Tag: marketing

Predictive Analytics World Events in 2012

A new line up of Predictive Analytics World and Text Analytics World conferences and workshops are coming March through July, plus see the save-the-dates and call-for-speakers for events in Sept, Oct, and Nov.

CONFERENCE: Predictive Analytics World – San Francisco

March 4-10, 2012 in San Francisco, CA
http://predictiveanalyticsworld.com/sanfrancisco/2012
Discount Code for $150 off: AJAYBP12

CONFERENCE: Text Analytics World – San Francisco
March 6-7, 2012 in San Francisco, CA
http://textanalyticsworld.com/sanfrancisco/2012
Discount Code for $150 off: AJAYBP12

VARIOUS ANALYTICS WORKSHOPS:
A plethora of 1-day workshops are held alongside PAW and TAW
For details see: http://pawcon.com/sanfrancisco/2012/analytics_workshops.php

SEMINAR: Predictive Analytics for Business, Marketing & Web
March 22-23, 2012 in New York City, NY
July 26-27, 2012 in São Paulo, Brazil
Oct 11-12, 2012 in San Francisco
A concentrated training program lead by PAW’s chair, Eric Siegel
http://businessprediction.com

CONFERENCE: Predictive Analytics World – Toronto
April 25-26, 2012 in Toronto, Ontario
http://predictiveanalyticsworld.com/toronto/2012
Discount Code for $150 off: AJAYBP12

CONFERENCE: Predictive Analytics World – Chicago
June 25-26, 2012 in Chicago, IL
http://www.predictiveanalyticsworld.com/chicago/2012/
Discount Code for $150 off: AJAYBP12

 

From Ajay-

CONTEST- If you use the discount code AJAYBP12, you will not only get the $150 off, but you will be entered in a contest to get 2 complementary passes like I did last year . Matt Stromberg won that one

http://www.decisionstats.com/contest-2-free-passes-to-predictive-analytics-world/

 

see last year results-

http://www.decisionstats.com/congrats-to-matt-stromberg-winner-2-free-passes-to-paw-new-york/

Predictive analytics in the cloud : Angoss

I interviewed Angoss in depth here at http://www.decisionstats.com/interview-eberhard-miethke-and-dr-mamdouh-refaat-angoss-software/

Well they just announced a predictive analytics in the cloud.

 

http://www.angoss.com/predictive-analytics-solutions/cloud-solutions/

Solutions

Overview

KnowledgeCLOUD™ solutions deliver predictive analytics in the Cloud to help businesses gain competitive advantage in the areas of sales, marketing and risk management by unlocking the predictive power of their customer data.

KnowledgeCLOUD clients experience rapid time to value and reduced IT investment, and enjoy the benefits of Angoss’ industry leading predictive analytics – without the need for highly specialized human capital and technology.

KnowledgeCLOUD solutions serve clients in the asset management, insurance, banking, high tech, healthcare and retail industries. Industry solutions consist of a choice of analytical modules:

KnowledgeCLOUD for Sales/Marketing

KnowledgeCLOUD solutions are delivered via KnowledgeHUB™, a secure, scalable cloud-based analytical platform together with supporting deployment processes and professional services that deliver predictive analytics to clients in a hosted environment. Angoss industry leading predictive analytics technology is employed for the development of models and deployment of solutions.

Angoss’ deep analytics and domain expertise guarantees effectiveness – all solutions are back-tested for accuracy against historical data prior to deployment. Best practices are shared throughout the service to optimize your processes and success. Finely tuned client engagement and professional services ensure effective change management and program adoption throughout your organization.

For businesses looking to gain a competitive edge and put their data to work, Angoss is the ideal partner.

—-

Hmm. Analytics in the cloud . Reduce hardware costs. Reduce software costs . Increase profitability margins.

Hmmmmm

My favorite professor in North Carolina who calls cloud as a time sharing, are you listening Professor?

Analytics for Cyber Conflict

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in –

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt

Interview JJ Allaire Founder, RStudio

Here is an interview with JJ Allaire, founder of RStudio. RStudio is the IDE that has overtaken other IDE within the R Community in terms of ease of usage. On the eve of their latest product launch, JJ talks to DecisionStats on RStudio and more.

Ajay-  So what is new in the latest version of RStudio and how exactly is it useful for people?

JJ- The initial release of RStudio as well as the two follow-up releases we did last year were focused on the core elements of using R: editing and running code, getting help, and managing files, history, workspaces, plots, and packages. In the meantime users have also been asking for some bigger features that would improve the overall work-flow of doing analysis with R. In this release (v0.95) we focused on three of these features:

Projects. R developers tend to have several (and often dozens) of working contexts associated with different clients, analyses, data sets, etc. RStudio projects make it easy to keep these contexts well separated (with distinct R sessions, working directories, environments, command histories, and active source documents), switch quickly between project contexts, and even work with multiple projects at once (using multiple running versions of RStudio).

Version Control. The benefits of using version control for collaboration are well known, but we also believe that solo data analysis can achieve significant productivity gains by using version control (this discussion on Stack Overflow talks about why). In this release we introduced integrated support for the two most popular open-source version control systems: Git and Subversion. This includes changelist management, file diffing, and browsing of project history, all right from within RStudio.

Code Navigation. When you look at how programmers work a surprisingly large amount of time is spent simply navigating from one context to another. Modern programming environments for general purpose languages like C++ and Java solve this problem using various forms of code navigation, and in this release we’ve brought these capabilities to R. The two main features here are the ability to type the name of any file or function in your project and go immediately to it; and the ability to navigate to the definition of any function under your cursor (including the definition of functions within packages) using a keystroke (F2) or mouse gesture (Ctrl+Click).

Ajay- What’s the product road map for RStudio? When can we expect the IDE to turn into a full fledged GUI?

JJ- Linus Torvalds has said that “Linux is evolution, not intelligent design.” RStudio tries to operate on a similar principle—the world of statistical computing is too deep, diverse, and ever-changing for any one person or vendor to map out in advance what is most important. So, our internal process is to ship a new release every few months, listen to what people are doing with the product (and hope to do with it), and then start from scratch again making the improvements that are considered most important.

Right now some of the things which seem to be top of mind for users are improved support for authoring and reproducible research, various editor enhancements including code folding, and debugging tools.

What you’ll see is us do in a given release is to work on a combination of frequently requested features, smaller improvements to usability and work-flow, bug fixes, and finally architectural changes required to support current or future feature requirements.

While we do try to base what we work on as closely as possible on direct user-feedback, we also adhere to some core principles concerning the overall philosophy and direction of the product. So for example the answer to the question about the IDE turning into a full-fledged GUI is: never. We believe that textual representations of computations provide fundamental advantages in transparency, reproducibility, collaboration, and re-usability. We believe that writing code is simply the right way to do complex technical work, so we’ll always look for ways to make coding better, faster, and easier rather than try to eliminate coding altogether.

Ajay -Describe your journey in science from a high school student to your present work in R. I noticed you have been very successful in making software products that have been mostly proprietary products or sold to companies.

Why did you get into open source products with RStudio? What are your plans for monetizing RStudio further down the line?

JJ- In high school and college my principal areas of study were Political Science and Economics. I also had a very strong parallel interest in both computing and quantitative analysis. My first job out of college was as a financial analyst at a government agency. The tools I used in that job were SAS and Excel. I had a dim notion that there must be a better way to marry computation and data analysis than those tools, but of course no concept of what this would look like.

From there I went more in the direction of general purpose computing, starting a couple of companies where I worked principally on programming languages and authoring tools for the Web. These companies produced proprietary software, which at the time (between 1995 and 2005) was a workable model because it allowed us to build the revenue required to fund development and to promote and distribute the software to a wider audience.

By 2005 it was however becoming clear that proprietary software would ultimately be overtaken by open source software in nearly all domains. The cost of development had shrunken dramatically thanks to both the availability of high-quality open source languages and tools as well as the scale of global collaboration possible on open source projects. The cost of promoting and distributing software had also collapsed thanks to efficiency of both distribution and information diffusion on the Web.

When I heard about R and learned more about it, I become very excited and inspired by what the project had accomplished. A group of extremely talented and dedicated users had created the software they needed for their work and then shared the fruits of that work with everyone. R was a platform that everyone could rally around because it worked so well, was extensible in all the right ways, and most importantly was free (as in speech) so users could depend upon it as a long-term foundation for their work.

So I started RStudio with the aim of making useful contributions to the R community. We started with building an IDE because it seemed like a first-rate development environment for R that was both powerful and easy to use was an unmet need. Being aware that many other companies had built successful businesses around open-source software, we were also convinced that we could make RStudio available under a free and open-source license (the AGPLv3) while still creating a viable business. At this point RStudio is exclusively focused on creating the best IDE for R that we can. As the core product gets where it needs to be over the next couple of years we’ll then also begin to sell other products and services related to R and RStudio.

About-

http://rstudio.org/docs/about

Jjallaire

JJ Allaire

JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion,Windows Live Writer, Lose It!, and RStudio.

From http://en.wikipedia.org/wiki/Joseph_J._Allaire
In 1995 Joseph J. (JJ) Allaire co-founded Allaire Corporation with his brother Jeremy Allaire, creating the web development tool ColdFusion.[1] In March 2001, Allaire was sold to Macromedia where ColdFusion was integrated into the Macromedia MX product line. Macromedia was subsequently acquired by Adobe Systems, which continues to develop and market ColdFusion.
After the sale of his company, Allaire became frustrated at the difficulty of keeping track of research he was doing using Google. To address this problem, he co-founded Onfolio in 2004 with Adam Berrey, former Allaire co-founder and VP of Marketing at Macromedia.
On March 8, 2006, Onfolio was acquired by Microsoft where many of the features of the original product are being incorporated into the Windows Live Toolbar. On August 13, 2006, Microsoft released the public beta of a new desktop blogging client called Windows Live Writer that was created by Allaire’s team at Microsoft.
Starting in 2009, Allaire has been developing a web-based interface to the widely used R technical computing environment. A beta version of RStudio was publicly released on February 28, 2011.
JJ Allaire received his B.A. from Macalester College (St. Paul, MN) in 1991.
RStudio-

RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. Like R, RStudio is available under a free software license. RStudio is designed to be as straightforward and intuitive as possible to provide a friendly environment for new and experienced R users alike. RStudio is also a company, and they plan to sell services (support, training, consulting, hosting) related to the open-source software they distribute.

Analytics Conferences for 2012

NOTE: Early Bird registration for PAW and TAW San Francisco is January 20th – $400 lower than Onsite Price.

CONFERENCE: Predictive Analytics World – San Francisco
March 4-10, 2012 in San Francisco, CA
http://predictiveanalyticsworld.com/sanfrancisco/2012
Discount Code : AJBP12

CONFERENCE: Text Analytics World – San Francisco
March 6-7, 2012 in San Francisco, CA
http://textanalyticsworld.com/sanfrancisco/2012
Discount Code :AJBP12

VARIOUS ANALYTICS WORKSHOPS:
A plethora of 1-day workshops are held alongside PAW and TAW
For details see: http://pawcon.com/sanfrancisco/2012/analytics_workshops.php

SEMINAR: Predictive Analytics for Business, Marketing & Web
March 22-23, 2012 in New York City, NY
July 26-27, 2012 in São Paulo, Brazil
A concentrated training program lead by Eric Siegel.
http://businessprediction.com

CONFERENCE: Predictive Analytics World – Toronto
April 26-27, 2012 in Toronto, Ontario
http://predictiveanalyticsworld.com/toronto/2012
Discount Code :AJBP12

CONFERENCE: Predictive Analytics World – Chicago
June 25-26, 2012 in Chicago, IL
http://www.predictiveanalyticsworld.com/chicago/2012/
Discount Code :AJBP12

MORE ANALYTICS EVENTS:
PAW Düsseldorf: November 6-7, 2012 – http://www.predictiveanalyticsworld.de
PAW London: November 27-28, 2012 – http://www.pawcon.com
PAW Videos: Available on-demand – http://www.pawcon.com/video

Information Ladder for Analytics

One very commonly used diagram in marketing and sales by analytics providers, which is hardly ever credited to its author is the Information Ladder

http://en.wikipedia.org/wiki/Information_ladder

The information ladder is a diagram created by education professor Norman Longworth to describe the stages in human learning. According to the ladder, a learner moves through the following progression to construct “wisdom” at the highest level from “data” at the lowest level:

Data →
   Information →
                Knowledge →
                                    Understanding → 
                                                                  Insight →
                                                                                 Wisdom

Whereas the first two steps can be scientifically exactly defined, the upper parts belong to the domain of psychology and philosophy.

I sometimes think the information ladder and especially the latter two parts are underutilized, under-quantified as metrics and rarely understood completely by the wise men in analytics and information display.

Some visual versions are below

 

Funny enough, it is one of the rare concepts first inspired by poetry-

http://en.wikipedia.org/wiki/DIKW

The earliest formalized distinction between wisdom, knowledge, and information may have been made by poet and playwright T.S. Eliot 

Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?

 

Does the Internet need its own version of credit bureaus

Data Miners love data. The more data they have the better model they can build. Consumers do not love data so much and find sharing data generally a cumbersome task. They need to be incentivize for filling out survey forms , and for signing to loyalty programs. Lawyers, and privacy advocates love to use examples of improper data collection and usage as the harbinger of an ominous scenario. George Orwell’s 1984 never “mentioned” anything about Big Brother trying to sell you one more loan, credit card or product.

Data generated by customers is now growing without their needing to fill out forms and surveys. This data is about their preferences , tastes and choices and is growing in size and depth because it is generated from social media channels on the Internet.It is this data that can be and is captured by social media analytics.

Mobile data is also growing, including usage of location based applications and usage of Internet from the mobile phone is leading to further increases in data about consumers.Increasingly , location based applications help to provide a much more relevant context to the data generated. Just mobile data is expected to grow to 15 exabytes by 2015.

People want to have more and more conversations online publicly , share pictures , activity and interact with a large number of people whom  they have never met. But resent that information being used or abused without their knowledge.

Also the Internet is increasingly being consolidated into a few players like Microsoft, Amazon, Google  and Facebook, who are unable to agree on agreements to share that data between themselves. Interestingly you can use Yahoo as a data middleman between Google and Facebook.

At the same time, more and more purchases are being done online by customers and Internet advertising has grown much above the rate of growth of other mediums of communication.
Internet retail sales have the advantage that better demand predictability can lead to lower inventories as retailers need not stock up displays to look good. An Amazon warehouse need not keep material to simply stock up it shelves like a K-Mart does.

Our Hypothesis – An Analogy with how Financial Data Marketing is managed offline

  1. Financial information regarding spending and saving is much more sensitive yet the presence of credit bureaus alleviates these concerns.
  2. Credit bureaus collect information from all sources, aggregate and anonymize the individual components accordingly.They use SSN as a unique identifier.
  3. The Internet has a unique number too , called the Internet Protocol Address (I.P) 
  4. Should there be a unique identifier like Internet Security Number for the Internet to ensure adequate balance between the need for privacy as well as the need for appropriate targeting? 

After all, no one complains about privacy intrusions if their credit bureau data is aggregated , rolled up, and anonymized and turned into a propensity model for sending them direct mailers.

Advertising using Social Media and Internet

https://www.facebook.com/about/ads/#stories

1. A business creates an ad
Let’s say a gym opens in your neighborhood. The owner creates an ad to get people to come in for a free workout.
2. Facebook gets paid to deliver the ad
The owner sends the ad to Facebook and describes who should see it: people who live nearby and like running.
The right people see the ad
3. Facebook only shows you the ad if you live in town and like to run. That’s how advertisers reach you without knowing who you are.

Adding in credit bureau data and legislative regulation for anonymizing  and handling privacy data can expand the internet selling market, which is much more efficient from a supply chain perspective than the offline display and shop models.

Privacy Regulations on Marketing using Internet data
Should laws on opt out and do not mail, do not call, lists be extended to do not show ads , do not collect information on social media. In the offline world, you can choose to be part of direct marketing or opt out of direct marketing by enrolling yourself in various do not solicit lists. On the internet the only option from advertisements is to use the Adblock plugin if you are Google Chrome or Firefox browser user. Even Facebook gives you many more ads than you need to see.

One reason for so many ads on the Internet is lack of central anonymize data repositories for giving high quality data to these marketing companies.Software that can be used for social media analytics is already available off the shelf.

The growth of the Internet has helped carved out a big industry for Internet web analytics so it is a matter of time before social media analytics becomes a multi billion dollar business as well. What new developments would be unleashed in this brave new world is just a matter of time, and of course of the social media data!