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:  //

Thursday, December 18, 2014

Kobielus big-data evangelization blogging output in 2014 (not including LinkedIn quick-hits, presentations, & tweets)

December 18, 2014

December 11, 2014

Peeling back the layers of the smarter city

The Challenges of Master Data Management in the Internet of Things

Optimized object storage is key to streaming media

Big Data Ethics for Targeted Segmentation

Cognitive computing: Programming the artificial mind

November 20, 2014

Chief data officer: My mixed and nuanced musings on the need for one

November 13, 2014

Behavorial analytics in the era of wearables

Cognitive computing as a wearable prosthetic

Five Ways To Move Your Big Data into the Cloud

Bringing cloud-based modernization to data warehousing and analytics

October 28, 2014

Big Data Is Not the Death Knell of ETL

What's keeping data science from playing a more central role in public policy?

October 23, 2014

Self-Service Analytics, Data Warehousing, and Information Management in the Cloud

Collaborations and correlations in the common cause

Distributing data science brainpower more equitably among the haves and have-nots

Governance, Stewardship, and Quality of Temporal Data in a Data Warehousing Context

Converging in-motion and in-memory analytics

Using analytics to help hospitals avoid inadvertently sickening patients and their caregivers

October 2, 2014

The Challenges of Deploying Big Data Analytics to the Cloud

Data science's limitations in addressing global warming

September 25, 2014

September 19, 2014

IBM Watson: Core of the cognitive revolution

September 18, 2014

Immunizing your business against toxic customer relationships

September 11, 2014
Who's afraid of the big (data) bad wolf?

When big data is truly better

The analytic potency of the ontology

Raising real-time transaction and analytic processing to the next power

September 2, 2014

Consolidating and migrating to an in-memory analytics cloud

August 27, 2014

Doing something about the weather

August 21, 2014

The Larger Stakes Behind Big Data Ethics

Explore New Frontiers in Business Analytics

Using real-time analytics to identify who's scooping whom in online journalism

August 14, 2014

August 7, 2014

Algorithms are not magic

Empowering athletes with real-time, data-driven decision support

July 31, 2014

The Power of Behavioral Fingerprinting

Can a machine detect sarcasm? Yeah, right

Real-time healthcare compliance analytics can keep patients alive and well

July 24, 2014

Big algorithm libraries breathe life into big data

Real-world experiments and the Facebook controversy over mood manipulation

July 17, 2014

Big data as a factor in life-or-death decisions

July 10, 2014

Transforming the agile data warehouse in the age of the in-memory cloud

July 9, 2014
James Kobielus: Cualquiera se beneficia del big data

Streaming media and narrative power of video content analytics

IBM Watson and the power of conversation in the cognitive fabric

June 26, 2014
Data De-Duplication Should Be the Heart of all Big Data Strategies

What's machine learning? It depends on who you ask

The delicate art of data science project prioritization and triage

June 19, 2014

Big data log analysis thrives on machine learning

The Ground Truth in Agile Machine Learning

The democracy of open data

Scientists beginning to tap the research potential of the quantified self

June 12, 2014

Hadoop, whither goest thou?

Too big, too small, or just right? Balancing your social connections

Grounding cognitive computing in probabilistic data analytics

Practical Data Science and the Tricky Business of A/B Testing

Evolving the binocular vision and opposable thumbs of cognitive computing

May 29, 2014

Whipping Web Analytics into Shape to Glimpse True Customer Experience

In Data Science, Take Nothing on Faith

Big data's bogus correlations

May 22, 2014

IBM Data Magazine: Its Value and Its Vision

Don’t Understaff and Overstretch Your Analytics Development Team

Never put everything in one database basket, even if it’s Hadoop

Open climate data will focus humanity on solutions to global warming

May 15, 2014

Sports Teams: Smack That Ol’ Moneyball Right Out of the Park

Hidden Biases That May Cloud Cognitive Computing

Using advanced image analytics to spot hidden cancer patterns

May 9, 2014

Data confidence: The proof's in the process

May 1, 2014

Hadoop is Beginning to Stare Newer Big Data Approaches in the Face

April 28, 2014

Nurturing open marketplaces for predictive models and modeling expertise

April 24, 2014

Moneyball is the true game-changing application of data analytics

April 17, 2014

The embryonic days of the data journalism industry are upon us

April 10, 2014

IBM big data roundtable: The transformative power of big data

April 4, 2014

Big data powers the practical know-it-all in us all

April 3, 2014
Extract maximum insights before you purge your data

Rules of thumb for identifying and prioritizing big data applications

The Irreducibly Human Center of Streaming Music Algorithmics

Saving the Planet

Plugging a lean, mean big-data-analyzing machine into manufacturing

March 21, 2014

Using big data to tune the fusion of human physiology and machine physics

March 13, 2014
Detecting the Signs of Drama and Drift in Customer Loyalty

Caveat on use of the Internet of Things in behavioral analytics

March 6, 2014

The exabyte era is rapidly streaming our way

The cloud now has a BLU lining and BLU has a cool hub

February 28, 2014

Machine learning floats all boats on big data's ocean

Mobile data traces the contours of urban experience

Mitigating the Disruption of Real-World Experiments

Data scientists need psychological insights to tune customer analytics

February 20, 2014

Big data drives the daisy chain of value in today's economy

February 13, 2014

Pay Your Customers What They’re Worth to You

Customer experience data is too important to foul with shoddy governance

February 6, 2014

IBM Patterns and Platforms for Business Intelligence (BI): Choosing What’s Best for You

When and When Not to Have Faith in Statistical Models – Part 2

Cognitive computing can take the semantic Web to the next level

Autonomic planet: Distributed intelligence for a self-healing ecosystem

When and When Not to Have Faith in Statistical Models – Part 1

Big data overkill can stunt scientific rigor

January 23, 2014

The Beauty Metric: Choosing the Best-Fit Advanced Analytic Algorithms

Next best expert: Collaboration of people and machines on big data and analytics

January 16, 2014

Log data is pivotal to analytics on Internet of Things

Koby's predictions for the Internet of Things in 2014 and beyond

January 10, 2014

Foretelling 2014 Trends In Big Data, Hadoop, Data Science & More

What Koby's tea leaves foretell for big data in 2014

January 2, 2014