Category: "Big Data"

BayAreaUseR October Special Event

Zhou Yu organized a great special event for the San Francisco Bay Area Use R group, and has asked me to post the slide decks for download. Here they are:

No longer missing is the very interesting presentation by Yasemin Atalay showing the difference in plotting analysis using the Windermere Humic Aqueous Model for river water environmental factors, without using R and then the increased in variety and accuracy of analysis and plotting gained by using R.

Search Terms for Data Management & Analytics

Recently, for a prospective customer, I created a list of some search terms to provide them with some "late night" reading on data management & analytics. I've tried these terms out on Google, and as suspected, for most, the first hit is for Wikipedia. While most articles in Wikipedia need to be taken with a grain of salt, they will give you a good overview. [By the way, I use the "Talk" page on the articles to see the discussion and arguments about the article's content as an indicator of how big a grain of salt is needed for that article] &#59;) So plug these into your favorite search engine, and happy reading.

  • Reporting - top two hits on Google are Wikipedia, and, interestingly, Pentaho
  • Ad-hoc reporting
  • OLAP - one of the first page hits is for Julian Hyde's blog, creator of the open source tool for OLAP, Mondrian, as well as real-time analytics engine, SQLstream
  • Enterprise dashboard - interestingly, Wikipedia doesn't come up in the top hits for this term on Google, so here's a link for Wikipedia:
  • Analytics - isn't very useful as a search term, but the product page from SAS gives a nice overview
  • Advanced Analytics - is mostly marketing buzz, so be wary of anything that you find using this as search term

Often, Data Mining, Machine Learning and Predictives are used interchangeably. This isn't really correct, as you can see from the following five search terms…

  • Data Mining
  • Machine Learning
  • Predictive Analytics
  • Predictive Intelligence - is an earlier term for Predictives that has mostly been supplanted by Predictive Analytics. I actually prefer just "Predictives".
  • PMML - Predictive Modeling Markup Language - is a way of transporting predictive models from one software package to another. Few packages will both export and import PMML. The lack of that capability can lock you into a solution, making it expensive to change vendors. The first hit for PMML on Google today is the Data Mining Group, which is a great resource. One company listed, Zementis, is a start-up that is becoming a leader in running data mining and predictive models that have been created anywhere
  • R - the R statistical language, is difficult to search on Google. Go to and … instead. R is useful for writing applications for any type of statistical analysis, and is invaluable for creating new algorithms and predictive models
  • ETL - Extract, Transform & Load, is the most common way of getting information from source systems to analytic systems
  • ReSTful Web Services - Representational State Transfer - can expose data as a web service using the four verbs of the web
  • SOA
  • ADBMS - Analytic Database Management Systems doesn't work well as a search term. Start with the site and follow the links from the Eigenbase subproject, LucidDB. Also, check out AsterData
  • Bayes - The Reverend Thomas Bayes came up with this interesting approach to statistical analysis in the 1700s. I first started creating Bayesian statistical methods and algorithms for predicting reliability and risk associated with solid propellant rockets. You'll find good articles using Bayes as a search term in Google. A bit denser article can be found at And some interesting research using Bayes can be found at: Andrew Gelman's Blog. You're likely familiar with one common Bayesian algorithm, naïve Bayes, which is used by most anti-spam email programs. Other forms are objective Bayes with non-informative priors and the original Subjective Bayes. I have an old aerospace joke about the Rand Corporation's Delphi method, based on subjective Bayes :-) I created my own methodology, and don't really care for naïve Bayes nor non-informative priors.
  • Sentiment Analysis - which is one of Seth Grimes' current areas of research
  • Decision Support Systems - in addition to searching on Google, you might find my recent OSS DSS Study Guide of interest

Let me know if I missed your favorite search term for data management & analytics.

Data Artisan Smith or Scientist

Over the past few months, a debate has been proceeding on whether or not a new discipline, a new career path, is emerging from the tsunami of data bearing down on us. The need for a new type of Renaissance [Wo]Man to deal with the Big Data onslaught. To whit, Data Science.

I'm writing about this now, because last night, at an every-three-week get together devoted to cask beer and data analysis, the topic came up. [Yes, every-THREE-weeks - a month is too long to go without cask beer fueled discussions of Rstats, BigData, Streaming SQL, BI and more.] The statisticians in the group, including myself, strongly disagreed with the way the term is being used; the software/database types were either in favor or ambivalent. We all agreed that a new, interdisciplinary approach to Big Data is needed. Oh, and I'll stay on topic here, and not get into another debate as to the definition of "Big Data". &#59;)

This lively conversation reinforced my desire to write about Data Science that swelled up in me after reading "What is Data Science?" by Mike Loukides published on O'Reilly Radar, and a subsequent discussion on Twitter held the following weekend, concerning data analytics.

The term "Data Science" isn't new, but it is taking on new meanings. The Journal of Data Science published JDS volume 1, issue 1 in January of 2003. The Scope of the JDS is very clearly related to applied statistics

By "Data Science", we mean almost everything that has something to do with data: Collecting, analyzing, modeling...... yet the most important part is its applications --- all sorts of applications. This journal is devoted to applications of statistical methods at large.
-- About JDS, Scope, First Paragraph

There is also the CODATA Data Science Journal, which appears to have last been updated in August of 2007, and currently has no content, other than its self-description as

The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology.

I think that two definitions can be derived from these two journals.

  1. Data Science is systematic study, through observation and experiment, of the collection, modeling, analysis, visualization, dissemination, and application of data.
  2. Data Science is the use of data and database technology within physical and natural sciences and engineering.

I can agree with the first, especially with the JDS Scope clearly stating that Data Science is applied statistics.

The New Oxford American Dictionary, on which the Apple Dictionary program is based, defines science as a noun

the intellectual and practical activity encompassing the systematic study of the structure and behaviour of the physical and natural world through observations and experiments.

And a similar definition of science can be found on

In many ways, I like Mike Loukides' article "What is Data Science?" in how it highlights the need for this new discipline. I just don't like what he describes to be the new definition of "data science". Indeed, I very much disagree with this statement from the article.

Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis. What differentiates data science from statistics is that data science is a holistic approach. We're increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.

A statistician is not an actuary. They're very different roles. I know this because I worked for over a decade applying statistics to determining the reliability and risk associated with very large, complex systems such as rockets and space-borne astrophysics observatories. I once hired a Cal student as an intern because she feared that the only career open to her as a math major, was to be an actuary. I showed her a different path. So, yes, I know, from experience, that a statistician is not an actuary. Actually, the definition of a data scientist given, that is "gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others" is exactly what a statistician does.

I do however see the need for a new discipline, separate from applied statistics, or data science. The massive amount of data to come from an instrumented world with strongly interconnected people and machines, and real-time analysis, inference and prediction from those data, will require inter-disciplinary skills. But I see those skills coming together in a person who is more of a smith, or, as Julian Hyde put it last night, an artisan. Falling back on the old dictionary again, a smith is someone who is skilled in creating something with a specific material; an artisan is someone who is skilled in a craft, making things by hand.

Another reason that I don't like the term "data science" for this interdisciplinary role, stems from what Mike Loukides describes in his article "What is Data Science?" as the definition for this new discipline "Data science requires skills ranging from traditional computer science to mathematics to art". I agree that the new discipline requires these three things, and more, even softer skills. I disagree that these add up to data science.

I even prefer "data geek", as defined by Michael E. Driscoll in "The Three Sexy Skills of Data Geeks". Michael Driscoll's post of 2009 May 27 certainly agrees skill-wise with Mike Loukides post of 2010 June 02.

  1. Skill #1: Statistics (Studying)
  2. Skill #2: Data Munging (Suffering)
  3. Skill #3: Visualization (Storytelling)

And I very much prefer "Data Munging" to "Computer Science" as one of the three skills.

I'll stick to the definition that I gave above for data science as "systematic study, through observation and experiment, of the collection, modeling, analysis, visualization, dissemination, and application of data". This is also applied statistics. So, what else is needed for this new discipline? Well, Mike and Michael are correct: computer skills, especially data munging, and art. Well, any statistician today has computer skills, generally in one or more of SAS, SPSS, R, S-plus, Python, SQL, Stata, MatLab and other software packages, as well as familiarity with various data storage & management methods. Some statisticians are even artists, perhaps as story tellers, as evidenced by that rare great teacher or convincing expert witness, perhaps as visualizers, creating statistically accurate animations to clearly describe the analysis, as evidenced by the career of that intern I hired so many years ago.

The data smith, the data artisan, must be comfortable with all forms of data:

  • structured,
  • unstructured and
  • semi-structured

Just as any other smith, someone following this new discipline might serve an apprenticeship creating new things from these forms of data such as a data warehouse or an OLAP cube, a sentiment analysis or a streaming SQL sensor web, or a recommendation engine or complex system predictives. The data smith must become very comfortable with putting all forms of data together in new ways, to come to new conclusions.

Just as a goldsmith will never make a piece of jewelry identical to the one finished days before, just as art can be forged but not duplicated, the data smith, the data artisan will glean new inferences every time they look at the data, will make new predictions with every new datum, and the story they tell, the picture they paint, will be different each time.

And perhaps then, the data smith becomes a master, an artisan.

PS: Here's a list of links to that Twitter conversation among some of the most respected people in the biz, on Data Analytics


April 2018
Mon Tue Wed Thu Fri Sat Sun
2 3 4 5 6 7 8
9 10 11 12 13 14 15
16 17 18 19 20 21 22
23 24 25 26 27 28 29
 << <   > >>

At the beginning, The Open Source Solutions Blog was a companion to the Open Source Solutions for Business Intelligence Research Project, and book. But back in 2005, we couldn't find a publisher. As Apache Hadoop and its family of open source projects proliferated, and in many ways, took over the OSS data management and analytics world, our interests became more focused on streaming data management and analytics for IoT, the architecture for people, processes and technology required to bring value from the IoT through Sensor Analytics Ecosystems, and the maturity model organizations will need to follow to achieve SAEIoT success. OSS is very important in this world too, for DMA, API and community development.

37.652951177164 -122.490877706959


  XML Feeds


Our current thinking on sensor analytics ecosystems (SAE) bringing together critical solution spaces best addressed by Internet of Things (IoT) and advances in Data Management and Analytics (DMA) is here.

Recent Posts

powered by b2evolution free blog software