Posts filed under ‘General Analytics’

Microsoft News Center: PatternBuilders brings big data analytics down to size

By Mary Ludloff

MSOFT BizAs regular readers of this blog know, Terence and I spend a great deal of time talking about the state of the big data analytics industry and what is needed before mainstream adoption becomes an actual fact (as opposed to the hyperbolic reporting on any and all things related to big data). Recently, we sat down with the Microsoft BizSpark (a partner of ours) team to talk about the state of big data analytics today and why we decided to co-found PatternBuilders. To read the full story, go here. And because I can never resist a great quote (I am in marketing after all), here’s what Terence had to say during our interview:

“We found it disconcerting that there was such a huge divide between big data excitement and actual adoption rates. Taking advantage of big data analytics often requires a budget, toolset and in-house expertise far beyond what most enterprises can muster. Mary and I founded PatternBuilders because we thought there must be a better approach.”

For more information on our technology choices and why we are unabashed fans of Microsoft technologies, you may find these posts helpful:

May 25, 2013 at 7:26 am Leave a comment

Boston Marathon Bombings: How To Help

By Mary Ludloff

Sadly, this week we were reminded once again of the fragility of life and the resilience of the human spirit. Terence, myself, and the PatternBuilders team send our condolences to all who were impacted by this tragedy. For those who would like to help, donations can be made to:

A number of resources can also be found here.

Much as it pains me to say this, beware of bogus Boston Marathon charity websites. Melanie Hicken of CNNMoney offers some advice on what to look out for.

Finally, there have been many moving tributes made by people via blogs, twitter, and other media sources. We leave you with this simple statement projected on the wall of the Brooklyn Academy of music:

Boston Marathon 2

April 17, 2013 at 4:04 pm Leave a comment

Big Data Project: Let’s Start at the Very Beginning—The Big Data Playbook

By Mary Ludloff

big data playbookIn my last post, I wrote about the three V’s of big data and why there are only three. There has been a messaging pile-on that seems to be happening in the big data space that even I, long-time marketer, find disconcerting. So, over the course of a number of posts, my colleague, Marilyn Craig, and I are going to de-mystify a big data project, taking apart each stage of a real big data initiative as if it were a release post-mortem. We will be talking about roles and responsibilities, data governance, project and process management, what went right, what went wrong, what we should have done differently. Except in this case, it will not be after the fact but rather a stage-by-stage review as we work on a real-world project. For your sanity and ours, we have created a special category, Big Data Project, as well as a tag with the same name. If you search on either, you will see all posts related to the project. Additionally, all posts about the project will start with Big Data Project in the title. Who knows? Maybe when we’re done, we’ll write a book (knowing what I know now about writing a book, I can’t believe I just said that)!

We’ll talk more about the project in the next post but first I wanted to take a look at a big data failure that anyone involved in a major enterprise application deployment could have seen coming and is Rule #1 in our big data playbook:

Rule #1: Big Data IS NOT rocket science.

(more…)

March 21, 2013 at 10:50 am 4 comments

A Big Data Showdown: How many V’s do we really need? Three!

By Mary Ludloff

3 vs of big dataMarilyn Craig (Managing Director of Insight Voices, frequent guest blogger, marketing colleague, and analytics guru) and I have been watching the big data “V” pile-on with a bit of bemusement lately. We started with the classic 3 V’s, codified by Doug Laney, a META Group and now Gartner analyst, in early 2001 (yes, that’s correct, 2001). Doug puts it this way:

“In the late 1990s, while a META Group analyst (Note: META is now part of Gartner), it was becoming evident that our clients increasingly were encumbered by their data assets.  While many pundits were talking about, many clients were lamenting, and many vendors were seizing the opportunity of these fast-growing data stores, I also realized that something else was going on. Sea changes in the speed at which data was flowing mainly due to electronic commerce, along with the increasing breadth of data sources, structures and formats due to the post Y2K-ERP application boom were as or more challenging to data management teams than was the increasing quantity of data.”

Doug worked with clients on these issues as well as spoke about them at industry conferences. He then wrote a research note (February 2001) entitled “3-D Data Management: Controlling Data Volume, Velocity and Variety” which is available in its entirety here (pdf too). (more…)

January 17, 2013 at 7:06 pm 4 comments

AnalyticsPBI for Azure: Turning Real-Time Signals into Real-Time Analytics

By Terence Craig

PBI 3 0 archslide 3For the second post on AnalyticsPBI for Azure (first one here), I thought I would give you some insight on what is required for a modern real-time analytics application and talk about the architecture and process that is used to bring data into AnalyticsPBI and create analytics from them. Then we will do a series of posts on retrieving data. This is a fairly technical post so if your eyes start to glaze over, you have been warned.

In a world that is quickly moving towards the Internet of Things, the need for real-time analysis of high velocity and high volume data has never been more pronounced. Real-time analytics (aka streaming analytics) is all about performing analytic calculations on signals extracted from a data stream as they arrive—for example, a stock tick, RFID read, location ping, blood pressure measurement, clickstream data from a game, etc. The one guaranteed component of any signal is time (the time it was measured and/or the time it was delivered).  So any real-time analytics package must make time and time aggregations first class citizens in their architecture. This time-centric approach provides a huge number of opportunities for performance optimizations. It amazes me that people still try to build real-time analytics products without taking advantage of them.

Until AnalyticsPBI, real-time analytics were only available if you built a huge infrastructure yourself (for example, Wal-Mart) or purchased a very expensive solution from a hardware-centric vendor (whose primary focus was serving the needs of the financial services industry). The reason that the current poster children for big data (in terms of marketing spend at least), the Hadoop vendors, are “just” starting their first forays into adding support for streaming data (see CloudEra’s Impala, for example) is that calculating analytics in real-time is very difficult to do. Period.

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December 12, 2012 at 5:22 pm 8 comments

Introducing AnalyticsPBI for Azure—A Cloud-Centric, Components-Based, Streaming Analytics Product

By Terence Craig

It has been a while since I’ve done posts that focus on our technology (and big data tech in general). We are now about 2 months out from the launch of the Azure version  of our analytics application, AnalyticsPBI, so it is the perfect time to write some detailed posts about our new features. Consider this the first in the series.

But before I start exercising my inner geek, it probably makes sense to take a look at the development philosophy and history that forms the basis of our upcoming release. Historically, we delivered our products in one of two ways:

  • As a framework which morphed (as of release 2.0) into AnalyticsPBI, our general analytics application designed for business users, quants, and analysts across industries.
  • As vertical applications (customized on top of AnalyticsPBI) for specific industries (like FinancePBI and our original Retail Analytics application) which we sold directly to companies in those industries.

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November 29, 2012 at 8:38 am 8 comments

In a New York/East Coast Frame of Mind—Ways to Help Hurricane Sandy Victims

By Mary Ludloff

A week ago, I was in New York City for Strata’s Big Data Conference. The weather was sunny and mild and as I walked around the City I was reminded of just how vibrant it is and told my husband later that evening that we have to visit it more often. After the conference, I headed home and then watched with disbelief as this wonderful city, surrounding areas, and many more states were engulfed by Hurricane Sandy. I was saddened by the destruction and loss of life, but today am reminded of the resilience of its inhabitants as the clean up and rebuilding begins. For those of you interested in helping, I point you to ABC News’ story and the Wall Street Journal’s article on ways to help the storm victims. Or you can go to the Red Cross home page for information on how to make a financial donation or give blood. To all of you on the East Cost impacted by Hurricane Sandy: Our hearts go out to you and you are in our prayers.

November 1, 2012 at 6:50 am Leave a comment

Big Data and Science: Focus on the Business and Team, Not the Data (Part 3 of 3)

By Mary Ludloff

Let me tell you a little secret: I always know when I am talking (and working) with a company that has successfully launched big data initiatives. There are three characteristics that these companies share:

  1. A C-level executive runs the “[big] data operations.”
  2. The Chief Data Officer (even if they are the CIO) has a heavy business/operations background.
  3. The data team is focused on the “business,” not the data.

Did you notice that technology and data science are not reflected in any of the characteristics? Some of you may consider this sacrilege—after all, we are operating in a world where technology (and I happily work for one of those companies) has changed the data collection, usage, and analysis game. Colleges and universities are now offering master degrees in analytics. The role of the data scientist has been pretty much deified (I refer you to Part 1 of this series). And we all need to be very worried about the “talent shortage” and our ability to recruit the “right analytical team” (I refer you to Part 2 of this series).

Yes—technology has had a tremendous impact on how much data we can collect and the ways in which we can analyze it but not everyone needs to be a senior computer programmer. Yes—we all should strive to be more mathematically inclined but not all of us need Master’s or PhD’s in statistics or analytics. Yes—some companies, based on their business models, may have a staff of data scientists but others may get along just fine without one (with the occasional analytics consultant lending a hand). (more…)

October 20, 2012 at 4:50 am 4 comments

Data Science: What the World Needs is Answers, Not Just Insights Part 2 (of 3)

By Marilyn Craig, Managing Director, Insight Voices

As you may or may not know, we are in the midst of a 3-part series on data science, covering roles, skills, etc.—generally what you should think about as well as what’s not as important (no matter what the latest articles say!). For Part 2, we have a guest poster—Marilyn Craig of Insight Voices. Marilyn is what I like to call a “classic quant.” She has been at the forefront of big data and data science before most people knew these terms (and spaces) existed and has been my go-to person whenever I had an analytics question (see title) that I needed an answer to. In this post, Marilyn looks at insights and makes the case for why we should all care far more about answers. Take it away Marilyn!

Here’s an interesting question for this new world order of Big Data Analytics: what’s an Insight and what’s an Answer? Sometimes they are the same, sometimes not. An insight is a piece of information or understanding. It may or may not be useful. It may or may not help your business improve, solve world hunger, or even make sense. An answer is always useful. It is the result of asking a question. And the best kinds of answers are those that solve the questions that you really care about. (more…)

October 8, 2012 at 10:57 am 5 comments

In Search of Elusive Big Data Talent: Is Science Big Data’s Biggest Challenge? Or Are We Looking in the Wrong Places? (Part 1 of 3)

By Mary Ludloff

When we talk to prospects about their big data initiatives our conversations usually revolve around issues of complexity that goes something like this:

“Big data is so big (no pun intended), there’s such a variety of sources, and it’s coming in so fast. How can we develop and deploy our big data projects when everyone is telling us that we need lots and lots of data scientists and oh, by the way, there aren’t enough?”

Admittedly, many media outlets and pundits are positioning the search for skilled big data resources as what I can only characterize as the battle for the brainiacs. Don’t get me wrong, I am not disputing McKinsey’s report on big data last year that made it clear a talent shortage was looming, estimating that the U.S. would need 140,000 to 190,000 folks with “deep analytical skills” and 1.5 million managers and analysts to “analyze big data and make decisions based on their findings.” But the hype surrounding the data scientist is getting a bit absurd and we seem to be forgetting that those 1.5 million managers and analysts may already be “walking amongst us.” Is a shortage of data scientists really big data’s biggest challenge? (more…)

September 30, 2012 at 2:04 pm 7 comments

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