Friday, December 19, 2014

Kobielus' Quick-Hits in 2014

January 17:  Big Science? Overreliance on big data can stunt development of scientific rigor: 
January 21:  Security of big data? Shoddy lifecycle management is ironic data security:  http:  //
January 22:  NoSQL? The architecture that's still curiously absent:  http:  //
January 23:  Context accumulation? Narratives drive home relevance of statistical models :  http:  //
January 24:  Meaty metadata? Data variety leads to metadata viscosity:  http:  //
January 27:  Smarter planet? Intelligence for a self-healing landscape:  http:  //
January 28:  Data monetization? Pay the persons for their personal data:  http:  //
January 29:  Internet of Things? Instrument the birdies, bees, and other beasties:  http:  //
January 30:  Recommendation engines? The untapped potential of video, image, and gesture analytics in retail showrooms:  http:  //
January 31:  Sexy statistics? The vintage kick of old data poured into fresh analytic bottles:  http:  //
February 3:  Peta-governance? Bottom-line ROI from boosting the quality of experience data:  http:  //
February 4:  Smarter planet? Continuous crowdsourcing of quality-of-life data will power livable urban existence:  http:  //
February 6:  Hadoop uber-alles? Climbing the slope of enlightenment, arriving at plateau of productivity:  http:  //
February 7:  Machine learning? Maximum impact from bigger data & deeper learning:  http:  //
February 10:  Ambient analytics? Mobile data traces the contours of urban experience:  http:  //
February 11Data-scientist skillsets? Psychological insights key to modeling customer causationhttp:  //
February 12:  Meaty metadata? Extracting corpus omniscience from big data:  http:  //
February 13:  Complex event processing? Plucking event graphs from the deep, dark, dynamic Web:  http:  //
February 14:  Big data vision? Modern economy mills new value from its own digital exhaust :  http:  //
February 17:  Real-world experiments? Disrupting your enterprise while mitigating the risks of doing so:  http:  //
February 18:  Engaging customer as individual? Abandonment metrics as warnings and/or opportunities:  http:  //
February 19:  Graph analysis? Identifying the happy medium between the under- and overconnected influencers: 
February 20:  Cognitive computing? When biases cloud automated cognition:  http:  //
February 24:  Geospatial analytics? Analytic surveillance in the cause of resource stewardship:  http:  //
February 25:  Healthcare analytics? Tuning the fusion of human physiology & machine physics:  http:  //
February 26:  Gamified analytics? Brownie points for consumers who share their brand love:  http:  //
February 27:  Sexy statistics? Distinguishing hidden (but real) patterns from those that are real-seeming (but bogus):  http:  //
February 28:  Workload-optimized systems? HPC now mostly big data analytics with growing emphasis on small lots & asynchronous processing:  http:  //
March 4:  Storage optimization? Compress what you can, extract insights prior to purging the rest:  http:  //
March 5:  Data-scientist skillsets? Juggling visualizations, algorithms, and narratives:  http:  //
March 6:  Internet of Things? New measurement tools for candid ethnography:  http:  //

March 7:  Advanced visualization? Eyeballing the dark dimensions :  http:  //
March 10:  Healthcare analytics? Sensing, mapping, and mining the mystery of the brain:  http:  //
March 12:  Experience optimization? Big data framing the engagement with art and culture:  http:  //
March 13:  Business process optimization? Plugging a lean, mean analyzing machine into your manufacturing operations:  http:  //
March 17:  Recommendation engines? Analytics grooving with whatever grooves groove you:  http:  //
March 19:  Privacy and big data? Addressing the tricky contours of in-store privacy:  http:  //
March 25:  Privacy and big data? The dangers of misplaced faith in tactical and technological quick-fixes:  http:  //
March 26:  Graph analysis? Apache Spark begins to spark convergence of Hadoop, streaming, in-memory, & graph analysis: 
March 31:  Healthcare analytics? Using advanced image analytics to spot hidden cancer patterns:  https:  //
April 1:  Data journalism?:  http:  //
April 2:  Peta-governance? Where trustworthiness is concerned, the proof is in the data-governance process:  http:  //
April 3:  Prediction markets? Fostering open marketplaces for models and modelers:  http:  //
April 4:  Moneyball?:  http:  //
April 7:  Hadoop uber-alles? Hadoop beginning to stare newer big-data approaches in the face:  http:  //
April 8:  Open data? Climate data should move as freely as the atmosphere that cloaks our warming planet:  http:  //
April 9:  Machine learning? When data scientists struggle to keep their foothold in ground truth :  http:  //
April 10:  Big Science? The rigid regimen of reproducible computational findings:  http:  //
April 11:  Big identity? The big data challenges of identity management in the Internet of Things :  http:  //
April 14:  Machine learning? Automating log-data analysis through unsupervised and reinforcement learning algorithms:  http:  //
April 15:  Internet of Things? The binocular vision and opposable thumb of cognitive computing :  http:  //
April 16:  Context accumulation? Grounding cognitive confidence in the probabilistic fabric of the real world :  http:  //
May 19:  Real-world experiments? The tricky business of A/B testing:  http:  //
May 20:  Storage optimization? Software-defined storage driving the demise of rip-and-replace:  http:  //
May 21:  Experience optimization? Drilling into the messy gusher of web analytics data:  http:  //
May 22:  Machine learning? A melting pot for today's leading-edge advanced analytics:  http:  //
May 23:  Sexy statistics? Big-data's correlations and cautionary tales:  http:  //
May 27:  Big-data discovery? The power of Bayesian search:  http:  //
May 28:  Open data? The democratization of standardized data in civic governance:  http:  //
May 29:  Big Data's optimal deployment model? Deeply embedded in the cloud:  http:  //
May 30:  Machine learning? Deep learning to filter text for the known, unknown, and unknowable unknowns:  http:  //
June 2:  Engaging customer as individual? Cognitive computing, conversational engagement, & customer confidence:  http:  //
June 3:  Internet of Things? Digitally fingerprinting the trusted endpoint:  http:  //
June 4:  Big identity? Using big data analytics to identify & shut down slippery cyberscammers:  http:  //
June 5:  Experience optimization? Internet of Things, next best actions, & the downside of the technological cocoon:  http:  //
June 6:  Healthcare analytics? Remaining skeptical about the data science behind dietary research:  http:  //
June 9:  Open data? The promise and privacy implications of open access to energy data:  http:  //
June 10:  Big data's optimal deployment model? The niche role for graph databases in hybrid architectures:  http:  //
June 11:  Quantified self?:  http:  //
June 12:  Information economics? The shifting economic role of official government statistics in the era of social listening:  http:  //
June 13:  Hadoop uber-alles? Implementing an extensible library of statistical algorithms & models to serve big-data developers:  http:  //
June 16:  Storage optimization? Data deduplication improves cuts IT costs, boosts data scientist productivity, & bolsters data quality:  http:  //
June 17:  Engaging customer as individual? Mapping the customer journey through the seemingly irrational :  http:  //
June 18:  Data-scientist skillsets? The delicate art of project prioritization and triage:  http:  //
June 19:  Healthcare analytics? Health data brokers and the arms race in intrusive target marketing:  http:  //
June 20:  Big identity? Nonintrusive strong authentication through never-ending behavioral fingerprinting:  http:  //
June 23:  Big Media? The narrative power of video content analytics:  http:  //
June 24:  Quantified self? The social physics of a quantified society:  http:  //
June 25:  Open data? The front line of grass-roots consumer protection:  http:  //
June 26:  Recommendation engines? Fancy math to illuminate stabs in the dark:  http:  //
June 27:  Data journalism? Using real-time analytics to identify who scooped whom online:  http:  //
June 30:  Internet of things? The potential for sensor-driven hyperlocalized weather forecasting:  http:  //
July 1:  Healthcare analytics? Big data as a factor in life-or-death decisions:  http:  //

July 2:  Ambient analytics? The advent of big-data wearables and "unaware-ables":  http:  //
July 3:  Engaging customer as individual? Parrying the double-edge of customer sarcasm:  http:  //
July 7:  Moneyball? Pitcrew analytics and within-race data-driven decision support:  http:  //
July 8:  Big Media? The shifting art of audience measurement in the era of all-online media:  http:  //
July 9:  Big-data development? The agile imperative and the risk of data scientists "boiling the lake":  http:  //
July 10:  All in memory? Scaling in-memory infrastructures up and out :  http:  //
July 11:  Healthcare analytics? Keeping patients from straying off the path to recovery:  http:  //
July 14:  Real-world experiments? Dissecting the Facebook controversy over mood manipulation:  http:  //
July 15:  Security of big data? The imperative and issues surrounding whole-population security analytics:  http:  //
July 16:  Data-scientist skillsets? Data science in the new product development repertoire:  http:  //
July 17:  Open data? Open correlations in the common cause:  http:  //
July 18:  Analytic acceleration in the cloud? The new era of big data as a service:  http:  //
July 21:  Recommendation engines? Predicting the exquisitely nonlinear shifts of customer taste:  http:  //
July 22:  Big-data ethics?:  http:  //
July 23:  All in memory? Transitional patterns on the road to the "all-and-only-in-memory cloud":  http:  //
July 24:  Security of big data? The self-protecting big-data honeypot:  http:  //
July 25:  Advanced analytics?:  http:  //
July 28:  Internet of Things? The walls have ears, eyes, noses, and every other sense organ:  http:  //
July 29:  Data-scientist skillsets? A polymathic grasp of myriad disciplines and applications:  http:  //
July 30:  Big data's optimal deployment model? The core principles of scalability:  http:  //
July 31:  Peta-governance? The challenges of probabilistic data-matching in the Internet of Things:  http:  //
August 1:  Open data? Monetizing your existence as a crowdsourced data scientist:  http:  //
August 4:  Big data on the move? The emergence of the mobile back-end as a service:  http:  //
August 5:  Geospatial analytics? Ammunition against pestilence:  http:  //
August 6:  Decision automation? Retraining and restraining the long-data arm of the law:  http:  //
August 7:  Advanced analytics? Pick an algorithm, any algorithm:  http:  //
August 8:  Conversation optimization? The delicate dance of accessorizing your lifestyle online:  http:  //
August 11:  Cognitive computing? Wrestling the myriad definitions down to manageable size:  http:  //
August 12:  Big Science? The open-sourcing of scientific inquiry throughout the world:  http:  //
August 13:  Data-scientist skillsets? Articulating the advantages of analytics over intuition:  http:  //
August 14:  Healthcare analytics? Wearable cognition-assist analytics as the new prosthetics:  http:  //
August 15:  Big Science? The staggering resource requirements of computational megascience:  http:  //
August 18:  Service-oriented analytics? Big-data analytics consulting as a service:  http:  //
August 19:  Big-data hardcore use cases? Assessing when bigger data truly is better:  http:  //
August 20:  Data-scientist skillsets? Girding yourself for the commoditization of your profession:  http:  //
August 21:  Advanced analytics? The converged and accelerated machine learning of ensemble methods:  http:  //
August 22Data-scientist skillsets? Teaming within the open expertise communitieshttp:  //
August 25:  Data-scientist skillsets? Introducing evidence-driven computational approaches into the public-policy arena:  http:  //
August 26:  Meaty metadata? The analytic potency of the ontology:  http:  //
August 27:  Hadoop uber-alles? Dredging the "data lake" metaphor down to its muddy bottom:  http:  //
August 28:  Machine learning? Distinguishing deep learning from its opposite:  http:  //
August 29:  Big-data-driven TV experience? Serving both active and passive audiences equally:  http:  //
September 2:  Machine learning? Delving into the depths of deep learning:  http:  //
September 3:  Advanced visualization? The analytical value of data-provenance tracking within big-data visualizations:  http:  //
September 4:  Healthcare analytics? Unstructured analytics powering pandemic early-warning systems:  http:  //
September 5:  Data journalism? Deep learning threatens to deep-six journalism's faith in the factuality of the photograph:  http:  //
September 8:  Decision scientists? Data scientists challenged to sway the hearts and minds of public policymakers:  http:  //
September 9:  Big Science? The insight-acceleration potential of elastic storage clouds:  http:  //
September 10:  Sexy statistics? The tricky serendipity of data-lake fishing expeditions:  http:  //
September 11:  Big data's optimal deployment model? "Fog" clouds optimized for Internet of Things analytics:  http:  //
September 12:  Big-data single version of the truth? The practical limits of clue-googling:  http:  //
September 15:  Big Science? The analytical challenges that frustrate use of data science in global studies:  http:  //
September 16:  Internet of Things? Revisiting Metcalfe's Law in the era of everything networking:  http:  //
September 17:  Recommendation engines? Black art of benchmarking against the past and pending:  http:  //
September 18:  Advanced analytics? Please avoid interpreting "advanced" as "hipper than thou":  http:  //

September 19:  Healthcare analytics? Mining hospital data for nonobvious infection and contagion patterns within their facilities:  http:  //
September 22:  Peta-governance? Timing means everything for establishing accountability:  http:  //
September 23:  Advanced analytics? Monte Carlo simulation when the past is an uncertain prologue to prediction:  http:  //
September 24:  Cognitive computing? Acing the Turing test is the least of it :  http:  //
September 25:  Data-scientist skillsets? Immersion in probabilistic programming languages:  http:  //
September 26:  Open data? Equitably distributing data-science brainpower among the haves and have-nots:  http:  //
September 29:  Stream computing? Converging in-motion, in-memory, and in-process analytics:  http:  //
September 30:  Engaging customer as individual? The blurry boundary between engagement, influence, and manipulation:  http:  //
October 1:  Healthcare analytics? Big data's "4 Vs" drive advances in computational bioinformatics:  http:  //
October 2:  Open data? The emergence of the urban data scientist:  http:  //
October 3:  Big Media? Video and image analytics for extracting real-time actionable insights:  http:  //
October 6:  Quantified self? Healthy self-monitoring vs. narcissistic self-obsession:  http:  //
October 7:  Machine data analytics? Man & machine data becoming indistinguishable:  http:  //
October 8:  Hadoop uber-alles? The challenge of staying current on an ever-shifting technology landscape:  http:  //
October 9:  Talent analytics?:  http:  //
October 10:  Big-data discovery? Shining analytical light deeply into dark data:  http:  //
October 13:  Smarter cities?:  http:  //
October 14:  Hadoop uber-alles? Data modeling will endure and you'll still need to pay the ETL piper somewhere sometime:  http:  //
October 15:  Big identity? Facial recognition, deep learning, and the end of anonymity in public spaces:  http:  //
October 16:  Big-data ethics? The difference between targeted segmentation and discriminatory profiling:  http:  //
October 17:  Engaging customer as individual? Quantification of student performance in the new education industry order:  http:  //
October 20:  Workload-optimized systems? Pushing MapReduce's efficiency envelope:  http:  //
October 21:  Machine learning? Need a decision tree for data scientists to choose among machine-learning statistical frameworks:  http:  //
October 22:  Business process optimization? The limits of disintermediation in the cognitive era:  http:  //
October 23:  Talent analytics? Non-obvious patterns of who knows what, does what, and gets what done:  http:  //
October 24:  Machine learning? An evolving grab-bag of magic tricks that still lacks a unifying framework:  http:  //
October 27:  Big data on the move? The evolving data fabric of the travel experience:  http:  //
October 28:  Marketing campaign optimization? Continuous campaigning for mass-market blockbusters:  http:  //
October 29:  Data-scientist skillsets? Getting up to speed on machine learning:  http:  //
October 30:  Quantified self? The delicious demon of self-awareness:  http:  //
October 31:  Chief Data Officer?:  http:  //
November 3:  Data monetization? Nabbing the counterfeiters behind fake online reviews:  http:  //
November 4:  Geospatial analytics? Predictive risk mitigation and retrofitting for disaster preparedness:  http:  //

November 5:  Cognitive computing? Programming the artificial mind:  http:  //
November 6:  Internet of Things? Behavioral analytics in the era of wearables:  http:  //
November 7:  Peta-governance? The potential of graph analytics in master data management:  http:  //
November 10:  Big-data single version of the truth? Curation vs. stewardship in the era of multistructured data:  http:  //
November 11:  Prescriptive analytics? Massive-scale prediction and real-time interdiction in the fight against cybercrime:  http:  //
November 12:  Big Media? Standards-based object-storage platforms are key to streaming media:  http:  //
November 13:  Transactional analytics? Channels cull continuous customer expertise from cognitive cloud:  http:  //
November 14:  Analytic acceleration in the cloud? The next evolution in self-service business analytics:  http:  //
November 17:  Modeling automation? Machine learning shapes the material world:  http:  //
November 18:  Workload-optimized systems? The challenges of scaling to Facebookian proportions:  http:  //
November 19:  Social sentiment as valuable market intelligence? The utility or futility of weeding out bogus online reviews:  http:  //
November 20:  Influence analytics?:  http:  //
November 21:  Big Media? Sentiment data may suffer as social networks evolve into broadcasting media platforms:  http:  //
December 1:  Cognitive computing? Fathoming photos at algorithmic speed:  http:  //
December 2:  Healthcare analytics? The possibility of appliance-enabled whole-body self-diagnosis:  http:  //
December 3:  Healthcare analytics? The electrified “third rail” of deep psychographic customer engagement:  http:  //

December 4:  Experience optimization? Assessing big data’s role in the grand scheme of human happiness:  http:  //

December 5:  Internet of Things? IoT insights that can best be revealed through graph analysis:  http:  //

December 8:  Crowdsourcing Big Data creativity? Intersection of interest graphs with the Internet of Things:  http:  //
December 9:  Geospatial analytics? Managing the land more effectively to protect the rainforest:  http:  //
December 10Smarter planet? Remote sensing the globe from every possible viewpointhttp:  //
December 11:  Marketing campaign optimization? Using pervasive analytics to drive a sustainable food chain:  http:  //
December 12:  Smarter cities? The infrastructure silo-busting imperative:  http:  //
December 15:  Advanced visualization? Seeing the spaces where numbers and words seamlessly join:  http:  //
December 16:  Business process optimization? The statistics that shape manufacturing:  http:  //
December 17:  Cognitive computing? Undecidable problems and the limits of algorithmic cognition:  http:  //
December 18:  Cognitive computing? Learning through associational population of sparse experiential matrices with fresh clues:  http:  //

December 19:  Social sentiment as valuable market intelligence? Black swans and the predictive challenge surrounding concocted controversy:  http:  //