Fifty years ago, a typical company on the S&P 500 stayed there for three-quarters of a century. Today, they last only fifteen years. Technological disruption has run roughshod through the boardrooms of the world.
At the same time, small startups with nothing to lose have become more methodical about iteration, experimentation, and innovation. Fueled by deep investment backing and unfettered by legacy distractions like regulation, customers, and infrastructure, they're turning into Billion-dollar ventures.
From lackluster jobs growth to tech speculation to the disruption of nearly every industry, the death of big companies is the elephant in the room. But can we teach the elephant to dance? Join author, entrepreneur, and Strata conference chair Alistair Croll for a look at how some large organizations are applying data-driven methods, a deliberate portfolio of innovation, and Lean approaches that help them survive—and even thrive—in a changing competitive landscape.
Search at Linkedin by Sriram Sankar and Kumaresh PattabiramanThe Hive
Search is an important and integrated part of the overall LinkedIn experience, and it takes many forms - such as Instant, SERP, Recruiter Search, Job Seeker, etc. Search needs to deal with both structured and unstructured content, and be personalized.
In this talk, Sriram will describe Linkedin unified infrastructure to support these different needs, and will provide some insights into our various approaches to search quality.
Search at Linkedin by Sriram Sankar and Kumaresh PattabiramanThe Hive
Search is an important and integrated part of the overall LinkedIn experience, and it takes many forms - such as Instant, SERP, Recruiter Search, Job Seeker, etc. Search needs to deal with both structured and unstructured content, and be personalized.
In this talk, Sriram will describe Linkedin unified infrastructure to support these different needs, and will provide some insights into our various approaches to search quality.
The Hive Think Tank: Rocking the Database World with RocksDBThe Hive
Igor Canadi, Facebook
Igor is a software engineer at Facebook where his job is making databases more awesome. He recently graduated from University of Wisconsin-Madison with Masters degree in Computer Science. During his time at UW-M, he worked with prof. Paul Barford in the area of internet measurement and analysis. Igor got his undergraduate degree from University of Zagreb in Croatia. During his undergraduate years, he founded and developed a local non-profit organization that focuses on educating talented high-school students.
Advanced Visual Analytics and Real-time Analytics at Platform scale by Brian ...The Hive
Some of the most demanding real-time big data driven platforms on the Internet today are in programmatic advertising and real-time bidding.
These platforms continuously ingest, store, analyze and act on billions of events and terabytes of data to personalize interactions with every click and swipe across websites, mobile apps, emails, social media, sensors and more. But that’s not enough. In order to win at auction, capture the user’s attention and drive revenue, they must continuously extract new insights with advanced visual analytics and combine these insights with real-time data to perform real-time analytics, moment-by-moment, all the time.
Brian Bulkowski, co-founder & CTO of Aerospike, an open source flash-optimized NoSQL database, will talk about the latest developments in storage and lead a discussion with Kiran about the challenges and opportunities created for analytics at platform scale.
Untethered health in a networked society by James MathewsThe Hive
Talk by James Mathews, Chairman, Health 2.0 India
CEO, Whiteboard Design Pvt Ltd at The Hive Big Data Think Tank Meetup - Healthcare 2.0 hosted at the EMC India.
BE YOUR OWN CLIENT: Monetizing Your Agency's Creativity Beyond AdvertisingVCU Brandcenter
Neil Patel, SVP, Content Strategy and Development at The Martin Agency and Mentor at 80amps (a new model venture incubator), and Eric Martin, Founding Partner at 80amps, gave this presentation at "The New Model Creative Director," the VCU Brandcenter's executive education program for creative direction, on August 6th, 2013 at the VCU Brandcenter in Richmond.
CUSTOMER CENTRIC ENTERPRISE - Big Data Part 1. Aligning IT and MarketingPetri Pekkarinen
Big Data is key for building corporate culture placing customer at the center of processes and strategy.
But it’s not easy.
This presentations is first part in a serious of presentations covering big data benefits for marketing from management, storage and software perspective.
The Hive Think Tank: Rocking the Database World with RocksDBThe Hive
Igor Canadi, Facebook
Igor is a software engineer at Facebook where his job is making databases more awesome. He recently graduated from University of Wisconsin-Madison with Masters degree in Computer Science. During his time at UW-M, he worked with prof. Paul Barford in the area of internet measurement and analysis. Igor got his undergraduate degree from University of Zagreb in Croatia. During his undergraduate years, he founded and developed a local non-profit organization that focuses on educating talented high-school students.
Advanced Visual Analytics and Real-time Analytics at Platform scale by Brian ...The Hive
Some of the most demanding real-time big data driven platforms on the Internet today are in programmatic advertising and real-time bidding.
These platforms continuously ingest, store, analyze and act on billions of events and terabytes of data to personalize interactions with every click and swipe across websites, mobile apps, emails, social media, sensors and more. But that’s not enough. In order to win at auction, capture the user’s attention and drive revenue, they must continuously extract new insights with advanced visual analytics and combine these insights with real-time data to perform real-time analytics, moment-by-moment, all the time.
Brian Bulkowski, co-founder & CTO of Aerospike, an open source flash-optimized NoSQL database, will talk about the latest developments in storage and lead a discussion with Kiran about the challenges and opportunities created for analytics at platform scale.
Untethered health in a networked society by James MathewsThe Hive
Talk by James Mathews, Chairman, Health 2.0 India
CEO, Whiteboard Design Pvt Ltd at The Hive Big Data Think Tank Meetup - Healthcare 2.0 hosted at the EMC India.
BE YOUR OWN CLIENT: Monetizing Your Agency's Creativity Beyond AdvertisingVCU Brandcenter
Neil Patel, SVP, Content Strategy and Development at The Martin Agency and Mentor at 80amps (a new model venture incubator), and Eric Martin, Founding Partner at 80amps, gave this presentation at "The New Model Creative Director," the VCU Brandcenter's executive education program for creative direction, on August 6th, 2013 at the VCU Brandcenter in Richmond.
CUSTOMER CENTRIC ENTERPRISE - Big Data Part 1. Aligning IT and MarketingPetri Pekkarinen
Big Data is key for building corporate culture placing customer at the center of processes and strategy.
But it’s not easy.
This presentations is first part in a serious of presentations covering big data benefits for marketing from management, storage and software perspective.
Social Media for Startups: StartGarden with notesPaul Kortman
Social Media has been around for a couple years now, but the tried and true strategies for entrepreneurs and intrapreneurs working in the lean-startup model aren't well known. In this Social Media for Startups class Paul will overview brief strategies for the major social platforms helping you achieve success in whatever phase of the startup you are in, from problem discovery, to problem validation, from problem/solution fit, to product/market fit. Learn how to leverage social media to acquire real customers!
How to provide a design which will actually sell? There is no direct answer, but there are clues. In this presentation I try to explain some of them, providing also my own, simple User Economy model.
Emerging theories of marketing and innovationavrch
a commemorative lecture paying tribute to Dr. V Kurien and Prof. Peter Drucker during the Drucker-Kurien Week celebrated at Development Management Institute, Patna on 21 Nov 2017
How To Train Your Computer - Peter WalkerPeter Walker
A.I. - Bots, Intelligent Assistants and Deep Learning. 2016 is the year that intelligent assistants and deep learning are becoming more abundant on web & mobile devices. From the big boys of artificial intelligence (Apple Siri, Google Now, Amazon Alexa, Microsoft Cortana, Facebook M & Messenger) to uses in medical facilities, we will examine how our lives are enriched and made simpler. Growing your business becomes a lot easier when you have a number of bots working on your behalf.
Quantum Computing (IBM Q) - Hive Think Tank Event w/ Dr. Bob Sutor - 02.22.18The Hive
Dr. Bob Sutor is Vice President for AI, Blockchain, and Quantum Solutions at IBM Research. In this role he is the R&D executive leading a large global group of scientists, software engineers, and designers who create and integrate leading edge science and technologies to give IBM's clients the most advanced solutions available. Our work is often mathematically-based and thus includes AI technologies like machine learning, deep learning, text and image analytics, statistics, predictive analytics, and optimization. Sutor co-leads the IBM Research effort to support IBM's commercial blockchain efforts with advanced innovations across a broad range of its embedded technologies. He leads the group developing the next generation software stack and algorithms for quantum computers.
Dr. Sutor has an undergraduate degree from Harvard College and a Ph.D. from Princeton University, both in Mathematics.
The Hive Think Tank: Rendezvous Architecture Makes Machine Learning Logistics...The Hive
Think Tank Event 10/23/2017, hosted by The Hive and presented by Ted Dunning, Chief Application Architect of MapR Technologies and Ellen Friedman of MapR Technologies.
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
The Hive Think Tank: Machine Learning Applications in Genomics by Prof. Jian ...The Hive
In this The Hive Think Tank talk, Professor Jian Ma introduces machine learning methods that can be used to help tackle some of the most intriguing questions in genomics and biomedicine. He discusses the research projects in his group to study genome structure and function, including algorithms to unravel complex genomic aberrations in cancer genomes and gene regulatory principles encoded in our genome, by utilizing
probabilistic graphical models and deep neural network techniques. The knowledge obtained from such computational methods can greatly enhance our ability to understand disease genomes.
The Hive Think Tank: The Future Of Customer Support - AI Driven AutomationThe Hive
The Hive Think Tank Panel Discussion moderated by Kate Leggett (Forrester) with panelists: Allan Leinwand (ServiceNow), Nitin Narkhede (Wipro), Jason Smale (Zendesk), Dan Turchin (Neva). The future of customer support is AI-driven virtual agents. Soon, we’ll interact conversationally with bots that know who we are, how we’re impacted, and what we need. Soon, the capabilities of virtual agents will far exceed those of today’s best human agents. We’ll receive support that is more reliable than friends, more accurate than social media, and less frustrating than waiting on hold.
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive
Over the next 15 years, India's growth will be fueled by its startups. Today, there are over 20,000 startups in India that have created a value of $80 billion and employ 325,000 people. Over the next ten years, by 2025, there will be 100,000 startups in the country that would have created over $500 billion of value and employ 3.2 million people.
This talk is about India's growth over the next 15 years and the prominent role that entrepreneurs and startups will play in its rapid evolution.
The Hive Think Tank: The Content Trap - Strategist's Guide to Digital ChangeThe Hive
In this The Hive Think Tank talk Harvard Business School Professor of Strategy Prof. Bharat Anand shares his insights on the Digital innovation trends that are shaping the way organizations will act in the future.
In this talk, Professor Anand presents the findings from his forthcoming book. To answer these questions, Anand examines a range of businesses around the world, from Chinese internet giant Tencent to Scandinavian digital trailblazer Schibsted, from The New York Times to The Economist, and from talent management to the future of education.
In this The Hive Think Tank talk, Heron team provides an introduction to Heron, how it is being used at Twitter and shares an operating experiences and challenges of running Heron at scale. They recently announced the open sourcing of Heron under the permissive Apache v2.0 license. Heron has been in production nearly 2 years and is widely used by several teams for diverse use cases. Prior to Heron, Twitter used Apache Storm, which we open sourced in 2011. Heron features a wide array of architectural improvements and is backward compatible with the Storm ecosystem for seamless adoption.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
The Hive Think Tank: Translating IoT into Innovation at Every Level by Prith ...The Hive
In this presentation Prith Banerjee discusses how a sustainable future must become radically more efficient with the way we use energy. He shared how the Internet of Things (IoT) and the convergence of Operational Technology (OT) and Information Technology (IT) are enabling Schneider Electric's innovation at every level, redefining power and automation for a new world of energy which is more electric, decarbonized, decentralized and digitized. Prith shared how, in this new world of energy, Schneider ensures that Life Is On everywhere, for everyone and at every moment. He also shared a set of IoT predictions for the future, based on findings of the company’s recent IoT Survey of 2,500 top business executives.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
9. The problem was framing:
Blockbuster thought it was in the video
store management business. Netflix
realized it was in the entertainment
delivery business.
Thursday, August 14, 14
11. Clay Christensen, The Innovator’s Dilemma
CostperMB
$1000
$100
$10
$1
Time
14”
M
ainfram
e
8”
M
inicom
puter
5.25”
Desktop
3.5”
N
otebook
Thursday, August 14, 14
12. Technologies
outstrip what
the market
needs, driven
by feedback
from the
“best”
current
customer.
Clay Christensen, The Innovator’s Dilemma
$1000
$100
$10
$1
Time
8” 5.25”
High end
customer
Low end
customer
Thursday, August 14, 14
13. The new
market has
different criteria
for success,
which are
uninteresting to
incumbents.
Clay Christensen, The Innovator’s Dilemma
$1000
$100
$10
$1
Time
Storage
capacity
Portability
Thursday, August 14, 14
15. Amazon Web Services and the
server value network
http://www.theregister.co.uk/2013/04/18/amazon_2_trillion_s3/
http://www.saphana.com/community/blogs/blog/2013/04/18/
hanalgorithmics--efficiency-by-design-with-sap-hana--part-1
Thursday, August 14, 14
16. Amazon Web Services and the
server value network
Bare metal equipment
• Density
• Heat
• GHz
• MIPS
Cloud computing
• Instances
• Objects
• Spinup time
• Scaleout
Capex, financing,
TCO, ROI
Opex, demand, time
to result
CIO, enterprise IT CTO, coder, app owner,
line of business, startup
Value
criteria
Money
Buyer
Thursday, August 14, 14
17. Server
vendors
missed the
disruption and
the change in
the value
network.
Clay Christensen, The Innovator’s Dilemma
$1000
$100
$10
$1
Time
Elastic resources mean we can scale
up to huge, and shrink costs when not in
use.
Physical servers:
MIPS, heat, density,
cost per cycle.
Sold to CIOs Cloud computing:
Opex cost, time to spin up,
scaleout, objects stored
Sold to developers &
marketers
Thursday, August 14, 14
25. Times a song in “heavy
rotation” is played daily
0
15
30
2007 2012
Thursday, August 14, 14
26. Why now?
Second: It’s no longer about whether
you can build it—it’s about whether
anyone will care.
Thursday, August 14, 14
27. The Attention Economy
“What information consumes
is rather obvious: it consumes
the attention of its recipients.
Hence a wealth of information
creates a poverty of attention, and a
need to allocate that attention efficiently
among the overabundance of
information sources that might
consume it.”
(Computers, Communications and the Public Interest, pages 40-41,
Martin Greenberger, ed., The Johns Hopkins Press, 1971.)Herbert Simon
Thursday, August 14, 14
31. Three kinds of innovation
Sustain/core
(optimizing for more of the same)
Innovate/adjacent
(introduce nearby product,
market, or method)
Disrupt/transformative
(Fundamentally changing
the business model)
Improve along
current metrics...
...or alter
the rate of
improvement
Switch to a new
value model
Change the business
model entirely
Thursday, August 14, 14
32. Improvement Adjacency Remodeling
Do the same,
only better.
Explore what’s
nearby quickly
Try out new
business models
Lean approaches apply, but the metrics vary widely.
Sustain/
core
Innovate/
adjacent
Disrupt/
transformative
Thursday, August 14, 14
34. Sustaining
innovation
is about
more of
the same.
(says Sergio Zyman)
More things
To more people
For more money
More often
More efficiently
Supply chain optimization
Per-transaction cost reduction
Loyal customer base that returns
Demand prediction, notification
Maximum shopping cart
Price skimming/tiering
Highly viral offering
Low incremental order costs
Inventory increase
Gifting, wish lists
Thursday, August 14, 14
35. Adjacent innovation is about changing
one part of the model in a way that
alters the value network.
Thursday, August 14, 14
39. Selling the same product to an
adjacent market in the same
way.
Of P&G’s 38 brands, only 19 were sold in Asia as of 2011
Market expansion is seldom selling the same thing to new people. In
Asia, P&G needed to
Align pricing with novelty (prestige, mass-tige, over-the-counter)
Change consumer expectations (moving from dilutes to
concentrates)
Adjust positioning and ingredients such as white fungus, ginseng,
and the parasitic cordyceps
Thursday, August 14, 14
40. Selling the same product to the
same market in a new way.
Thursday, August 14, 14
41. (At this point, observant Intrapreneurs
should be asking, should P&G be in
the house cleaning business?
And that would be transformative.)
Thursday, August 14, 14
42. Transformative innovation is about
taking a leap, changing more than one
dimension simultaneously in search of
a new business model.
Thursday, August 14, 14
44. If sustaining, incremental innovation
produces linear growth, then
disruptive, transformative innovation
produces exponential growth.
Thursday, August 14, 14
46. Significant market
850K full-time law enforcement officers in
the US; 700K state/local; 525K patrol
officers
130M incident reports/y. 70M new
incidents; 200K involve use of force
Only 31% of local police agencies keep
computer files on use-of-force incidents
Strong product benefits
Exonerates the officer 96% of the time.
47% percent increase in charges and
summons (2007)
Patrol officers spend 15-25% of their time
writing incident reports, recorded evidence
reduces this by 22%, meaning 50m more
on patrol
Challenges
New business model
Pricing unclear
SaaS offering
Compliance and governance
Unions, regulation, chain of evidence
Changing the current model (radio is
everything)
Transformative incubation:
Taser evidence.com
Thursday, August 14, 14
49. “Efficiency is tied to
analytics. We’ll still look
for new materials, or
for the physics of
devices, but the
analytics ... is what’s
really untapped.”
Thursday, August 14, 14
51. Intrapreneur:
Someone working to produce
disruptive change in an organization
that has already found a sustainable,
repeatable business model.
Thursday, August 14, 14
52. Also: a pariah.
Successful innovators share certain attributes.
Bad listener: Wilfully ignore feedback from your best customers.
Cannibal: If successful, destroying existing revenue streams.
Job killer: Automation & lower margins are your favorite tools.
Security risk: Advocate of transparency, open data, communities.
Narcissist: Worry constantly about how you’ll get attention.
Slum lord: Sell to those with less money, deviants, and weirdos.
Thursday, August 14, 14
53. Some things smart Intrapreneurs
do differently
Thursday, August 14, 14
55. The plural of anecdote is not data.
(Roger Brinner)
Thursday, August 14, 14
56. Don’t sell what you can make. Make what you can sell.
Kevin Costner is a lousy entrepreneur.
Thursday, August 14, 14
57. The core of Lean
is iteration.
Thursday, August 14, 14
58. Everyone’s idea is
the best right?
People love
this part!
(but that’s not always
a good thing)
This is where
things fall apart.
No data, no
learning.
Thursday, August 14, 14
59. Companies that use data-driven
analytics instead of intuition have
5%-6% higher productivity and
profits than competitors.
Brynjolfsson, Erik, Lorin Hitt, and Heekyung Kim. "Strength in Numbers: How Does Data-Driven
Decisionmaking Affect Firm Performance?." Available at SSRN 1819486 (2011).
2011 MIT study of 179 large publicly traded firms
Thursday, August 14, 14
60. Empathy
Stickiness
Virality
Revenue
Scale
E-
commerce
SaaS Media
Mobile
app
User-gen
content
2-sided
market
Interviews; qualitative results; quantitative scoring; surveys
Loyalty,
conversion
CAC, shares,
reactivation
Transaction,
CLV
Affiliates,
white-label
Engagement,
churn
Inherent
virality, CAC
Upselling,
CAC, CLV
API, magic #,
mktplace
Content,
spam
Invites,
sharing
Ads,
donations
Analytics,
user data
Inventory,
listings
SEM, sharing
Transactions,
commission
Other
verticals
(Money from transactions)
Downloads,
churn, virality
WoM, app
ratings, CAC
CLV,
ARPDAU
Spinoffs,
publishers
(Money from active users)
Traffic, visits,
returns
Content
virality, SEM
CPE, affiliate
%, eyeballs
Syndication,
licenses
(Money from ad clicks)
Thursday, August 14, 14
62. Frame it like a study
Product creation is almost
accidental.
Unlike a VC or startup, when
the initiative fails the
organization still learns.
http://www.flickr.com/photos/creative_tools/8544475139
Thursday, August 14, 14
63. When in doubt, collect data
From tackling the FTA rate to
visualizing the criminal justice
supply chain.
Thursday, August 14, 14
64. Use data to create a taste for
data
Sitting on Billions of rows of
transactional data
David Boyle ran 1M online surveys
Once the value was obvious to
management, got license to dig.
Thursday, August 14, 14
68. Focus on the desired behavior, not just
the information.
http://www.psychologytoday.com/blog/yes/
200808/changing-minds-and-changing-towels
26% increase in towel
re-use with an appeal
to social norms; 33%
increase when tied to
the specific room.
Energy Conservation “Nudges” and Environmentalist
Ideology: Evidence from a Randomized Residential Electricity
Field Experiment - Costa & Kahn 2011
The effectiveness of energy
conservation “nudges” depends on
an individual’s political ideology ...
Conservatives who learn that their
consumption is less than their
neighbors’ “boomerang” whereas
liberals reduce their consumption.
Thursday, August 14, 14
72. Twitter’s 140-character
limit isn’t arbitrary. It’s
constrained by the size
http://i.i.cbsi.com/cnwk.1d/i/tim/2011/11/18/
sms_screen_twitter_activity_stream_270x405.png
Thursday, August 14, 14
73. Innovation portfolios at big companies
Core Adjacent Transformative
70% 20% 10%
Investment
70%20%10%
Return
Thursday, August 14, 14
74. “The most important figures that one
needs for management are unknown
or unknowable, but successful
management must nevertheless take
account of them.”
Lloyd S. Nelson
Thursday, August 14, 14
75. Pic by Twodolla on Flickr. http://www.flickr.com/photos/twodolla/3168857844
Thursday, August 14, 14