Data Warehouse… Done: Automatic for and by the business
Toward frictionless data discovery: IBM Fluid Query eases the way
Published Articles June-July 2015
In a feature new to my website, I’m bringing together links to articles and blogs I’ve written for other sites and press articles citing my opinions. I’m thrilled that this current June-July sees more published articles than I’ve ever had over a similar time period. They include an overview of my Business unIntelligence architecture, observations on the social and economic implications of big data, technological displacement of jobs, and an exploration of the value of context setting information in data wrangling.
From Layers to Pillars—A Logical Architecture for BI and Beyond
1 June, Business Intelligence Journal, Vol. 20, No. 2
This paper outlines the conceptual and logical level architectures which emerge from the data and processing needs of modern business, operating in a world of abundant information, high connectivity and powerful technology. Recognizing three distinct types of data/information, the architecture supports shared context across these types, and the key role of traditional, modeled data in creating consistency and enabling governance. By defining pillars of data/information as a logical design, it supports optimization of technology choices and eases migration from current implementations, in contrast to the Data Lake approach favored by some in the industry.
Business unIntelligence and the Problem with the Data Wave
26 June, MediaPlanet, Future of Business and Tech
With the advent of big data and sophisticated analytical algorithms, business intelligence now stands on the verge of unintended world domination.
The Sexiest (And Last?) Job Of The 21st Century
3 July, TechCrunch
…data scientist may well continue to be the sexiest job of the 21st century. It will certainly be among the last…
Data Wrangling, Information Juggling and Contextual Meaning, Part 1
6 July, Eckerson Group
“Data wrangling is a huge—and surprisingly so—part of the job,” said Monica Rogati, vice president for data science at Jawbone. With all due respects to Ms. Rogati, the only surprising thing about this is her surprise. Data wrangling, also previously known as data cleansing and integration, is as old as data warehousing itself. Older perhaps, than many data scientists in senior roles in the industry… The really interesting aspect is that this extensive preparation effort occurs even after IT has done the groundwork for the data warehouse. The implication, perhaps, is that we are missing something, and have been missing it for some time.
The ethics of big data is an industry concern: Dr Barry Devlin
23 July, CIO New Zealand, by
One the pioneers of Big Data research says: The ‘fast and furious’ collection of data spawns privacy and economic issues. “We cannot just put our heads in the sand like the scientists who tamed atomic energy but said something like, ‘We just invented the bomb but we did not use it’.”
Data Wrangling, Information Juggling and Contextual Meaning, Part 2
23 July, Eckerson Group
The importance of context for data wrangling (and, indeed, analysis) cannot be over-estimated. Context may exist both within formal metadata and elsewhere. I coined the phrase context setting information (CSI) as a more general and useful name for metadata.
Future BI Could Perfect Labor’s End, Part 1
24 July, IT Knowledge Exchange, Now…Business unIntelligence Blog
It’s indisputable that technology is displacing many of today’s jobs. The question is: what should, or can, we do about it? This series explores the possible consequences of this shift and how information use and decision making support through enhanced and expanded Business Intelligence.
“How to take the mind-set of a startup”
28 July, CIO New Zealand, by
“Look at it from the way new entrants do.” This is the prime advice from Dr Barry Devlin, one of the pioneers of Big Data research, on how CIOs can tackle disruption caused by advances in technology.
Photo Copyright: bowie15 / 123RF Stock Photo
Insights Across the Hybrid Enterprise: Big Data 2015
April 2015
Multiple sponsors
The third EMA / 9sight Big Data Survey was conducted in late 2014, with the results published in April 2015. Beyond Big Data itself, the survey also addressed the concepts of data lake, data driven and the Internet of Things, providing a comprehensive view of the state of thinking in the broadest definition of Big Data from 351 respondents. (more…)
BI, Built to Order, On-demand: Automating data warehouse delivery
January 2015
Sponsored by WhereScape
Three key ways in which data warehouse automation changes the design, development and ongoing maintenance of data warehouses and marts. First, how automation addresses the old conundrum of delivering consistent, quality data in the timeframe demanded by modern business needs. Second, how streamlining the overall process provides a single repository of metadata and integrated tooling to speed and simplify development. Third, how business and IT can truly collaborate in delivering business solutions. (more…)
The Emergent Operational/Informational World: An examination of the emerging importance of HTAP
November 2014
Sponsored by NuoDB Inc.
We explain how and why the current layered DW architecture developed and examine why today’s business imperatives of speedy decision making and data driven action taking demand a new operational/informational approach. This environment, also called HTAP (hybrid transaction/analytical processing), needs a combination of in-memory operation and novel database techniques to enable simultaneous read/write and long-read activities on the same data. (more…)