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Posts Tagged ‘Predictive Analytics

Web Data Mining and Orwellian Risks for Abuse at the Private Individual Level

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This posting consists of my comments per a guest post published on by Chris Taylor, a technologist with TIBCO Software, founder of, & fellow member of LinkedIn group ‘Disruptive Technologies’.

The ever burgeoning data explosion and the resulting technologies being developed to interpret meaningful information from it (e.g., Data Mining; interpretive/predictive analytics; etc.) are here to stay.  The competitive advantages which stand to be gained by companies and the military/security sectors of governments that can effectively glean valuable information from the morass of personal data now available on the world-wide web is immense.  Personal data that is gathered and analyzed/stored at a sector group level seems to present less of a threat to each individual’s personal privacy when used in traditional ways (e.g., company marketing studies).  But the “Orwellian” risks for abuse at the private individual level as the current data mining technologies in use become increasingly more sophisticated cannot be ignored.  Furthermore, the judicial systems of developed countries have not been keeping pace with the burgeoning privacy violation ramifications of the information revolution that is currently taking place.  Exacerbating all of this is the fact that for increased revenue purposes, social networking users are being urged by the likes of Facebook’s Mark Zuckerberg to become more transparent by revealing more of their personal information on these sites; a factor which is serving to make personal transparency in public forums a current “popular culture dynamic”.  So the genie is definitely out of the bottle here, which should behoove users of all social networking sites to become more familiar with the “primitive” privacy settings made available by the provider and use them to tailor who gets to see what parts of their personal information that gets generated as time goes by.

Finally, to effectively manage the increasingly sophisticated video parsing technologies currently being utilized by data mining entities, the use of iconic “monikers” in lieu of facial snapshots for one’s social networking sites would be the best option to use in order to remain anonymous per the analysis of video data by companies (and unscrupulous governments, where they may exist).  In addition, video and photo tagging on social networking sites should also be meticulously controlled via one’s privacy settings as well.

Link to Chris Taylor’s article “While You Slept Last Night: Big Data, Privacy, and the Public Square” –

Chris Taylor’s twitter handle is @Successful Work.

Interested LinkedIn readers are invited to join ‘Disruptive Technologies’ group for discussions on this & similar issues!

Does Predictive Analytics (BI) Field Represent a Potentially Major Disruptive Innovation?

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Does the burgeoning field of Predictive Analytics (PA) and the competitive advantage that it can potentially reap represent a potentially major disruptive technology of the near future?  Or will it go by the wayside (i.e., in terms of being disruptive) in the same manner that Artificial Intelligence (AI) computer technology and applications did in the 1980s and 90s?

Several big name companies like IBM and SAIC are making serious runs at developing PA based software applications as their major business models.  IBM is pulling out all the stops here as it recently bought out Cognos and SSPS for their PA assets and high levels of expertise in order to build up its new PA consulting model for its Global Business Services consulting group.  In addition, engineering firm SAIC has recently announced a major PA application system that is now market ready (i.e., SAIC plans to market this new PA application on a worldwide basis alongside its hardware and consulting services).

To elaborate, SAIC’s new “Distribution Monitoring System” is designed to proactively predict the occurrence of failures in distribution and transmission systems in a matter of days, weeks, or even months before they occur.  The utilization of a “complex-event” processing engine evaluates masses of data (i.e., data mining) against rules laying out the relationship between specific, fairly obvious events in the life of a particular device and the likelihood that such a device will fail (and when it will fail).  Millions of records containing event driven data can be run either daily or in real time against these rules of thumb (or indicators) that have been designed to identify potential failure points and the timing of their occurrence.  This process then utilizes a knowledge database that can correlate  faults and failures (i.e., it learns to proactively detect problems that can cause failures).  A neural network is then utilized which can determine whether a failure will occur at some point and can also assess, with a stated probability, when the failure will occur.

In support of the above premise that PA could become a major disruptive technology in the near future, a recent study by McKinsey Consulting Group infers that corporations are going to have to embrace disruptive technologies that will shape the new economic terrain that is evolving out of the latest global economic downturn.  As economies around the world emerge from the recent economic downturn, many companies are starting to grasp that what follows most likely won’t be just another typical turn of the business cycle.  The resulting new economic terrain will undoubtedly be shaped by persistent uncertainty, tighter credit, lower consumer spending, greater consumer saving, and more pronounced government involvement in business (i.e., McKinsey terms this as being the “new normal”).  The use of powerful PA and Business Intelligence (BI) technologies may be the difference maker for companies in terms of removing the persistent uncertainty factor and, as a result, being better able to proactively address potentially serious problems before they become detrimental to the bottom line (i.e., a distinct competitive advantage).  The premise here is that those organizations that don’t invest heavily (or effectively) in PA and BI technologies may be left behind the “eight ball” in the currently evolving new economic order.

So the key question here is, does the burgeoning field of Predictive Analytics and the competitive advantage that it can potentially generate represent a major disruptive technology of the near future (i.e., in terms of dominant business models and profitability)?  Or does it represent just another trendy (and costly) “fad” that will go by the wayside without much impact?  My bet is on the former.

Addendum:  One factor to consider in the disruptive technology genre is the impact of the new technology (or innovations) on the existing business model.  It seems as if many disruptive innovations are really not “disruptive” in terms of the technological challenges presented, but are disruptive from the standpoint of the resulting business model challenges that don’t get managed properly.  Polaroid’s handling of the digital imaging technology when it was new is a real good example of this (see Case below).  It lends creedence to the premise that promising new technologies can end up falling through the cracks due to the failure of their supporting business models (and companies).  As a result, the necessary business model changes also need to be considered and implemented whenever a  new disruptive technology is being implemented in order to be successful.

 Case -> Comparing Polaroid’s Film-Based Business Model with Apple’s iTunes Model –

I).  Being a technology driven company, Polaroid was all about the technological “challenges” presented by the instant photo processing industry at the expense of marketing challenges involved (resulting in Polaroid’s eventual bankruptcy filing).  When digital imaging came on the scene Polaroid was able to deal with it from a technological standpoint, but it could not change its existing film-based business model based on polaroid film sales over to one based on digital imaging/processing  (i.e.,  no film involved).  As a result, the arrival of digital imaging technology served as a very disruptive innovation from a business model standpoint for Polaroid as it went from being very profitable to “collapsing” revenues in a short period of time.  This was evidently due to Polaroid’s propensity to view the new digital imaging technology as a technological challenge only, while ignoring the business model challenges presented by the technological change.  The key point here is that disruptive innovations are not primarily technological challenges, but actually business model challenges instead if not managed competently.  

Footnote:   In Polaroid’s defense, there are numerous hurdles involved in restructuring business models, which include  a). re-educating employees;  b). initial lower profitability;  c). current product cannibalization;  d). increased  management/stakeholder/customer conflicts;  e). complex organizational changes (including culture, etc.);  and f). conflict with traditional (i.e., successful) core competencies. 

 II).  Also being a technology driven company, Apple too is driven by the technological challenges presented by the computer and electronics industries. But unlike Polaroid, Apple is also driven by defining the new business models that need to be adopted in order to help propagate its ”disruptive innovation” types of products.  Apple’s iTune has basically turned the recording (or record) industry “on its head” in that consumers can now purchase and download individual songs at home instead of having to pay for entire albums of songs bundled onto CDs at record stores.  As a result of this “disruptive” business model, record stores are now a thing of the past as iTunes has revolutionized the music industry at the retail level due to its lower costs, increased conveniences,  and more desirable product selection changes.  As a result, some retail record stores have moved over to the movie/DVD side of the industry, but it is just a matter of time before this extended business model meets its demise too due to the arrival of even more disruptive innovations in the movie industry.  These innovations will primarily be based on new delivery technologies (e.g. better internet streaming methods, et al).

NOTE:  Interested LinkedIn members having disruptive technology interests are invited to join LinkedIn Group ”Disruptive Technologies” .

Ten Most Disruptive Technologies of 20th and 21st Centuries!

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My choices for the ten most disruptive technologies of the 20th and 21st centuries per the related Discussion issue in LinkedIn professional group “Disruptive Technologies” are as follows:

20th Century –

01).  Advent of nuclear technology, which drastically changed the balance of power in the world via its weapons applications, and it is still a burgeoning factor as the technology spreads.

02).  Advent of the automobile and airplane, which revolutionized the transportation industry and made traversing the world alot more feasible in terms of time, distance covered, and costs.

03).  Advent of Wernher von Braun’s rocket technology, which revolutionized the communications industry (via satellites), made space travel possible, and drastically changed military strategies and power structures.

04).  Advent of digital data processing at the large mainframe level (starting with Rand’s Eniac).

05).  Advent of the transistor, which replaced vacuum tube technology and allowed for start of integrated circuit technology.

06).  Advent of the integrated circuit technology from transistors, which enabled the commoditization of computer technology via the resulting dissemination of higher speed, lower cost computers (and more portable computers due to the resulting miniaturization).

07).  Advent of the personal computer, which replaced the mainframe as the primary means of computing and has greatly expanded access to computer technology to the masses.

08).  Advent of the world wide web (Internet), and the resulting information and communications revolution that it has invoked.

21st Century (so far) –

09).  Advent (emerging) of nanotechnology and its potentially huge impact on medical technologies and society in the not-too-distant future (e.g., “Singularity” type issues, etc).

10).  Advent (emerging) of teleportation technology at the level of the atom, which is in the beginning stages of greatly increasing the speed and overall power of computer technology (i.e., quantum information processing); its crossover to other applications includes possible revolutionary changes in travel technology at the surreal level by the end of the century.

Addendum1:  Taking item 10’s discussion a step further within the context of disruptive microchip techology, I still like the idea of developing data teleportation technology at the level of the atom, which stands to greatly increase the speed and overall power of computer technology (i.e., quantum information processing). The possible crossovers to other product applications, including surreal, revolutionary changes in travel and shipping technology, is what has really piqued my interest. But in discussing this issue with an executive at one of the major chip firms over the holiday, it was conveyed to me that the atomic teleportation of data is still at least twenty years away in terms of becoming feasible enough to be a disruptive technology per the quantum information processing genre. I would think that the speeding up of the development process for this entity would have to represent a major competive advantage for a developer within the microchip (or academic) industry, especially considering the possibilities represented by revolutionary crossover product development (i.e., major disruptive technologies)!

Addendum2:  the fulfillment of emerging items 09 and 10 is hghly contingent on the continued mitigation of item 01.

Note: From an IT standpoint, I especially like the world-class Gartner Group’s clear, concise definition of what constitutes a disruptive technology from a business systems standpoint: “…[as] one that causes major change in ‘the accepted way of doing things’, including business models, processes, revenue streams, industry dynamics and consumer behaviour”:

Interested LinkedIn members are invited to join the ”Disruptive Technologies” professional group (URL below):

Good Starter Book on Predictive Analytics for Financial Markets

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A good book to read for those who are just getting started with Predictive Analytics per financial markets is “Smart Momentum” by Hugh Clark. This original BI/PA book (2001, John Wiley & Sons Ltd.) postulates on the future (or now current state) of Predictive Analytics within the realm of financial markets. It provides both a good introduction to the subject matter and a working Excel example, which can be used by the reader to create a simple trading model that can be built upon using various indicator variables. Hugh Clark’s “Smart Momentum” model can be applied to existing techniques in both technical and fundamental analysis.

Written by Larry Fry, CCP, MBA

June 10, 2009 at 6:15 am