The document discusses O'Reilly's use of data and data culture. It outlines the types of data O'Reilly collects, including book sales, conference, and government data. It describes how O'Reilly analyzes data to provide insights and communicate stories to support decision making. It also discusses key aspects of O'Reilly's data culture, which includes quantitative analysis, experimentation, and data-driven communications.
## Agenda
- Introduction
- Examples
- A guide of bar chart
- The analysis of source code
- The Wealth & Health of Nations
- An time series visual design of Adserver svn commit record
- Resources
## Agenda
- Introduction
- Examples
- A guide of bar chart
- The analysis of source code
- The Wealth & Health of Nations
- An time series visual design of Adserver svn commit record
- Resources
Web 2.0 Weekly - July 20, 2010: "DST Cashing Up"David Shore
Large deals drive July financing to near record levels: With another huge financing (DST - see below) July financing is near record monthly levels. Total raised month-to-date is $742.6 million over 29 deals - averaging $25.6 million each. Excluding the Zynga ($100 million) and DST ($388 million) deals, the average deal size is $9.4 million. In comparison, total capital raised in July 2009 was $209.0 million, averaging $5.2 million over 40 deals. The monthly median total is ~$280 million with a median deal size of $8.1 million.
Deals (M&A, Finance)
Naspers, a South African media and print conglomerate, invested $388 mm in Digital Sky Technologies, the Russian digital media holding company with significant stakes in Facebook, Groupon, Mail.ru, and Zynga.
The next biggest raise of the week was Australian enterprise software developer, Atlassian, receiving $60 mm in Series A funds from Accel Partners.
Social Commerce continues to garner attention – this week BlueSwarm ($0.8 mm angel round) and Shopkick ($15 mm Series B round) raised money.
Another $31 mm in venture funds found their way to Advertising-related start-ups, including Israel-based Adsmarket ($17 mm), as well as a pair of India-based companies – Komli Media ($6 mm) and InMobi ($8 mm).
Price performance split
The Web 2.0 public company universe was split this week, with 48% of companies seeing their market cap fall vs. 43% rising and 9% flat.
Scottish Parliament Information Centre (SPICe) introduction (by Paul Cannon)guestbf6957
This presentation was given by Paul Cannon as a contribution to ELISA Open Forum 2009. The theme of the Forum was "Libraries and Learning in the e-Environment". This presentation was an introduction to the services offered by SPICe.
This presentation was a contribution to the ELISA Open Forum 2009. The theme of the Forum was "Libraries & Learning in the e-Environment". The presentation described the way that this information services uses the e-environment to give the best service possible to its clients operating in the ever changing political world.
Scottish Parliament Information Centre (SPICe) tools and services in the e-En...guest6324be
This presentation was given as a contribution to the ELISA Open Forum 2009. It discusses in more detail the way that the Scottish Parliament information services use digital technologies in their work.
Uncover Your Data Journey: End-To-End Data Lineage For SAP BOBJ And SAP Data ...Wiiisdom
If you get asked that question on where your data is coming from, what transformation it gets, and who's accessing it, then this session is made just for you. In this session, get a deep understanding of your data lifecycle from BODS all the way to your final report, and understand how to carry out an audit and impact analysis in SAP BusinessObjects to document unused content.
Watch the session here: https://youtu.be/YC7VM-GsW6w
Feature Extraction for Predictive LTV Modeling using Hadoop, Hive, and Cascad...Kontagent
Description:
One of the biggest challenges for people building data products today is developing and refining features for modeling purposes (i.e. feature extraction) with the volume and variability of web scale data. In this talk, Martin will discuss some of the challenges and solutions faced by Kontagent as it built out a predictive lifetime value model for its customers. As you will learn, Hadoop is critical to this feature extraction process, and Cascading is quite handy when building out more complex features than can be readily developed in a query framework like Hive.
Speaker:
Martin Colaco, Director of Data Science for Kontagent
Industry researchers at Gartner announced in April 2012 that the worldwide business intelligence, analytics, and performance management software market surpassed the US$12 Billion level in 2011, a 16.4% increase over the previous year. This statistic is among many pointing to the need for both groups to apply what management guru Peter Senge proclaimed decades ago in The Fifth Discipline: the need for a learning organization. This presentation focuses on three learning areas for anyone in the business analytics profession. First, we analysts need to learn what the markets and industries are saying today. We discuss recent trends which show how analytics will shape the future. Second, we need to learn what group learning options are available. From industry conferences (such as the PASS BA Conference, and virtual PASS sessions) to free MOOCs (massive open online courses), we have more options available to improve our knowledge. Finally, we need to learn what leadership roles our groups can have. We can leverage social networks (including PASS) and social media -- both individually and as organizations -- to communicate passion.
Oracle Machine Learning Overview and From Oracle Data Professional to Oracle ...Charlie Berger
DBAs spend too time with routine tasks leaving little time for innovation. Autonomous Databases free data professionals to extract more value from data. Oracle Machine Learning, in Autonomous Database, “moves the algorithms; not the data” for 100% in-database processing. Data professionals perform many supporting tasks for “data scientists”, typically 80% of the work. Come learn an evolutionary path for Oracle data professionals to leverage domain knowledge and data skills and add machine learning. See how to build and deploy predictive models inside the Database. Using examples, demos and sharing experiences, Charlie will show you how to discover new insights, make predictions and become an “Oracle Data Scientist” in just 6 weeks!
Web 2.0 Weekly - July 20, 2010: "DST Cashing Up"David Shore
Large deals drive July financing to near record levels: With another huge financing (DST - see below) July financing is near record monthly levels. Total raised month-to-date is $742.6 million over 29 deals - averaging $25.6 million each. Excluding the Zynga ($100 million) and DST ($388 million) deals, the average deal size is $9.4 million. In comparison, total capital raised in July 2009 was $209.0 million, averaging $5.2 million over 40 deals. The monthly median total is ~$280 million with a median deal size of $8.1 million.
Deals (M&A, Finance)
Naspers, a South African media and print conglomerate, invested $388 mm in Digital Sky Technologies, the Russian digital media holding company with significant stakes in Facebook, Groupon, Mail.ru, and Zynga.
The next biggest raise of the week was Australian enterprise software developer, Atlassian, receiving $60 mm in Series A funds from Accel Partners.
Social Commerce continues to garner attention – this week BlueSwarm ($0.8 mm angel round) and Shopkick ($15 mm Series B round) raised money.
Another $31 mm in venture funds found their way to Advertising-related start-ups, including Israel-based Adsmarket ($17 mm), as well as a pair of India-based companies – Komli Media ($6 mm) and InMobi ($8 mm).
Price performance split
The Web 2.0 public company universe was split this week, with 48% of companies seeing their market cap fall vs. 43% rising and 9% flat.
Scottish Parliament Information Centre (SPICe) introduction (by Paul Cannon)guestbf6957
This presentation was given by Paul Cannon as a contribution to ELISA Open Forum 2009. The theme of the Forum was "Libraries and Learning in the e-Environment". This presentation was an introduction to the services offered by SPICe.
This presentation was a contribution to the ELISA Open Forum 2009. The theme of the Forum was "Libraries & Learning in the e-Environment". The presentation described the way that this information services uses the e-environment to give the best service possible to its clients operating in the ever changing political world.
Scottish Parliament Information Centre (SPICe) tools and services in the e-En...guest6324be
This presentation was given as a contribution to the ELISA Open Forum 2009. It discusses in more detail the way that the Scottish Parliament information services use digital technologies in their work.
Uncover Your Data Journey: End-To-End Data Lineage For SAP BOBJ And SAP Data ...Wiiisdom
If you get asked that question on where your data is coming from, what transformation it gets, and who's accessing it, then this session is made just for you. In this session, get a deep understanding of your data lifecycle from BODS all the way to your final report, and understand how to carry out an audit and impact analysis in SAP BusinessObjects to document unused content.
Watch the session here: https://youtu.be/YC7VM-GsW6w
Feature Extraction for Predictive LTV Modeling using Hadoop, Hive, and Cascad...Kontagent
Description:
One of the biggest challenges for people building data products today is developing and refining features for modeling purposes (i.e. feature extraction) with the volume and variability of web scale data. In this talk, Martin will discuss some of the challenges and solutions faced by Kontagent as it built out a predictive lifetime value model for its customers. As you will learn, Hadoop is critical to this feature extraction process, and Cascading is quite handy when building out more complex features than can be readily developed in a query framework like Hive.
Speaker:
Martin Colaco, Director of Data Science for Kontagent
Industry researchers at Gartner announced in April 2012 that the worldwide business intelligence, analytics, and performance management software market surpassed the US$12 Billion level in 2011, a 16.4% increase over the previous year. This statistic is among many pointing to the need for both groups to apply what management guru Peter Senge proclaimed decades ago in The Fifth Discipline: the need for a learning organization. This presentation focuses on three learning areas for anyone in the business analytics profession. First, we analysts need to learn what the markets and industries are saying today. We discuss recent trends which show how analytics will shape the future. Second, we need to learn what group learning options are available. From industry conferences (such as the PASS BA Conference, and virtual PASS sessions) to free MOOCs (massive open online courses), we have more options available to improve our knowledge. Finally, we need to learn what leadership roles our groups can have. We can leverage social networks (including PASS) and social media -- both individually and as organizations -- to communicate passion.
Oracle Machine Learning Overview and From Oracle Data Professional to Oracle ...Charlie Berger
DBAs spend too time with routine tasks leaving little time for innovation. Autonomous Databases free data professionals to extract more value from data. Oracle Machine Learning, in Autonomous Database, “moves the algorithms; not the data” for 100% in-database processing. Data professionals perform many supporting tasks for “data scientists”, typically 80% of the work. Come learn an evolutionary path for Oracle data professionals to leverage domain knowledge and data skills and add machine learning. See how to build and deploy predictive models inside the Database. Using examples, demos and sharing experiences, Charlie will show you how to discover new insights, make predictions and become an “Oracle Data Scientist” in just 6 weeks!
TOC Bologna 2012: How to Receive Funding and Support for New Digital and Prin...OReillyTOC
In the panel we will look at funds and grants available for new and current publishing ventures. The panel will discuss how small to medium size children’s publishers can receive funding for translated co editions and new digital publishing projects. Advice on how to combine the craft of print publishing with the new challenges of digital delivery with be shared with the participants. The panel will be able to share some really useful tips and offer guidance for those planning new ventures and wanting to make the most of new digital funds from across the world to support their activities.
Speakers: Charles Beckett, Agnes Vogt. Moderated by Neal Hoskins
TOC Bologna 2012: The Opportunity in Digital: Putting Publishers at the Helm...OReillyTOC
This panel of long-time digital publishing players will discuss how publishers can improve their chances of writing their own destiny and that of the industry by taking the right actions.
Speakers: Omer Ginor, Andrew Sharp, and Kevin O’Connor, moderated by Joe Schick
TOC Bologna 2012: How to Receive Funding and Support for New Digital and Prin...OReillyTOC
In the panel we will look at funds and grants available for new and current publishing ventures. The panel will discuss how small to medium size children’s publishers can receive funding for translated co editions and new digital publishing projects. Advice on how to combine the craft of print publishing with the new challenges of digital delivery with be shared with the participants. The panel will be able to share some really useful tips and offer guidance for those planning new ventures and wanting to make the most of new digital funds from across the world to support their activities.
Speakers: Charles Beckett, Agnes Vogt. Moderated by Neal Hoskins
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
What Can Data Tell Us?
1. Data and Culture for Publishers
Roger Magoulas
Research Director
O’Reilly Media
roger@oreilly.com
2. Data at O’Reilly
O’Reilly Research
Data
– BookScan-based Retail POS Mart (Computers)
– Ebooks / Ecommerce
• O’Reilly only
– Conferences
– Job Post DB
– Facebook and MySpace application usage
– Apple iPhone AppStore ranks
• iBook Ranks since iPad release
– Top Twitter Users and Usage
– US Government Data Analysis
• CTO Jobs Studies / HHS Jobs Trends
Analysis / Access / Communications
– Research Portal
– Sync’d
3. What’s New in Data
Stats and Analysis as the ‘sexy’ job of the coming era
More data, more types of data and big data tools
Increased skills integration
Cross-Discipline
Machine Learning / Natural Language Processing
O’Reilly Strata Conference
4. The Why of Data
Tell Stories
– Communicate results
• make vivid, memorable, social
Input to Decision Processes
– Provide relevant information, not decisions
Real-Time Integration
– Integrating data / analysis / modeling / predictions into real-
time processes
• Feedback for users
• Self-tuning algorithms stay relevant
– Support database of expectations
5. Data Science
Data Management Analysis / Insight
– Loading – Exploration
– Big Data – Visualization
– Parallelism – Collective Intelligence
• Teasing Insights from Crowd Behavior
– Sandboxes
• Crowdsourcing / Mechanical Turks
– Integration with Analysis
– Machine Learning
• Classifying / Deduplication
Data Collection
• Clustering
– Scraping / Feeds / APIs • Summarizing
– Parsing • Sentiment
• Human Review
Data Integration – Natural Language Processing
– Identification / Association • Entity Extraction
– Deduplication / Conditioning • Disambiguation
– Statistics / Probability
Data Organization – Predictive Modeling
Culture / Organizational Behavior
– Quantitative Culture
– Organize to Learn / Experiment
13. Taxonomies
To make sense of data:
– Categorize
– Orthogonal Dimensions
– Hierarchical
• Drill Up / Drill Down
– Dynamic
BISAC for books
– Not enough for dynamic topics like computers / technology
Taxonomies are hard!
– Resources, Concentration, Ambiguity, Vigilance, Time,
Madness
– Maintaining Multiple Rollups
– A Messy Process
21. Publishing Data - Supply Side Analytics Example
Best Seller Share - Top 5 Books
– Sustained Change Since Holiday Sales Season
– Hypothesis: Less Retail Shelf Space Focuses (Impulse)
Demand to Fewer Titles or Perfect Storm
15%
Top 5 Books % Share
10%
5%
0%
09
09
09
09
10
10
10
10
11
11
11
11
12
20
20
20
20
20
20
20
20
20
20
20
20
20
4/
4/
4/
4/
4/
4/
4/
4/
4/
4/
4/
4/
4/
/0
/0
/0
/0
/0
/0
/0
/0
/0
/0
/0
/0
/0
01
04
07
10
01
04
07
10
01
04
07
10
01
22. Publishing Data - Supply Side Analytics Example
Best Seller Share - Top 5 Books
– Sustained Change Since Holiday Sales Season
– Hypothesis: Less Retail Shelf Space Focuses (Impulse)
Demand to Fewer Titles or Perfect Storm
15%
Top 5 Books % Share 2009
2010
2011
2012
10%
5%
0%
w01
w03
w05
w07
w09
w11
w13
w15
w17
w19
w21
w23
w25
w27
w29
w31
w33
w35
w37
w39
w41
w43
w45
w47
w49
w51
23. Publishing Data - Supply Side Analytics Example
Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
50%
Javascript YoY
25%
0%
-25%
big pub huge pub O'Reilly All
24. Publishing Data - Supply Side Analytics Example
Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• Dominant Share
30
Javascript Books 2010
2011
20
50%
Javascript YoY 10
0
25% 80% big pub huge pub O'Reilly
Javascript Units
60% 2010 share
2011 share
0% 40%
20%
-25%
0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly
25. Publishing Data - Supply Side Analytics Example
Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
2,000
50% Javascript Units/Book
Javascript YoY
1,500
25%
1,000
0%
500
-25% 0
big pub huge pub O'Reilly All big pub huge pub O'Reilly
26. Publishing Data - Supply Side Analytics Example
Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
200%
50% Javascript Units/Book
Javascript YoY
150%
25%
100%
0%
50%
-25% 0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly
27. Publishing Data - Supply Side Analytics Example
Publisher Efficiency by Topic - Javascript
– Hypothesis - Market Saturation
• Unit Sales Up
• O’Reilly growing faster than market
• High Share
• Efficient Publishing Program
– Not Saturated
200%
50% Javascript Units/Book
Javascript YoY
150%
25%
100%
0%
50%
-25% 0%
big pub huge pub O'Reilly All big pub huge pub O'Reilly
28. What Can You Do
Get Data Savvy
– Find a Ben, Math Club
Keep Analysis Close to Data
Go Outside
Encourage Collaboration / Critical Vetting
– Internal and External
Experiments as Fundamental Business Process
– New Risk: Measuring cost of what you won’t learn
Supply-Side Analytics
– Sandbox
Communicate with Stories
Scale Up Decision Making to Match Data
38. Quantitative Culture
Functionally Integrated Teams
– Responsible for all steps of analysis:
• Data Management / Munging
• Analysis / Visualization / Story Telling
Encourage collaborative development
– Cross-Function Coordination (e.g., via Google Docs)
– Technical Cross-Training
• Use Agile and Extreme Programming Methods
Share processes, techniques, tool knowledge, results
– Encourage integrated approach
– Open source philosophy
Experimentation as Fundamental Process
Supply-Side Analytics
Analytic Sandbox
– Provide access to large, flexible, high performance data management
systems
Scale Up Decision Making to Match Data
39. O'Reilly Media
Publishing / Conferences / On-line / Radar / Research
Changing the world by spreading the knowledge of
innovators
We’re essentially story-tellers
Democratizing Innovation
“The Future is here, it’s just not evenly distributed”
– William Gibson
Research supports O’Reilly mission of changing the world by spreading knowledge of innovators\n Quantitative and qualitative research on technology adoption\n to support publishing / conferences and beyond\nThree people: quant, ops, data\n many shared duties\n access to fantastic O’Reilly social network\n informs our perspective\ndmart - lots of value add\n 11+ dimensions / 1K topic taxonomy / data from 2004\nemart - deal analysis\nJob data - messy\n 15 Tb / 1.8 b rows\n mostly for tech adoption, HHS project\nApple iBook\n iPad first day to figure out data we could use\nTwitter - sentiment and event analysis\n
Google / Facebook / Zynga / LinkedIn\nText, sensors\nCollaboration/Integration of data disciplines to speed and deepen analysis\n do everything, no waiting\nWide net for data skills and technology - physics => science; biostats => business\nBeyond stats - ML and NLP for unstructured text\n people as the last mile\n\n\nData Science is a meme more than an actual field, we refer to a set of skills that improve knowledge work productivity and effectiveness; the meme is based on our seeing how people with these skills have made an impact at companies like Twitter, Facebook, LinkedIn and Google\n Google the entire search engine is an example of an applied data science applications\n Google Insights used for analysis that showed the swine flu outbreak faster than CDC data\nNo new components, what is new is the level of integration between components to provide more sophisticated insights from increasingly large data sets\n Moving beyond reporting to analysis, insight, predictions\n New tools: big data management, data munging\n New Sources: web, sensors\n New data types: unstructured, graphs, multi-media\n New tasks: classifying, summarizing, sentiment analysis\n New techniques: collective intelligence, machine learning, natural language processing, modeling\nHal Varian, Google chief economist, quote from interview\nMore data\n Sensors, smart mobile devices, web-based\n Unstructured text, graphs, images, audio, video\nSkills Collaboration/Integration\n The integration we’ll focus on is based on the data science frame we present in the next slide (data management, data munging, analysis and presentation)\nCross discipline analysis\n Science learning from business and business learning from science\n Biostats - Many of the data science folks we know and follow come from biostats backgrounds (e.g., Mike Driscoll, Brian Dolan, Pete Skomoroch, Joe Adler)\n Other examples: genetic algorithms used to run business simulations and crowd control, randomized control trials used for economics and other social science, graph theory used for social network analysis\nStrata Conference (strataconf.com)\n The business of data\n Focus on integrating skills, collaborative work, building a community\n Amazing buy-in by data science folks we most respect\n Technology tracks\n Including pre-conference classes on machine learning and math\n Business tracks\nThemes - we focus more on folks building their own tools than on commercial products\n\nhttp://flowingdata.com/2009/06/04/rise-of-the-data-scientist/\n
We’re wired to respond to and remember stories, take advantage of innate human characteristic\nData is not a black box you buy, it’s a process you follow, an input to decisions, part of an experiment-based learning culture\nThe output (the why) of data science:\nHumans are wired to respond to and remember stories\n Analytic types can sometimes get caught up telling the story of how they performed the a study or worked toward a result, that is not the story to relate (not in this context, more on technique sharing later)\n Supercrunchers by Ian Ayres provides good examples of how to package analysis for quick cognition and retelling (more on Supercrunchers shortly)\n Data stories can be used to help promote and reinforce a data-oriented culture, stories tend to spread quickly, helping spread the lessons from the analysis throughout an organization\n Stories a heuristic to remember data, helps to make them social\nDecision Support\n Think of how data analysis can help with many decision processes\n Don’t rely on results to make decisions, results should lead to better understanding or to asking more questions\nTell a story; show anomalies (exceptions); show trends\n don’t show numbers, always show magnitude (especially when showing RoC)\nReal-Time Integration\n How data science gets put to work in an application context\n In many cases cloud enabled, sophisticated analytics computed on server and delivered through a relatively thin, often browser-based, client (e.g., recommendation engines)\n Some of the most interesting data science work supports real-time analysis\n Web analytics\n Recommendation engines\n Who you might know apps\n Ad tracking and analysis\n Anti-fraud analysis\n Data center / operations support (trouble alerts, reconfiguring / redeploying resources based on demand, energy management, cost management)\n Mobile device voice recognition, computer vision, translation\n Real-Time Analysis via a message bus architecture\n db of expectations - sense and respond hallmark of all living things and now we’re building computer systems around this (e.g., recommendation engines that use multiple models and reformulate 20 times per day)\n
The geeky stuff\nHow we see the space\nConditioning not quality - a cost / benefit decision\nAnalysis/Insight - exploring the cave of the unknown\nNeed culture to make most of data and insight\n Understand the message\n Address innumeracy\n Value results appropriately\n Think experiments\n Stay curious - keep asking questions\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Most pandering you’ll likely see at conference\nJoe - asking questions\nLaura - math major\nMike Hendrickson, Allen Noren, Laurie Petrycki, Sara Winge\n
Linnaeus ref: categorizing fauna and flora\nBISAC great when it works\n Dynamic example: rise of tablets, app programming\n Ebooks, videos, one-offs, conference content, oh my\nCategorized > 25K+ books, whew!\n triple check\n new books / new topics / new relations ships\nAmbiguity - some books hard to categorize, if multiple categorize, managing aggregate rollups (primary cat)\nNeed to maintain consistency for multiple rollups\nFour rollups: topic, retail, division, cust (ecommerce)\nMachine Learning? it’s possible\n
Research Portal - 3 clicks to a book\nComplete market, not just us\n
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Weekly; delivered via E-mail to prompt reading\nRegular reporting\nOffbeat to keep interest\n
Lies, Damn Lies, and Statistics\nDefault Excel\n
Default Excel\nCould miss the unusually flat period\n
Data a popular topic - help explain opportunity for O’Reilly\nComplements book sales data\n better coverage for mature technologies\nSAS roughly matching the market\nMachine Learning & Hadoop smaller but growing\n
Data a popular topic - help explain opportunity for O’Reilly \nComplements book sales data\n better coverage for mature technologies\nSAS roughly matching the market - mature technology\nMachine Learning & Hadoop smaller but growing\nDrill down gives better sense of growth in these nascent fields\nMagnitude + rate of change\n
Top books: (consumer oriented) iPad, iPhone, Kindle\n bought to complement presents\nMonitor, consider implications to sales strategy\nB&N pushing Nook Book\nSeasonal\n \n
Top books: (consumer oriented) iPad, iPhone, Kindle\n bought to complement presents\nMonitor, consider implications to sales strategy\nB&N pushing Nook Book\nSeasonal\n \n
Unit sales up in a down market\nO’Reilly growing faster than market\n\n
Unit sales up in a down market\nO’Reilly growing faster than market\nDominant share on similar publishing program\n 2011 - rising to 66% share\n
Unit sales up in a down market\nO’Reilly growing faster than market\nDominant share on similar publishing program\n 2011 - rising to 66% share\nMuch higher units / book ratio\n \n
Unit sales up in a down market\nO’Reilly growing faster than market\nDominant share on similar publishing program\n 2011 - rising to 66% share\nMuch higher units / book ratio\n another view - 100% represents sales for average book in topic\n O’Reilly well above\n \n
Unit sales up in a down market\nO’Reilly growing faster than market\nDominant share on similar publishing program\n 2011 - rising to 66% share\nMuch higher units / book ratio\n another view - 100% represents sales for average book in topic\n O’Reilly well above\nConsider publishing\nNote arc of analysis\n \n
Data Savvy - Get a book, take a class\nLet analysis requirements drive how data organized\n learn from agile\nCritical Vetting\n Smell Test\n ref Jonah Lehrer teams article re: constructive criticism\nGo Outside - augment your data w/ outside sources\n Gov (Census, BLS), Factual, Scraping\n crowdsourcing\nExperiment\n Test hypothesis; learn from everything - feedback loops\nSupply-Side Analytics - Let analysts explore (Google 20%)\n Sandbox - create big data areas w/ quick spin-up and full data management support (cloud)\nNumbers w/ no story don’t resonate, don’t lead to action\n Occam’s razor - look for simplest analysis path\n\n\nSmall team, but with enough a range of expertise, covering the data management and data insight skills required to perform an analysis and explain the results\nThe integrated team is design to prevent process road blocks, and to encourage everyone to pick up the skills from others\n Don’t set the expectation that everyone can acquire and become expert at all the data science skills, but they should have enough knowledge to get basic tasks done on their own - not to have to wait if others are busy\nOnline coordination tools like Google Docs allows more flexibility, and geographic independence\nAgile / Extreme Programming for training\n Double folks up on tasks to encourage cross training\n Encourage walk-throughs and team vetting of intermediate steps to help facilitate organization learning and expectations\nCreates example of how to organize and how to integrate skills to increase analytic productivity\nSharing (covered in previous slides)\n Open source style over-sharing to build skills\n Sharing techniques and tools to get feedback, improvement, learn\n Other recommendations covered in earlier slide: intra-company discussion, join public discussions and meet-ups, actively share\nExperimentation - learning as key goal of all processes, consider risk of missing opportunity to learn\nSupply-side analytics (as covered in previous slides)\n give data science team time and resources to run their own, uncommissioned studies\n Shows importance of analysis function, demonstrates data-driven culture\n Take advantage of organizational and data knowledge accumulated in the analysis group\nAnalytic Sandbox\n Provide an easy-to-configure, quick-to-spin up facility for quickly building fast query data stores - a cloud like facility that provides fast cycling for computational analysis\n No or easy requisition process\n Big storage to allow experiments in data organization that can speed analysis iteration cycles\n MapReduce can improve analytic productivity by providing fast, parallel execution of procedural logic beyond what SQL on its own can provide (e.g., logic between rows not covered by aggregate functions(\n Hadoop or MPP databases (Aster, Greenplum, Vertica)\nIntegrated Tools\n E.g., Datameer, Mathematica, Karmasphere, Big Sheets, Splunk, Palintir \n The tools listed all tend to perform more of the analysis functions, e.g., mixing data loading, transforming and organizing data, built-in analysis tools and built-in visualization; some of the tools have provide easy access to web based data\nAvoid becoming paralyzed by possibilities (Driscoll CIA example)\n
data - doesn’t make decisions\n\n
data doesn’t make decisions\n\n
or solve problems on its own\n\n
There will be issues\n\n
Data a process - w/ no end\nRequires resources, commitment, training, vigilance - find O’Reilly books\nBest analysis poses more questions than it answers\nRemember magnitude, direction, rate of change\nArt and Science\n designing an experiment still an art\n Freakonomics / Supercrunchers for inspiration\nLike many hard things - its a lot of fun\n Ref: Improving Cognitive Functioning article, Doing things the hard way as one of five keys to increasing cognitiion\n Others: Be creative, Constant Challenge\n\n
stay in the game\n\n
enlightenment and...\n\n
bliss\n\n
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Address problems large, enterprise scale organizations face optimizing the value of their data when they have distributed analytic silos and large, tightly controlled data stores\nStart integrating teams as example of a new way to work, in a cross-disciplinary fashion, with rapid, iterative development processes (Agile-like)\nSmall team, but with enough a range of expertise, covering the data management and data insight skills required to perform an analysis and explain the results\nThe integrated team is design to prevent process road blocks, and to encourage everyone to pick up the skills from others\n Don’t set the expectation that everyone can acquire and become expert at all the data science skills, but they should have enough knowledge to get basic tasks done on their own - not to have to wait if others are busy\nOnline coordination tools like Google Docs allows more flexibility, and geographic independence\nAgile / Extreme Programming for training\n Double folks up on tasks to encourage cross training\n Encourage walk-throughs and team vetting of intermediate steps to help facilitate organization learning and expectations\nCreates example of how to organize and how to integrate skills to increase analytic productivity\nSharing (covered in previous slides)\n Open source style over-sharing to build skills\n Sharing techniques and tools to get feedback, improvement, learn\n Other recommendations covered in earlier slide: intra-company discussion, join public discussions and meet-ups, actively share\nExperimentation - learning as key goal of all processes, consider risk of missing opportunity to learn\nSupply-side analytics (as covered in previous slides)\n give data science team time and resources to run their own, uncommissioned studies\n Shows importance of analysis function, demonstrates data-driven culture\n Take advantage of organizational and data knowledge accumulated in the analysis group\nAnalytic Sandbox\n Provide an easy-to-configure, quick-to-spin up facility for quickly building fast query data stores - a cloud like facility that provides fast cycling for computational analysis\n No or easy requisition process\n Big storage to allow experiments in data organization that can speed analysis iteration cycles\n MapReduce can improve analytic productivity by providing fast, parallel execution of procedural logic beyond what SQL on its own can provide (e.g., logic between rows not covered by aggregate functions(\n Hadoop or MPP databases (Aster, Greenplum, Vertica)\nIntegrated Tools\n E.g., Datameer, Mathematica, Karmasphere, Big Sheets, Splunk, Palintir \n The tools listed all tend to perform more of the analysis functions, e.g., mixing data loading, transforming and organizing data, built-in analysis tools and built-in visualization; some of the tools have provide easy access to web based data\nAvoid becoming paralyzed by possibilities (Driscoll CIA example)\n
O’Reilly and the Public Good:\n Support for CfA; work for HHS / NIH; Explicit support for open source\nO’Reilly - more than just books; first comm’l, ad supported web site, first to use collab filtering; coined open source; coined web 2.0\n Thought Leaders\n ran conference that named Open Source\n Named Web 2.0 and developed principles, including collective intelligence\n instigated unconference movement w/ Foo camp\n instigated DIY movement w/ Make\ndemocratizing innovation - MIT’s Eric Von Hipple, users as greatest source of innovation; cheaper tools; global communications and sourcing give users/innovators more power\nMake magazine a manifestation of democratizing innovation\nFundamentally we are storytellers\nwho would have thought amazon would own cloud computing, apple would own music biz, people would pay for apps\nO’Reilly has unparalleled access to a great technical social network\n events and reputation keeps us close to the community; we find out what they think is interesting; we have access to many social alpha geeks, not just nerds, many have started successful business or written wildly popular apps\n entrepreneurial\n subversive, disruptive, fail fast\n DIY / hacking\n amateur professionals\n open source / collaborative\n catalyst for alpha geek community\n foster cross disciplinary mixing\n international reach (recently in rome, milan and athens)\nMany start-ups pass by O’Reilly (incl: int’l)\nmonitor variety of app platforms, facebook, myspace\n heard about twitter when 12 users; youtube founders at Foo, 14 months before sale to google\nDavid Brooks - got idea for Alpa Geeks post from O’Reilly\nWe do geopolitical and industrial policy analysis for gov’t\nResearch - quantitative and qualitative research for internal and external clients\n
Complements book sales data\n better coverage for mature technologies\nPopular languages\n
Complements book sales data\n better coverage for mature technologies\nIncreasingly Popular languages\nJava - mature tech shows strength\n