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Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results
The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data.
Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective.
From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario.
Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter
Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective.
From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario.
Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter
277 pages, Kindle Edition
First published April 4, 2016
102 people are currently reading
649 people want to read
About the author
Bernard Marr
64 books123 followersBest-Selling Author, Keynote Speaker and Leading Business and Data Expert
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Displaying 1 - 25 of 25 reviews
April 5, 2021
Well, this is basically a compilation of web sourced data on the topic, made in a way ready made for the reader's consumption. Nice case selection: some l'enfants terribles, some doing a world of good.
Key takeouts:
Of the more standardized and expected titans:
Wallmart and strawberry poptarts to prevail upon Hurricane Sandy. Data Cafe and monitoring novelty cookies. Shopycat service.
Netflix's Big Data adventure of a personalized TV
Apixio & 'patient objects' & 'knowledge graphs'.
Facebook: the personal ad hell.
RBS & personology for getting the services out the door
Miscrosoft: DAAS / SAAS
Acxiom. The marketeering profiteers off the Big Data long before it was. PersonicX, Select language.
Q: Nominated for a Big Brother Award as “worst corporate invader for a tradition of
data brokering”. (c) Yay!
NEST: Q: “The home has to be thoughtful and understand your habits.” (c)
Palantir: another contender for the Big Bro Award, I think.
Q:
It’s an issue that founder Alex Karp doesn’t shy away from. Speaking to Forbes a couple of years ago, he said: “I didn’t sign up for the government to know when I smoke a joint or have an affair.” And in a company address he stated: “We have to find places that we protect away from government so that we can all be the unique and interesting and, in my case, somewhat deviant people we’d like to be.” (c)
Airbnb: Aerosolve algorithm for dynamic pricing.
Experian: Mosaic.
Terra Seismic: 90% accuracy (not too bad!)
Of the not exactly expected:
LotusF1 Big Data for the 'fast and furious spectacle'
Pendleton & Son Butchers
Narrative Science: QuillTM packaged as SAAS
Technically suprising:
John Deere - SAP HANA, seriously?
Key takeouts:
Of the more standardized and expected titans:
Wallmart and strawberry poptarts to prevail upon Hurricane Sandy. Data Cafe and monitoring novelty cookies. Shopycat service.
Netflix's Big Data adventure of a personalized TV
Apixio & 'patient objects' & 'knowledge graphs'.
Facebook: the personal ad hell.
RBS & personology for getting the services out the door
Miscrosoft: DAAS / SAAS
Acxiom. The marketeering profiteers off the Big Data long before it was. PersonicX, Select language.
Q: Nominated for a Big Brother Award as “worst corporate invader for a tradition of
data brokering”. (c) Yay!
NEST: Q: “The home has to be thoughtful and understand your habits.” (c)
Palantir: another contender for the Big Bro Award, I think.
Q:
It’s an issue that founder Alex Karp doesn’t shy away from. Speaking to Forbes a couple of years ago, he said: “I didn’t sign up for the government to know when I smoke a joint or have an affair.” And in a company address he stated: “We have to find places that we protect away from government so that we can all be the unique and interesting and, in my case, somewhat deviant people we’d like to be.” (c)
Airbnb: Aerosolve algorithm for dynamic pricing.
Experian: Mosaic.
Terra Seismic: 90% accuracy (not too bad!)
Of the not exactly expected:
LotusF1 Big Data for the 'fast and furious spectacle'
Pendleton & Son Butchers
Narrative Science: QuillTM packaged as SAAS
Technically suprising:
John Deere - SAP HANA, seriously?
February 16, 2020
Big data basically refers to the fact that we can now collect and analyse data in ways that was simply impossible even a few years ago. There are two things that are fuelling this Big data movement: the fact we have more data on anything and our improved ability to store and analyse any data.
This book provides a compressive overview of the current state of play in Big Data. It’s a concise hand book which you can find all challenges that big companies around the world are facing daily.
This book provides a compressive overview of the current state of play in Big Data. It’s a concise hand book which you can find all challenges that big companies around the world are facing daily.
May 9, 2019
İçerik çok yüzeysel, pek çok bilgiyi google'dan kolayca bulabilirsiniz. Hayal kırıklığı.
Read
April 21, 2021It was what I needed, because I wanted the big picture, but it wasn't the quickest read! Good that it was structured the same across the 45 chapters. Could easily pick the information I needed.
August 2, 2016
This book “does exactly what it says on the tin”: presents 45 mini case studies on the use of big data for a diverse range of companies, from CERN to Uber. Marr’s books may not meet the academic rigor of scientific journals, avoiding difficult concepts like principal component analysis (pca), etc. or focussing too much on the negatives of big data analytics (unsuccessful companies or the inappropriate use of big data analytics; using a sledge hammer to crack a nut), however they do provide an important introduction to complex topics in a well-written, informative and entertaining manner.
I agree with Marr that the term “big data”, if not lost, will lose its impotence, although not all “big data” is relevant and the first objective of analytics is to make big data, “smaller data” through methods like pca.
Another excellent text from Marr aimed at non-expert readers with an interest in big data.
Review by Deryn Graham FBCS, Senior Lecturer
Originally posted http://www.bcs.org/content/conWebDoc/...
I agree with Marr that the term “big data”, if not lost, will lose its impotence, although not all “big data” is relevant and the first objective of analytics is to make big data, “smaller data” through methods like pca.
Another excellent text from Marr aimed at non-expert readers with an interest in big data.
Review by Deryn Graham FBCS, Senior Lecturer
Originally posted http://www.bcs.org/content/conWebDoc/...
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September 15, 2016
Does exactly what the title says. I kind of wish it talked a little more about the pitfalls of Big Data, but oh well.
Irreconcilable differences: the grammar is horrible. Nitpicking yes, but is/are were killing me the whole time. "Business are," "Amazon are," etc., drove me a little nuts.
Irreconcilable differences: the grammar is horrible. Nitpicking yes, but is/are were killing me the whole time. "Business are," "Amazon are," etc., drove me a little nuts.
July 14, 2017
“Big Data, unlike any other trend at the moment, will affect everyone and everything we do”, writes Marr at the start of Big Data, concluding that “companies who ignore Big Data [will] be overtaken by those who don’t.”
Across each of 45 brief (~6 page) case studies, Marr sets out: the background; what problem big data is helping to solve; how big data is use in practice; what the results were; what data was used; some (very high level) technical details; the challenges the organisation needed to overcome; and their key learning points.
There are three major items missing from this. One is any real insight into customer benefits. Another significant limitation is the absence of any real discussion on the security, privacy and ethical implications from organisations collection and use of data. The third is the uncritical analysis by Marr in each case study.
Because each chapter is brief and uncritical, they end up being shallow and bland. In the chapter on how big data is used in banks Marr fails to cover the many ways banks are already using data to enhance customer experience by understanding consumer preferences; for credit decision making; in fraud identification; to improve operational effectiveness. In lauding LinkedIn’s use of data, Marr fails to provide any insight on the benefit to members that flows from LinkedIn’s data analysis – I certainly haven’t experienced any.
In the chapter on Etsy Marr notes the choice of cloud and in-house data storage, but does not provide any analysis of what contingencies would influence an organisation’s choice between these alternatives.
Each chapters’ list of technology used, becomes a mere list of names and acronyms: Apache, Apixio, Azure, BigQuery, Cassandra, Cloudera, Conjecture, Datameer, DeepQZ, DMX, EC2, EMC, Flume, Fusion, Hadoop, HANA, HDFS, Hive, Java, Lambda, Mahoot, MapR, MK:Smart, Mongo, Oozie, Predix, Presto, Python, R, Redshift, Solr, Spark , Splunk, SQL MemSQL MySQL and MySQLSSD, Sqoop, TeraData, V-Block, Vertica, Voldemort, Yellowfin. By time you get to Espresso and Pinot you can start to get confused about whether you’re reading about data technology or beverages and wanting one of the latter.
Marr notes that he has been in communication with the organisations about their use of data. But when he writes in the chapter on Microsoft that “data and analytics have existed for a long time and we’ve always combined them”, you can’t help but wonder whether this was written by Marr or by Microsoft. The book also has a liberal sprinkling of factual and grammatical errors (just what is a ‘compressive’ overview (p293) – perhaps Marr wanted to accurately represent the brevity of his analysis?) reflecting the regrettable decline in editorial standards given the changing economics of publishing.
The end result is that Marr has merely summarised (albeit effectively) a very high level overview of how organisations are using data, information that is generally available online. But his uncritical approach means there are no real insights for readers who have anything more than a cursory interest in this important topic.
Across each of 45 brief (~6 page) case studies, Marr sets out: the background; what problem big data is helping to solve; how big data is use in practice; what the results were; what data was used; some (very high level) technical details; the challenges the organisation needed to overcome; and their key learning points.
There are three major items missing from this. One is any real insight into customer benefits. Another significant limitation is the absence of any real discussion on the security, privacy and ethical implications from organisations collection and use of data. The third is the uncritical analysis by Marr in each case study.
Because each chapter is brief and uncritical, they end up being shallow and bland. In the chapter on how big data is used in banks Marr fails to cover the many ways banks are already using data to enhance customer experience by understanding consumer preferences; for credit decision making; in fraud identification; to improve operational effectiveness. In lauding LinkedIn’s use of data, Marr fails to provide any insight on the benefit to members that flows from LinkedIn’s data analysis – I certainly haven’t experienced any.
In the chapter on Etsy Marr notes the choice of cloud and in-house data storage, but does not provide any analysis of what contingencies would influence an organisation’s choice between these alternatives.
Each chapters’ list of technology used, becomes a mere list of names and acronyms: Apache, Apixio, Azure, BigQuery, Cassandra, Cloudera, Conjecture, Datameer, DeepQZ, DMX, EC2, EMC, Flume, Fusion, Hadoop, HANA, HDFS, Hive, Java, Lambda, Mahoot, MapR, MK:Smart, Mongo, Oozie, Predix, Presto, Python, R, Redshift, Solr, Spark , Splunk, SQL MemSQL MySQL and MySQLSSD, Sqoop, TeraData, V-Block, Vertica, Voldemort, Yellowfin. By time you get to Espresso and Pinot you can start to get confused about whether you’re reading about data technology or beverages and wanting one of the latter.
Marr notes that he has been in communication with the organisations about their use of data. But when he writes in the chapter on Microsoft that “data and analytics have existed for a long time and we’ve always combined them”, you can’t help but wonder whether this was written by Marr or by Microsoft. The book also has a liberal sprinkling of factual and grammatical errors (just what is a ‘compressive’ overview (p293) – perhaps Marr wanted to accurately represent the brevity of his analysis?) reflecting the regrettable decline in editorial standards given the changing economics of publishing.
The end result is that Marr has merely summarised (albeit effectively) a very high level overview of how organisations are using data, information that is generally available online. But his uncritical approach means there are no real insights for readers who have anything more than a cursory interest in this important topic.
April 25, 2021
I would rate it 2.5 stars if I could. I felt there was poor editing throughout (in my edition the first example of Walmart says that Hurricane Sandy was in 2004 and Hurricane Frances was in 2012), and the structure makes it extremely repetitive. There is hardly a need to include a section on technologies used if it is just a list of the analytics stack. Plus Hadoop itself shows up about for nearly every other example, so its hardly instructive.
Given the structure, the treatment of recurring theme is very scattershot. Privacy, finding good talent, , competition and economics between data-literate companies and non data-literate ones, etc all show up multiple times, so to get a better understanding on these topics, it would have better to separate out these themes into their own sections so that the author could expand on these labels in a cohesive and coherent manner.
The one merit is that some of the examples are quite interesting and unexpected. I would hardly think of fast food chains as using big data, for example. However, if you want a better value than this book, just curate a couple good blog posts and you'll 90% of it.
Given the structure, the treatment of recurring theme is very scattershot. Privacy, finding good talent, , competition and economics between data-literate companies and non data-literate ones, etc all show up multiple times, so to get a better understanding on these topics, it would have better to separate out these themes into their own sections so that the author could expand on these labels in a cohesive and coherent manner.
The one merit is that some of the examples are quite interesting and unexpected. I would hardly think of fast food chains as using big data, for example. However, if you want a better value than this book, just curate a couple good blog posts and you'll 90% of it.
March 30, 2018
This is a good supplement to Bernard Marr's other book regarding applying SMART big data concepts to your business (I rated that 4 stars.) In my opinion, they are better when read together than this one is as a standalone book.
It includes 45 case studies of how businesses across a variety of industries are using big data. Each case study includes: background, problem data was trying to solve, the results, technical details, challenges that needed to be overcome, and key learning points/takeaways.
The case studies are short but still long enough to capture all of the above. I marked down 9 of the 45 that I would want to read again out of personal interest, and I imagine that percentage will be about the same or lower for others as well.
It includes 45 case studies of how businesses across a variety of industries are using big data. Each case study includes: background, problem data was trying to solve, the results, technical details, challenges that needed to be overcome, and key learning points/takeaways.
The case studies are short but still long enough to capture all of the above. I marked down 9 of the 45 that I would want to read again out of personal interest, and I imagine that percentage will be about the same or lower for others as well.
February 24, 2018
On one side, Im greatly impressed by the vast innovative application of companies in their smart use of Big data to make things better for themselves/ for consumers as well. However, on the other side, together with reflection of Book 1984 of George Orwell, Im now a bit fraid of this super inter-connected world with numerless sensoring and possible privacy invasion by such smart applications or machines. All in all, still a big thumb up for Big data to be employed in this century if it is used sensibly.
August 11, 2018
This book presents 45 case studies of companies that took advantage of big data analytics or "smart data" like to call it. The scale of companies goes from the small local butchery to big multinationals.
I wish the author had gone into more details about what insights and/or advantages the analytics brought to the companies. There are a few examples among the 45 stories, but most of them only deal with how the data is collected, how much it amounts to and how it is processed.
I wish the author had gone into more details about what insights and/or advantages the analytics brought to the companies. There are a few examples among the 45 stories, but most of them only deal with how the data is collected, how much it amounts to and how it is processed.
March 4, 2018
Kitaptaki şirket sayısı daha az tutulup, verinin işleniş şekli ve analiz yöntemleri deteylandırılabilirdi ancak bu durum kitabın, veri ile ilgilenenler için zihin açıcı bir kitap olduğu gerçeğini değiştirmemiş.
May 25, 2018
The format of the book is ineffective, it is an attempt at a standardised review of 45 organisationa (one is the US Gov) of varying scales, and with varying levels of detail. In the end the content for each organisation is what you could have guessed without even reading.
October 14, 2018
Good coverage of Big Data Use Cases
The author pulled together 45 useful use cases from a variety of companies and industries that highlight how big data is being used and some of the things to consider while navigating the road to truly using big data.
The author pulled together 45 useful use cases from a variety of companies and industries that highlight how big data is being used and some of the things to consider while navigating the road to truly using big data.
July 24, 2019
This is a collection of short essays about how firms have used big data and technology to their advantage. The range of examples is pretty wide from large firms (Walmart) to governments to startups. I good resources.
May 29, 2019
On positive side : Interesting collection of use cases
On negative side : each case is treated at very high level and some are very generic. Also the writing style needs serious improvement
On negative side : each case is treated at very high level and some are very generic. Also the writing style needs serious improvement
August 12, 2019
Neredeyse hiç özel örnek yok. Çarpıcı bir bilgi yok. Sonucu değiştiren uygulamalar yok.
September 7, 2019
Very good summary of defintion of big data and its use cases and analysis.
February 25, 2020
İlginc bir kitap okumaya deger
July 1, 2022
Boring read
October 27, 2023
Enjoyed it. Interesting overview of how a lot of companies big and small are using data analytics.
November 10, 2023
Giriş kitabı, konuya dair yüzeysel bilgi var. Şirket çeşitliliği fazla olmakla birlikte büyük veri analizleri konusunda yeterince aydınlatılmamış.
Want to read
January 4, 2024004.6 M358b
July 3, 2024
flipped through these 45 stapled-together press releases while I waited for a big thing to compile, zero information
November 27, 2020
Big data is the hottest technical topic in town right now. Every industry right from retail, technology to medicine, sports, music to government and financial companies are trying to benefit from this big data boom. Everyone knows the extent of power it has but only a few have grasped the concept of effectively utilizing it. The book tries to fill that gap in knowledge by giving us a close overview of how successful companies are leveraging this magical tool called Big Data.
This book presents the big data strategies of 45 companies which are essentially 45 mini cases on the use of big data. Across each of the companies, the book helps us to find out: the background of the company, what problem they encountered, what kind of data is/was used, how big data was used to tackle that problem, the results achieved after the application of big data processes, technical details in some cases, the challenges these companies needed to face and finally the key learning outcomes.
Overall a nice interesting read.
This book presents the big data strategies of 45 companies which are essentially 45 mini cases on the use of big data. Across each of the companies, the book helps us to find out: the background of the company, what problem they encountered, what kind of data is/was used, how big data was used to tackle that problem, the results achieved after the application of big data processes, technical details in some cases, the challenges these companies needed to face and finally the key learning outcomes.
Overall a nice interesting read.
Displaying 1 - 25 of 25 reviews