tool for business analytics and has seen some powerful improvements and plug-ins in recent years that have enabled more sophisticated analysis. Excel is ideal for business users, who do not handle large datasets or perform complex analytics.
- Page 145 Your organization needs to be strong in four areas for you to successfully leverage analytics: leadership, analytics talent, decision making, and data maturity.
- Page 177 In a cross-functional organization, your ability to influence starts by recognizing these basic principles: • Making decisions collaboratively and leveraging others’ domain expertise is more effective than making decisions as an individual. • Engaging stakeholders from the start will accelerate execution, whether they are your execution partners or budget approvers.
- Page 178 Boosting your influence starts with building alignment with stakeholders at the outset of an initiative. There are four stages of any initiative or project (see Exhibit 10-1): 1. Vision: Define “that” which you want to drive the team toward. 2. Plan: Lay out a plan detailing scope, stakeholders, resources, and timelines. 3. Execute: Act according to the plan, and adjust it in flight if required. 4. Learn: Learn from this initiative and feed the results into your next project.
- Page 191 Overreliance on external consultants also signals to your internal resources that you do not trust their capabilities, which, needless to say, does not help morale.
- Page 199 However, you can easily get distracted by supporting small-to-medium impact projects—collecting a series of wins with minimal impact. Good analytics leaders have to think big and become involved in the strategic discussion.
- Page 211 Impact: In the first 7 years of the program, from 2005 to 2012, violent crime decreased by 23 percent and burglaries in Memphis dropped five times more than the national average. An IBM case study estimated that Memphis achieved an 863 percent return on its investment, calculated as the cost of additional forces that would have been required to bring about this decline in crime without the help of analytics.
- Page 216 Good vs. great managers: “Project Oxygen” analyzed a ton of data to determine that great managers were critical for retention and top performance of the workforce. The analyses also identified eight characteristics of great managers as opposed to just good managers. These included periodic one-on-one coaching and frequent personalized feedback, which were valued much more than technical knowledge. Today, managers are rated twice a year by their employees on those eight factors.
- Page 217 Impact: Google employees have an amazing workforce productivity that few can match. On average each employee generates $1 million in revenue each year.
- Page 217 Case Study 8: Reversing the High School Dropout Rate in Hamilton County
- Page 222 Mode is the number that appears most frequently in a set of numbers. This behavior is called a modality. In a nonnormal distribution, mode is a better representation of average than mean or median. For example, if you look at the number of cars on the road, it is usually nonnormal bimodal distribution, where the numbers peak around 8 AM and 5 PM.
- Page 224 Z-score This is the technical term for describing how many standard deviations a data value is from the mean. It is used to evaluate whether a particular point is typical or atypical.
- Page 224 Since she is more than two standard deviations away from the mean, she is not your typical customer. Let’s say Mary is willing to splurge and asks for extra amenities that you don’t currently have. Just because you know she is not a typical customer, you may not need to start investing in those extra amenities unless many of your typical customers start asking for it.
- Page 226 Error is calculated using the formulas given below. Error for difference between two sample means (e.g., average age comparison between two samples) is:
- Page 227 CORRELATION This is the statistical measure of the linear relationship between two or more random variables and is represented by r (the Pearson correlation coefficient) with values between + 1 and –1. The closer the value is to + 1 or –1, the higher is the correlation. No correlation is indicated by r = 0.
- Page 3 Section I is an introduction to analytics: the what, why, and common methodologies. Section III is about building a data-driven organization. Section IV contains real-life stories on how analytics has catapulted typical business environments—from politics to sports to law enforcement to technology.
- Page 17 Here’s a bold statement. Every company is exposed to the very same factors to virtually the same degree. It is not the challenge that distinguishes the success of an organization, but its response to the challenge.
- Page 52 This chapter introduces the BADIR framework that can take you from Data to Decisions using a set of five lean, streamlined steps that can address 80 percent of business problems using simple, yet powerful, analytics. BADIR stands for Business question, Analysis plan, Data collection, Insights, and Recommendations
- Page 63 People often think that hypotheses come from data, but that is not true. Hypotheses are best generated through a brainstorming session with all the key stakeholders based on what they think may be driving the situation.
- Page 64 Two separate meetings are recommended: 1. The first should generate the hypotheses. 2. The second should prioritize them.
- Page 191 Overreliance on external consultants also signals to your internal resources that you do not trust their capabilities, which, needless to say, does not help morale.
- - Page 224 Z-score This is the technical term for describing how many standard deviations a data value is from the mean. It is used to evaluate whether a particular point is typical or atypical.
- - Page 224 Since she is more than two standard deviations away from the mean, she is not your typical customer. Let’s say Mary is willing to splurge and asks for extra amenities that you don’t currently have. Just because you know she is not a typical customer, you may not need to start investing in those extra amenities unless many of your typical customers start asking for it.
All Highlights:
- Page 1 Not all analytics problems need to be megacomplex projects with complex models built and read by a data scientist. In fact, 80 percent of these problems can be addressed day to day by managers and decision makers who have had the right exposure to simple tools and methods. They will know when and how best to leverage data scientists and analysts to solve more complex business issues. This book is designed to prime these business professionals with the basics and help data scientists deliver at their best. It seeks to marry the best of both worlds to drive results.
- Page 3 Section I is an introduction to analytics: the what, why, and common methodologies. Section III is about building a data-driven organization. Section IV contains real-life stories on how analytics has catapulted typical business environments—from politics to sports to law enforcement to technology.
- Page 10 Analytics is the science of applying a structured method to solve a business problem using data and analysis to drive impact.
- Page 10 Intuition + Data = Powerful insights → Good decision
- Page 12 With 127,000 employees worldwide and 300 brands sold in 180 countries, how has P&G managed to lead the household goods industry over the 177 years of its existence—since 1837? This $140 billion conglomerate consumer goods company has contextually reinvented itself over time using a hypothesis-driven analytics strategy to stay relevant and fuel the edge.
- Page 14 P&G’s analytics is driven by a cultural mindset to ask questions and propose hypotheses before delving into complex data analytics.
- Page 17 Here’s a bold statement. Every company is exposed to the very same factors to virtually the same degree. It is not the challenge that distinguishes the success of an organization, but its response to the challenge.
- Page 25 You’ll also be surprised to know that it is actually the people skills needed for bridging the gap between business and math that make or break the deal.
- Page 27 The truth, however, is that only 20 to 30 percent of decisions really require the use of advanced techniques like predictive analytics. Seventy to 80 percent of business decisions can be judiciously addressed with business analytics, or simple analytics techniques, which can be learned by any professional and executed in an Excel spreadsheet.
- Page 28 Analytics, on the other hand, is performed on data delivered by business intelligence. Analytics then convert the data to insights, decisions, actions, and, eventually, revenue or other impact.
- Page 29 Analytics looks at a business event and analyzes the historical data to come up with insights. Testing, on the other hand, is a controlled experiment conducted when you do not have historical data on which to base a decision.
- Page 30 Additionally, analytics can only prove a relationship (AÑÒB), whereas testing can prove causation (AÒB).
- Page 32 Big Data is often explained using three Vs, where a very high Volume of data with lot of Variety is flowing at a high Velocity.
- Page 36 EXHIBIT 3-1. Seven Most Common Analytics Methodologies (Top 4 are the Most Commonly Used) METHODOLOGY DESCRIPTION APPLICATION Aggregate Analysis Used to describe a population or a segment or to compare two segments. Descriptive analysis, profiling, campaign analysis, winner-loser analysis. Correlation Analysis Looks for the relationship between two or more things with the prospect of being able to explain or drive one with other. Pre and post, test-control, drivers, dashboard. Trends Analysis Aggregate or correlation analysis over time, that is, trends over a period of time. Trends of sales, revenues, breaks in trend and segments or drivers over a period of time. Sizing/Estimation Structured approach to make a near-accurate guesstimate in the absence of historical data. Business case with limited internal data or one that is dependent on external data and assumptions. Predictive Analytics/Time Series Looks at both current and historical data to make predictions about future events. Drivers of conversion or consumer engage- ment, forecasting. Segmentation Groups customers or products into meaningful segments, usually to enable better targeting for the purpose of driving higher value through customization. Grouping customers or products for targeting and customization. Customer Life Cycle Looks at the different stages of the purchase process to determine what stage a group of customers is at and decide how to move them up to the next stage in the purchase process. Customer progress stages from consideration through purchase to use, sales funnel.
- Page 45 While this may seem like a compelling case for predictive analytics to some, we think a CMO can realize a better ROI using simple business analytics techniques to arrive at insightful and informed decisions.
- Page 48 In most cases, simple business analytics can give you a scale of efficiency, instead of waiting for the analytics team to get around to answering your question. The data you will need, most likely, is already at hand; you just have to dig it out and look at it.
- Page 52 This chapter introduces the BADIR framework that can take you from Data to Decisions using a set of five lean, streamlined steps that can address 80 percent of business problems using simple, yet powerful, analytics. BADIR stands for Business question, Analysis plan, Data collection, Insights, and Recommendations
- Page 54 You may notice right away that even though BADIR is a process about data analytics, it does not start with data. This framework starts with an understanding of the real business question that the data needs to answer.
- Page 57 If you find that the stakeholders are unable to take action based on the findings, then this analysis doesn’t need to be done.
- Page 63 People often think that hypotheses come from data, but that is not true. Hypotheses are best generated through a brainstorming session with all the key stakeholders based on what they think may be driving the situation.
- Page 64 Two separate meetings are recommended: 1. The first should generate the hypotheses. 2. The second should prioritize them.
- Page 94 Without question, you need to know the details, but keep them to yourself unless asked.
- Page 126 Learn to program in R (a free statistical package) with free online courses by portals like Coursera.
- Page 137 Our tool of choice for simple analytics is, drumroll, Microsoft Excel. It is the spreadsheet application in the Microsoft Office package. It is by far the most widely used tool for business analytics and has seen some powerful improvements and plug-ins in recent years that have enabled more sophisticated analysis. Excel is ideal for business users, who do not handle large datasets or perform complex analytics.
- Page 145 Your organization needs to be strong in four areas for you to successfully leverage analytics: leadership, analytics talent, decision making, and data maturity.
- Page 177 In a cross-functional organization, your ability to influence starts by recognizing these basic principles: • Making decisions collaboratively and leveraging others’ domain expertise is more effective than making decisions as an individual. • Engaging stakeholders from the start will accelerate execution, whether they are your execution partners or budget approvers.
- Page 178 Boosting your influence starts with building alignment with stakeholders at the outset of an initiative. There are four stages of any initiative or project (see Exhibit 10-1): 1. Vision: Define “that” which you want to drive the team toward. 2. Plan: Lay out a plan detailing scope, stakeholders, resources, and timelines. 3. Execute: Act according to the plan, and adjust it in flight if required. 4. Learn: Learn from this initiative and feed the results into your next project.
- Page 191 Overreliance on external consultants also signals to your internal resources that you do not trust their capabilities, which, needless to say, does not help morale.
- Page 199 However, you can easily get distracted by supporting small-to-medium impact projects—collecting a series of wins with minimal impact. Good analytics leaders have to think big and become involved in the strategic discussion.
- Page 211 Impact: In the first 7 years of the program, from 2005 to 2012, violent crime decreased by 23 percent and burglaries in Memphis dropped five times more than the national average. An IBM case study estimated that Memphis achieved an 863 percent return on its investment, calculated as the cost of additional forces that would have been required to bring about this decline in crime without the help of analytics.
- Page 216 Good vs. great managers: “Project Oxygen” analyzed a ton of data to determine that great managers were critical for retention and top performance of the workforce. The analyses also identified eight characteristics of great managers as opposed to just good managers. These included periodic one-on-one coaching and frequent personalized feedback, which were valued much more than technical knowledge. Today, managers are rated twice a year by their employees on those eight factors.
- Page 217 Impact: Google employees have an amazing workforce productivity that few can match. On average each employee generates $1 million in revenue each year.
- Page 217 Case Study 8: Reversing the High School Dropout Rate in Hamilton County
- Page 222 Mode is the number that appears most frequently in a set of numbers. This behavior is called a modality. In a nonnormal distribution, mode is a better representation of average than mean or median. For example, if you look at the number of cars on the road, it is usually nonnormal bimodal distribution, where the numbers peak around 8 AM and 5 PM.
- Page 224 Z-score This is the technical term for describing how many standard deviations a data value is from the mean. It is used to evaluate whether a particular point is typical or atypical.
- Page 224 Since she is more than two standard deviations away from the mean, she is not your typical customer. Let’s say Mary is willing to splurge and asks for extra amenities that you don’t currently have. Just because you know she is not a typical customer, you may not need to start investing in those extra amenities unless many of your typical customers start asking for it.
- Page 226 Error is calculated using the formulas given below. Error for difference between two sample means (e.g., average age comparison between two samples) is:
- Page 227 CORRELATION This is the statistical measure of the linear relationship between two or more random variables and is represented by r (the Pearson correlation coefficient) with values between + 1 and –1. The closer the value is to + 1 or –1, the higher is the correlation. No correlation is indicated by r = 0.
August 29, 2015
A solid book with a lot of actionable information for anyone who is an analytics manager. There are several frameworks and step-by-step instructions throughout the book that are easy to follow. It's also a quick read so you can get a lot out of it in a short amount of period. I just moved into a new analytics-centric role and I will be able to utilize a lot of the suggestions in this book.
July 27, 2016
مناسب جداً لكل من يرغب في مقدمة عامة عن تحليل البيانات و علاقته بالأعمال و كذلك لغير المتخصصين ..
الكاتبان لهما خبرة ممتازة و جيدة لها أثرها الواضح في تقسيم الكتاب و الأمثلة
الكاتبان لهما خبرة ممتازة و جيدة لها أثرها الواضح في تقسيم الكتاب و الأمثلة
December 31, 2019
Makes a convincing case for rigorous decision-making processes. Outlines when to use and when NOT to use business analytics; when to engage with big data and when it's overkill. Advocates for developing analytics expertise in-house. Stresses the importance of defining the right questions, getting buy-in, communicating, and making recommendations. The coverage of analytics methodology is the weak point. Most of the examples are sales-related; the 10 short case studies from other fields, at the end of the book, were fascinating!
July 5, 2019
Puts analytics into simple terms with industry relevant case studies. Easy read with diagrams that puts it into perspective. Very textbook like.
Didn't continue after Predictive Analytics because it wasn't relevant to my work yet. Should revisit this book again if I have to use more advanced methods in the future.
Didn't continue after Predictive Analytics because it wasn't relevant to my work yet. Should revisit this book again if I have to use more advanced methods in the future.
February 22, 2020
Talks about pragmatic data science which is much needed today in the business world.
November 26, 2021
nice but some materials are not new if you have been working some time in analytics
January 8, 2015
The content was pretty average. The audiobook was horrible to listen to though. It felt like someone was reading a table to me constantly. After 5 mins of lists of lists of lists they would then say, and now see the accompanied PDF for reference. No comment as to what figure in the accompanied PDF (not that I was able to pull up a PDF while driving a car).
December 23, 2014
Well organized, but it suffers some pacing stutters. Bonus points for not trying to sell itself every other chapter, as most business books do. I recommend this to business professionals who have scratched the surface of analytics; this makes an excellent jumping off point.
Read
April 20, 2015nothing
May 28, 2016
a very practical book targeted towards business analytics practitioners. More designed around giving advice on frameworks needed to set up an analytics team
Displaying 1 - 14 of 14 reviews