How Much Big Data Do You Actually Need?
by James Kobielus The point of big data is to extract deeper intelligence from data while eliminating the scale constraints that have frustrated traditional business analytics initiatives....
View ArticleIs Privacy Possible in the Era of Big Data?
by James Kobielus Can you have big data with airtight personal data privacy? In the age of ubiquitous over sharing and zealous big-data-driven hyper-analysis, digital privacy has become elusive and...
View ArticleBig Data Supports Development of a Visual Business Case
by James Kobielus Big data with zero relevance to your business needs is essentially the same as no data at all. Recently, I came across an interesting article that introduced the word “evidence” into...
View ArticleInternet of Things May Disrupt Predictive Analytics in Big Data Clouds
by James Kobielus Many operational big data applications have predictive analytics at their core. So, given how much of the business may be riding on a predictive analytics infrastructure, business...
View ArticleSocial Data Quality Will Take Back Seat to Data Relevance
by James Kobielus People prevaricate, mislead, and exaggerate in every possible social context. It’s no surprise that their Tweets and other social media remarks are full of the same. If you imagine...
View ArticleBig Data’s Coming End-State Architecture
by James Kobielus Maturity is a complex concept where big data is concerned. I see it mentioned in many industry contexts, but I rarely see anybody put together a clear picture of where it’s all...
View ArticleData Scientists Need a Professional Code of Conduct
by James Kobielus Some professions are in positions of public trust, even though many of their practitioners are in the private sector and may have no compunction about amassing vast wealth from their...
View ArticleMachine Learning Boosts Data-Scientist Learning, and Vice Versa
by James Kobielus Data scientists are human, from what I’ve seen. The same can be said about the subject-matter domain specialists who work hand-in-hand with data scientists. Sometimes one and the same...
View ArticleBig Data and the Power of Mathematical Breakthroughs
by James Kobielus Advanced mathematics can feel extremely detached from earthly reality until you realize how incredibly practical it is. The models and algorithms that power big data applications are...
View ArticleOpen Data Graphs Can Boost the Global Economy
by James Kobielus Openness, transparency, and agility are where the world is headed. However, these trends are problematic for those of us who have intellectual property – including software, data, and...
View ArticleThe Beauty Metric: Choosing the Best-Fit Advanced Analytic Algorithms
by James Kobielus Advanced algorithms power the engines of statistical analysis, but they’re not invisible ghosts in the machine. Instead, data scientists compose and tweak algorithms visually, writing...
View ArticleMitigating the Disruption of Real-World Experiments
by James Kobielus You can’t shut down a community and expect it to spring back to life at a later date, picking up exactly where it left off. Living communities cannot be reinvented from scratch. They...
View ArticleHadoop is Beginning to Stare Newer Big Data Approaches in the Face
by James Kobielus Waves pass, though the soft shorelines they sculpt might endure longer than you’d expect. Every new technology platform enters the world as a wave that builds and builds until it’s...
View ArticleDon’t Understaff and Overstretch Your Analytics Development Team
by James Kobielus Enterprises increasingly worry about whether they can fulfill all their new data science requirements with qualified candidates. But an even larger issue has been simmering for years...
View ArticlePractical Data Science and the Tricky Business of A/B Testing
by James Kobielus Increasingly, the best websites aren’t so much designed as calculated. And that calculation, more often than not, rides on a never-ending campaign of A/B testing. And the A/B testing,...
View ArticleData De-duplication Should Be the Heart of all Big Data Strategies
by James Kobielus Administrators of monolithic data architectures in the olden days had it easy. They didn’t have to worry about keeping tabs on where their data was being stored, since it was all in...
View ArticleIs Privacy Possible in the Era of Big Data?
by James Kobielus Can you have big data with airtight personal data privacy? In the age of ubiquitous over sharing and zealous big-data-driven hyper-analysis, digital privacy has become elusive and...
View ArticleBig Data Supports Development of a Visual Business Case
by James Kobielus Big data with zero relevance to your business needs is essentially the same as no data at all. Recently, I came across an interesting article that introduced the word “evidence” into...
View ArticleInternet of Things May Disrupt Predictive Analytics in Big Data Clouds
by James Kobielus Many operational big data applications have predictive analytics at their core. So, given how much of the business may be riding on a predictive analytics infrastructure, business...
View ArticleSocial Data Quality Will Take Back Seat to Data Relevance
by James Kobielus People prevaricate, mislead, and exaggerate in every possible social context. It’s no surprise that their Tweets and other social media remarks are full of the same. If you imagine...
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