Philip Russom, Ph.D., is senior director of TDWI Research for data management and is a well-known figure in data warehousing, integration, and quality, having published over 550 research reports, magazine articles, opinion columns, and speeches over a 20-year period. Before joining TDWI in 2005, Russom was an industry analyst covering data management at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and consultant, was a contributing editor with leading IT magazines, and a product manager at database vendors. His Ph.D. is from Yale. You can reach him by email (prussom@tdwi.org), on Twitter (twitter.com/prussom), and on LinkedIn (linkedin.com/in/philiprussom).
Blog by Philip Russom
Research Director for Data Management, TDWI
The current hype and hubbub around big data analytics has shifted our focus on what’s usually called “advanced analytics.” That’s an umbrella term for analytic techniques and tool types based on data mining, statistical analysis, or complex SQL – sometimes natural language processing and artificial intelligence, as well.
The term has been around since the late 1990s, so you’d think I’d get used to it. But I have to admit that the term “advanced analytics” rubs me the wrong way for two reasons:
More
Posted by Philip Russom, Ph.D. on August 5, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
Do you know what a cul-de-sac is? In French, it literally means “bottom of the bag.” But figuratively it means what most Americans would call a “dead-end street.” In residential real estate, a cul-de-sac is a desirable place to live. In analytics, a cul-de-sac is where the epiphanies of advanced analytics never get off a dead-end street to be fully leveraged elsewhere in the enterprise.
The current hype around big data analytics has most discussions of analytics focused on “discovery” analytics. That’s where a business analyst or similar user employs an advanced analytics tool (based on data mining, statistics, natural language processing, complex SQL, etc.) to discover facts never known before. For example, the analyst may discover the root cause for a new form of customer churn, a new partner behavior that’s potentially fraudulent, or the hidden costs that erode otherwise profitable customers.
More
Posted by Philip Russom, Ph.D. on July 21, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
While researching a new TDWI report on big data analytics, I’ve run across a few BI professionals who are concerned about the seeming lack of data preparation that’s common with some forms of advanced analytics. Allow me a moment to sort this out.
On the one hand, all of us in BI and data warehousing are indoctrinated to believe that the data of an enterprise data warehouse (EDW) (and hence the data that feeds into reports) must be absolutely pristine, integrated and aggregated properly, well-documented, and modeled for optimization. To achieve these data requirements, BI teams work hard on extract, transform, and load (ETL), data quality (DQ), meta and master data management (MDM), and data modeling. These data preparation best practices make perfect sense for the vast majority of the reports, dashboards, and OLAP-based analyses that are refreshed from data warehouse data. For those products of BI, we want to use only well-understood data that’s brought as close to perfection as possible. And many of these become public documents, where problems with data could be dire for a business.
More
Posted by Philip Russom, Ph.D. on July 12, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
A few weeks ago, I talked with Mike Eacrett, the vice president of product management for SAP HANA at SAP Labs. Among other things, Mike explained the “secret sauce” that gives SAP HANA flexibility and performance for big data analytics. Give me a moment to recount Mike’s explanation.
Philip Russom
: What forms of analytics are you seeing on the rise with SAP customers?
More
Posted by Philip Russom, Ph.D. on June 27, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
What exactly is Big Data Analytics?
It’s two things: big data and the kind of analytics users want to do with big data. Let’s start with big data, then come back to analytics.
Users interviewed by TDWI state that data isn’t big until it breaks 10Tb. So that’s the low end of big data. And some user organizations have cached away hundreds of terabytes--just for analytics. The size of big data is relative; hundreds of TBs isn’t new, but hundred just for analytics is—at least, for most user organizations.
More
Posted by Philip Russom, Ph.D. on June 21, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
In prior blogs, I’ve talked about how big data’s primary attribute is data volume. That’s pretty obvious. But it’s defined by other characteristics, too. For example, one of the things that makes big data so big is that it’s coming from a greater variety of sources than ever before. Now let’s look at the last of the three Vs of Big Data Analytics, namely data velocity.
Data Feed Velocity as a defining attribute of Big Data
More
Posted by Philip Russom, Ph.D. on June 17, 20110 comments