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Winning with Data: Transform Your Culture, Empower Your People, and Shape the Future
Crest the data wave with a deep cultural shift Winning with Data explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture—this book provides sound guidance on building the type of organizational culture that creates and leverages data daily, in every aspect of the business. Real-world examples illustrate these important concepts at you'll learn how data helped Warby-Parker disrupt a $13 billion monopolized market, how ThredUp uses data to process more than 20 thousand items of clothing every day, how Venmo leverages data to build better products, how HubSpot empowers their salespeople to be more productive, and more. From decision making and strategy to shipping and sales, this book shows you how data makes better business.
Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve.
Understand the changes a data culture brings to companies Instrument your company for maximum benefit Utilize data to optimize every aspect of your business Improve decision making and transform business strategy Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs.
Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve.
Understand the changes a data culture brings to companies Instrument your company for maximum benefit Utilize data to optimize every aspect of your business Improve decision making and transform business strategy Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs.
164 pages, Kindle Edition
Published May 26, 2016
107 people are currently reading
1024 people want to read
About the author
Tomasz Tunguz
1 book6 followersTomasz Tunguz is a venture capitalist at Redpoint and writes daily, data-driven blog posts about key topics for startups including fundraising, startup benchmarks, best practices, and team building.
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Displaying 1 - 30 of 47 reviews
October 27, 2016
A very shallow approach to data. No practical info at all. Reads like a marketing prospectus of Looker. The 3 stars are for the real stories from around tge industry and maybe the chapter on recruitment data. It probably deserves less.
February 25, 2017
I would have given this a 4 if there was less blatant product placement. Frank Bien, one of the co-authors of the book, is the CEO of a product called "Looker". And this shows very clearly when it comes to examples that show up in the book on how to "do data right". It'd be OK if Looker was mentioned once or twice, but it was mentioned in pretty much every other chapter, while other products got very little airtime.
At times it felt a little like reading a brochure. I would have been OK with it if Looker was open source or non-commercial, but it's not. If they had introduced other alternative products as well it would have been better too.
To be fair, the book has some very valid points on how data is managed in companies. The introduction of the concept of data breadlines, and the politics behind data management and real-world access was great (if you're just getting into the world of data as an analyst this is a superb introduction).
But in the end I found myself reading the book like how one would watch a horror movie, always anticipating but not knowing when "Looker" would show up.
At times it felt a little like reading a brochure. I would have been OK with it if Looker was open source or non-commercial, but it's not. If they had introduced other alternative products as well it would have been better too.
To be fair, the book has some very valid points on how data is managed in companies. The introduction of the concept of data breadlines, and the politics behind data management and real-world access was great (if you're just getting into the world of data as an analyst this is a superb introduction).
But in the end I found myself reading the book like how one would watch a horror movie, always anticipating but not knowing when "Looker" would show up.
June 4, 2017
A perfect read for anyone wanting to get a general big picture on the importance of data.
WWD or "Winning With Data" gives a general prescription for today's world on how to take hold of the world heavyweight championship belt. The prescription is simple: The more a company embraces a data-driven culture, the more its products and services will contain a competitive advantage from the competition.
Most people who are not seeing their tidings may be reacting right now and saying: "I use data every day! No matter our effort, we are still back to square one!". The problem is not the effort of "using data", but how an organization "processes the data" more efficiently and effectively. That is why "some" start-ups "actually" disrupted big behemoths organizations: They found a weak blind spot, they nailed their target on it by grinding their teeth real hard, and just like Rocky, took over the longest reigning champion in the last round. But this used to happen all the time. Just slower. Unlike the past, our present economy is more open to bringing those disruptions with less red tape.
So you understand what the problem is? Processes. Organizations that can bend the rules of their current processes are better off. What is the point of benchmarks comparing yourself if you can't change your own benchmark in the first place? After all, as Einstein well has said, "Insanity is doing the same thing over and over again". Which brings us to the main point: Your organization may doing very well being "data-driven", but maybe not so "data-driven" like others do. You see, some stay in a stall state and don't innovate to be more data-driven than they used to be. For most organizations, most should now be miles away keeping their databases stored in document files in magnetic disks (yes, literally files! But they saved us like an eagle during the world wars!). However, a lot of big companies still lag a lot on making their data-warehouse compatible with their flat and decentralized organization structure. Whether human or robot, data is the bread and butter these days for taking track of your actions and making better decisions. It is part of our survival. Ergo, the main thesis of this book is that most employees face their data problems like a huge queue waiting to get their soup, bread and cheese portions like those breadlines during the great depression.
So what is going wrong? Hello, you are employee #542. There have been many iterations of making data more accessible before you came here. In the old days, big boss was around, wanted some numbers, somebody is in the terminal, and he gets his results. God forbid, let us hope the guy behind the terminal knows what he is doing, otherwise, he will soon to be fired if five years later they found out he got the numbers wrong. But employee #542, there is another company XYZ that handles data better than you. You see, the people who work behind the terminal don't prepare static reports and dashboards all the time, they give access to enough raw data that you are interested and that you can play with, maybe not so easy like a Google search engine, but fun enough like playing a game of civilization. In other words, the author says that data warehouses are evolving in such a way where employees can have the data they need with the least red tape in order to make their own decisions and explorations. With the advent of many business intelligence tools, the person behind the terminal is more responsible for collaborating all departments for the definitions of all the metrics everybody can agree on and having all the allocated bandwidth and expertise for making that possible. This leads to a domino effect: Once an organization lifts the barriers off for anyone being able to access data, you turn the wheel 180 degrees to the type of employees you want to hire in your company. No longer you want employees that comply about "enough is enough" on their data requests. Instead, you want to have curious employees (also called "Googliness" in google) that always ask "Why" while backing up all their opinions with "data".
Congratulations! About now, we just discussed what the book is all half about: The problem, the background, the big picture of how data has evolved over the years, and how to get back on track to the latest trends with data. The last chapters discuss supporting elements for a company to be an enabler of a data culture. Firstly, have some data literacy classes in your organization. At the time of writing this review, Airbnb, the world’s largest accommodation provider that owns no real estate just announced their own "data university" to its own employees. If you don't teach people to be more literate about data, not only on how to gain access to data but to also making their hypothesis sound, does that not sound like a red flag? Second, data that is not practical and relevant can be a waste of effort if it deviates from the company's vision. For example, some companies make a basket case by spending more time over a $10,000 bike rack debate than proposing new functional features that project a churn rate drop by 20 percent over the next year. Like the old bible saying goes, "A quart of wheat for a denarius, and three quarts of barley for a denarius". However, if an idea is not very clear whether it is worth hitting the rubber to the road, most innovative companies try to explore their assumptions with more data in order to get a more clear picture. The last chapter tells us to write our insights as stories. If you have the data right, and you know you are right, and if your audience is only awake by mesmerizing them with a good old parable story, then mesmerize them. It is good to show your passion out. After all, they will find out sooner or later whether you are the real deal or just a bluff.
Yes, the book has a face value of advertisement from the personal experiences of the author to the data software in his workplace (Looker) that enables anyone to become a data rock star. Nonetheless, whether you just punch numbers or you work inside a terminal with the nuclear launch codes on your pocket, if you still don't have a big picture how data was and how it is and going to evolve, then this book is just for you in order to get started.
With health trackers on your watch and better financial management and advice on your pocket, we hope, that not only companies will start winning with data, but single individuals too will take over their life with making better lifestyle choices as well. Let us all take part of this and see how all this unfolds.
WWD or "Winning With Data" gives a general prescription for today's world on how to take hold of the world heavyweight championship belt. The prescription is simple: The more a company embraces a data-driven culture, the more its products and services will contain a competitive advantage from the competition.
Most people who are not seeing their tidings may be reacting right now and saying: "I use data every day! No matter our effort, we are still back to square one!". The problem is not the effort of "using data", but how an organization "processes the data" more efficiently and effectively. That is why "some" start-ups "actually" disrupted big behemoths organizations: They found a weak blind spot, they nailed their target on it by grinding their teeth real hard, and just like Rocky, took over the longest reigning champion in the last round. But this used to happen all the time. Just slower. Unlike the past, our present economy is more open to bringing those disruptions with less red tape.
So you understand what the problem is? Processes. Organizations that can bend the rules of their current processes are better off. What is the point of benchmarks comparing yourself if you can't change your own benchmark in the first place? After all, as Einstein well has said, "Insanity is doing the same thing over and over again". Which brings us to the main point: Your organization may doing very well being "data-driven", but maybe not so "data-driven" like others do. You see, some stay in a stall state and don't innovate to be more data-driven than they used to be. For most organizations, most should now be miles away keeping their databases stored in document files in magnetic disks (yes, literally files! But they saved us like an eagle during the world wars!). However, a lot of big companies still lag a lot on making their data-warehouse compatible with their flat and decentralized organization structure. Whether human or robot, data is the bread and butter these days for taking track of your actions and making better decisions. It is part of our survival. Ergo, the main thesis of this book is that most employees face their data problems like a huge queue waiting to get their soup, bread and cheese portions like those breadlines during the great depression.
So what is going wrong? Hello, you are employee #542. There have been many iterations of making data more accessible before you came here. In the old days, big boss was around, wanted some numbers, somebody is in the terminal, and he gets his results. God forbid, let us hope the guy behind the terminal knows what he is doing, otherwise, he will soon to be fired if five years later they found out he got the numbers wrong. But employee #542, there is another company XYZ that handles data better than you. You see, the people who work behind the terminal don't prepare static reports and dashboards all the time, they give access to enough raw data that you are interested and that you can play with, maybe not so easy like a Google search engine, but fun enough like playing a game of civilization. In other words, the author says that data warehouses are evolving in such a way where employees can have the data they need with the least red tape in order to make their own decisions and explorations. With the advent of many business intelligence tools, the person behind the terminal is more responsible for collaborating all departments for the definitions of all the metrics everybody can agree on and having all the allocated bandwidth and expertise for making that possible. This leads to a domino effect: Once an organization lifts the barriers off for anyone being able to access data, you turn the wheel 180 degrees to the type of employees you want to hire in your company. No longer you want employees that comply about "enough is enough" on their data requests. Instead, you want to have curious employees (also called "Googliness" in google) that always ask "Why" while backing up all their opinions with "data".
Congratulations! About now, we just discussed what the book is all half about: The problem, the background, the big picture of how data has evolved over the years, and how to get back on track to the latest trends with data. The last chapters discuss supporting elements for a company to be an enabler of a data culture. Firstly, have some data literacy classes in your organization. At the time of writing this review, Airbnb, the world’s largest accommodation provider that owns no real estate just announced their own "data university" to its own employees. If you don't teach people to be more literate about data, not only on how to gain access to data but to also making their hypothesis sound, does that not sound like a red flag? Second, data that is not practical and relevant can be a waste of effort if it deviates from the company's vision. For example, some companies make a basket case by spending more time over a $10,000 bike rack debate than proposing new functional features that project a churn rate drop by 20 percent over the next year. Like the old bible saying goes, "A quart of wheat for a denarius, and three quarts of barley for a denarius". However, if an idea is not very clear whether it is worth hitting the rubber to the road, most innovative companies try to explore their assumptions with more data in order to get a more clear picture. The last chapter tells us to write our insights as stories. If you have the data right, and you know you are right, and if your audience is only awake by mesmerizing them with a good old parable story, then mesmerize them. It is good to show your passion out. After all, they will find out sooner or later whether you are the real deal or just a bluff.
Yes, the book has a face value of advertisement from the personal experiences of the author to the data software in his workplace (Looker) that enables anyone to become a data rock star. Nonetheless, whether you just punch numbers or you work inside a terminal with the nuclear launch codes on your pocket, if you still don't have a big picture how data was and how it is and going to evolve, then this book is just for you in order to get started.
With health trackers on your watch and better financial management and advice on your pocket, we hope, that not only companies will start winning with data, but single individuals too will take over their life with making better lifestyle choices as well. Let us all take part of this and see how all this unfolds.
July 12, 2017
It's a mix between a bad commercial for Looker - the company of one of the authors and a high school primer on why you should use statistics.
True bummer because Tunguz's blog is fantastic.
True bummer because Tunguz's blog is fantastic.
February 2, 2017
The book covers themes like how modern companies such as Uber, Google, Facebook, Zendesk etc are using data to make day to day operational decisions; What are the problems with the old school BI+Analytics team set up and how modern data platforms have evolved that process petabytes of data.
The book also touches upon important execution related topics such as setting a data dictionary for common formulas across the company, creating an intellectually honest culture that doesn't make the data tell what it wants to hear (do proper statistical validity etc.), and telling data stories for executive decision making.
Some nuggets I took away
* Facebook makes their analysts "walk the floor"/Gemba. Allows them to not duplicate effort, but at the same time be very business/outcome focussed.
* "One Equation that defines the whole business". Was done by the author's boss at Google Adwords. Helped visualise the complexity and at the same time the key levers to better outcomes.
* How Greenhouse set up a very robust metrics dashboard for hiring that looked almost like a sales funnel.
* How all state insurance accident prediction went up by 340% after opening it up to data scientists on Kaggle!
The book also touches upon important execution related topics such as setting a data dictionary for common formulas across the company, creating an intellectually honest culture that doesn't make the data tell what it wants to hear (do proper statistical validity etc.), and telling data stories for executive decision making.
Some nuggets I took away
* Facebook makes their analysts "walk the floor"/Gemba. Allows them to not duplicate effort, but at the same time be very business/outcome focussed.
* "One Equation that defines the whole business". Was done by the author's boss at Google Adwords. Helped visualise the complexity and at the same time the key levers to better outcomes.
* How Greenhouse set up a very robust metrics dashboard for hiring that looked almost like a sales funnel.
* How all state insurance accident prediction went up by 340% after opening it up to data scientists on Kaggle!
This entire review has been hidden because of spoilers.
October 7, 2016
Interesting book on something that's necessary in a modern business - enable data access across your org. Data in an organization has typically been siloed into BI and IT-type roles, where employees of a company come to them asking for a complex set of data, and expect quick turnover. In the modern day of technology companies, data-informed decisions are the most transparent, solid way to make decisions within a company.
Winning with Data explores how you can build tools to enable data access to everyone at a company, how it helps a company, and case studies of companies adding this weapon to their arsenal. If you've worked at an earlier-stage, growing startup before, you definitely know the pain of "data breadlines", and this book helps outline a solution. A lot of the insights are relatively straightforward, and not the most mind-blowing reading you'll do (most people agree upon this paradigm), nevertheless, this is a fantastic toolkit to learn about the power of data.
Winning with Data explores how you can build tools to enable data access to everyone at a company, how it helps a company, and case studies of companies adding this weapon to their arsenal. If you've worked at an earlier-stage, growing startup before, you definitely know the pain of "data breadlines", and this book helps outline a solution. A lot of the insights are relatively straightforward, and not the most mind-blowing reading you'll do (most people agree upon this paradigm), nevertheless, this is a fantastic toolkit to learn about the power of data.
January 21, 2020
I read this book twice, 2 years later, to give it a second chance - it’s disorganized, lacks quantified data points, delivers meaningless recommendations, and repeatedly pitches Looker without providing any supporting details. I was disappointed compared to the quality of the content the authors produce on their blogs (which is great)
January 27, 2018
I honestly expected more but I should have managed my expectation after the lacklustre reception and other reviews. One of the authors is the CEO of Looker. And it read more like an ad for Looker half the time. Sure, there some interesting concepts but really, it was more of pointing out basic things most entrepreneurs or start up founders already know or at least should remember.
September 26, 2016
A fantastic book but could have been written to flow slightly nicer. The jumping between anecdotes and essays was jarring at times but overall it was a great read.
September 12, 2019
Uwaga: w co drugim rozdziale pojawia się Looker. To niestety głównie wątpliwej jakości content marketing, a nie książka, jakiej można się spodziewać po okładce. Gdybym miał sprzedać mojego maila do bazy marketingowej za taką pozycję - byłbym nawet względnie zadowolony, ale tutaj, niestety, zapłaciłem pieniędzmi i kilkoma godzinami za bycie odbiorcą reklamy.
Jest kilka ciekawych konceptów (np. uspójniony „data dictionary” dla całej organizacji, „równanie” opisujące sposób działania na przykładzie doświadczeń autora w pracy zespole AdSense), jest kilka ciekawych przykładów (e.g. jakie metryki sprzedażowe funkcjonują w Hubspocie), ale wszystko niestety jest przyćmione przez zbyt jawne reklamy Lookera.
Na plus: na końcu jest lista dość ciekawych, przekrojowych wskaźników, głównie do zastosowania w biznesach SaaSowych.
Jest kilka ciekawych konceptów (np. uspójniony „data dictionary” dla całej organizacji, „równanie” opisujące sposób działania na przykładzie doświadczeń autora w pracy zespole AdSense), jest kilka ciekawych przykładów (e.g. jakie metryki sprzedażowe funkcjonują w Hubspocie), ale wszystko niestety jest przyćmione przez zbyt jawne reklamy Lookera.
Na plus: na końcu jest lista dość ciekawych, przekrojowych wskaźników, głównie do zastosowania w biznesach SaaSowych.
February 10, 2023
There were a couple nuggets of insight, but I'd say it's not worth the read. The chapters are disjointed in narrative, and a lot of the book felt like a written infomercial for using the data product developed by Looker, which is a company invested in by the author. Additionally, there could have been more elaboration on the data maturity processes of data modeling and a description of what a ”data fabric" actually is. Don't waste your time. It'd probably be better to read a statistical analysis book.
January 10, 2019
Tunguz makes a lot of good points, but perhaps because I work in this field, a lot of it was already redundant. Would be a great book for an entrepreneur or aspiring analyst to gain a greater understanding of data driven decision making in technology companies. The information was good, the delivery a little dry. Nothing earth shattering or compelling. Would actually make a decent textbook for a college course for business majors.
June 16, 2019
I really enjoyed the approach of the author in regard of data usage and it's importance nowadays, every single case is an inspiration after the other. The only thing that prevents me from rating it with a 5 star is that it could have less marketing from Looker and a bit more real cases outside it. Overall it's a great book, and I do recommend it as a first book to understand the importance of data in the world we are living in.
July 4, 2019
I’ve been thinking deeply about how to improve our data pipeline, gain insights, and operationalize our data. This book pretty much summarized everything I learned after a lot of work. I wish I would have read it before!! It’s a great starting place for those working on improving how they use their data.
It’s primary drawback is that it’s a bit of a pitch book for Looker. So dismiss that and you’ll be good.
It’s primary drawback is that it’s a bit of a pitch book for Looker. So dismiss that and you’ll be good.
December 23, 2023
Solid book of data insights that did feel like an intro for how people have thought about using data. Reads very much for an experienced management or executive team or younger professionals who have not been introduced to how businesses can and strategically think about data. It did not get into details for someone who is working with it all their career except to recognize the pitfalls or hindrances to data driven methods.
October 10, 2017
Short and snappy, laymen-friendly (some parts are too dumbed-down), examples from various industries and startup stories. However, I've felt like this is a very subtle marketing for Looker. The writer is Looker CEO. Readers need to further educate themselves that there are other alternatives similar to Looker. Also, almost all are Silicon Valley startups. Not much of diversity to benchmark from.
December 15, 2017
A great overview of how data can and should work in companies, but not a lot of practical info in here for the startup entrepreneur who doesn’t have a robust data team to implement complex “data fabrics” and the like. Case studies were interesting, and seeing data-driven companies at scale was inspiring. Now what can I do with this info?
February 23, 2018
Great tools and resources for teams and organizations looking to make better, faster decisions using data. Easy to read and filled with many interesting examples of teams and organizations that have implemented this approach. Good resource library/appendix in the back for tactical steps that can be applied to different areas of the business: HR, sales, etc.
March 27, 2019
Probably a good intro to data for people with no previous exposure to data in modern tech companies. Probably not going to add a ton of value for people that are emersed in the tech space and know about data tools from a high level. That said, there were a few parts that made me think a little, mostly around how our data is stored.
January 14, 2020
More like 3.5 stars. Started out very solid. Great analogies and inside look at what goes on in an organization that cannot handle its data. Then it goes onto way too many tangents about using data wisely rather than giving any unifying theory.
Also it read as a weird infomercial for Looker and Zendesk for much of the book.
Also it read as a weird infomercial for Looker and Zendesk for much of the book.
February 21, 2020
I really enjoyed this, and will likely give it another read to absorb more. Open data rules and more companies should work to be more transparent. I really liked a quote included in the book from Albert Einstein, "Not everything that counts can be counted. And not everything that can be counted, counts."
June 18, 2020
Data is the new GOLD. In today's day of age understanding data has become really important to run any business. This is the perfect book which shows how you can shape the future by revolutionizing your team's and compan's culture to wield data to gain a superior competitive edge over your competitors.
August 23, 2020
Can not recommend. It did have some useful content on data analysis and incorporating data decisioning into your company, but it was so poorly written it was hard to glean. The flow was terrible; it felt like it jumped all over the place. Or just stopped abruptly at points. Also seemed low budget; numerous grammar mistakes throughout that just made me cringe as I read.
October 10, 2020
I think it is a great book that contains a nice overview of various concepts related to data management and relevant for CTO, CDO, CMO, and C-level Executives. It summarizes very well data driven culture and why it is important nowadays and gives the relevant history behind certain solutions.
IMHO a must in terms of Cloud Data Platforms and why they are more and more relevant nowadays.
IMHO a must in terms of Cloud Data Platforms and why they are more and more relevant nowadays.
June 25, 2017
Simple touchdown on the benefits of data backed decision making. Written non-jargonistic, the author underpins the data as the critical point for future especially in an ever increasing digital world.
September 1, 2018
There is barely any valuable information on "winning with data" really. There are some case studies but with very shallow interpretation and lacking in insight. The book is more like an advertisement of Looker as one of the author is the CEO of that company.
November 1, 2020
It’s clear this is a marketing book for Looker.
It’s a high level, superficial review of data, but does go over a few helpful frameworks in terms of data governance within a business. For those, I thought the book was useful.
It’s a high level, superficial review of data, but does go over a few helpful frameworks in terms of data governance within a business. For those, I thought the book was useful.
August 22, 2021
What’s the summary? “Data is important”
Hardly had any other takeaways. Some interesting case studies peppered through and a good glossary of metrics
Maybe I’m not the intended audience here. Though it’s hard to imagine who the intended audience would be for this book in this day and age
Hardly had any other takeaways. Some interesting case studies peppered through and a good glossary of metrics
Maybe I’m not the intended audience here. Though it’s hard to imagine who the intended audience would be for this book in this day and age
January 1, 2022
Data is the new currency of business, particularly for companies like Google, Facebook, Amazon, etc. The author provides a good overview of how data is used to help companies win, but does so with (dare I say it?) limited data that mostly focuses on his own company startups.
October 22, 2023
Interesting book on how significant corporations incorporate dig data into their systems to increase decision-making and use data to determine what happens, what to do, how to find bottlenecks, etc. The Marine Corps must catch up to what is happening in corporate America.
Displaying 1 - 30 of 47 reviews