All Things Must Come to an End—PatternBuilders is Shutting Down

By Terence Craig and Mary Ludloff

There’s a sad, but true, statistic that every entrepreneur knows by heart: 9 out of 10 startups fail. Unfortunately, PatternBuilders is adding its number to this pile. We have been procrastinating writing this post because shutting down a company is hard. When you put your heart and soul into something, you need time to process, reflect, and eventually get to the point where you can move on.

But moving on does not mean that we are disappearing; after all, shutting down the company does not end our passion for big data, privacy, and all things tech-related (especially IoT). To that end, we will be maintaining this blog, as our main place to write and comment about those issues.  We are also consulting around all areas involving big data and/or privacy (via our existing consulting organization, Ludloff-Craig Associates) and are working on some other things that we are keeping under wraps for now. But if you follow our blog, @terencecraig, or @mludloff, you will be the first to know.  And if you have interesting opportunities, consulting projects, or for the right company – a full-time job ­– please get in touch.

There are a number of reasons why we are shutting our doors, but suffice to say, we made some decisions we knew might have an adverse effect on the company. And we stand by those decisions.

Getting Value from Your Data Series: It’s All About Your Data Ecosystem

By Marilyn Craig and Mary Ludloff

We’re back with the fourth post in our series on how to get value from your data, including how to ensure that new “data” and “analytics” products are designed for successful delivery to new and existing customers.

In the previous posts in this series, we discussed our methodology and what is required in terms of understanding your target customer—who they are and what they need—as well as making sure you have the right Team in place to work on the project. In this post, we are going to discuss how you build your Data Ecosystem:

• What is needed to ensure that data processes will support the new product(s)?
• How do you identify appropriate data partners and enhancements?
• What privacy- and security-related issues must you be aware of and address?

Getting Value From Your Data Series: The Road May Be Rocky But It’s Well Worth the Effort!

By Mary Ludloff and Marilyn Craig

Unless you’ve been asleep for the past couple of years, you, like us, have heard this phrase again and again:  Data is the new oil.  It certainly sounds great but what exactly does it mean? Here’s our take: Getting the most value out of your data can make you better at what you do as well as enable you to do more with what you have. In other words, there’s unrealized value in those data silos that all companies have. But make no mistake: the road to realizing data value is paved with good intentions and often times, poor execution and results.

Today, most companies are drowning in data—there’s historical data from operations, data from public sources, data from partners and acquisitions, data you can purchase from data brokers, etc.  These companies have read all the research and want to leverage their data assets to make “better” operational decisions, to offer their existing customer base more insights, to pursue new revenue opportunities. Of course, the real value in that data is derived from the business analytics that deliver the insights that drive better decisions. As we’ve said quite often on this blog: Data, without the proper use of analytics, is meaningless. If data is the new oil, think of analytics as the oil drills—you need both to be successful.

Enterprise Software in the Cloud: Why We Chose Azure as our First PaaS Platform

By Terence Craig

I’ve been absent from the blog too long, but if you’ve been following my colleagues (Mary and Marilyn) postings, you’ll see it’s been a very busy and fruitful time at PatternBuilders.  While I’m still overdue for the next segment of the architecture blog series, I thought I would take a break and talk a bit about some of the things we learned as we moved our product and business model to Microsoft Azure.

As someone who has worked with Microsoft technology and partnered with them off and on over the last two decades (even flirting with going to work for them a couple of times), the most surprising discovery was how serious Microsoft has become about the cloud, open source, and being an active and supportive partner for startups.  As many of you who have been around as long as I have will no doubt remember, this is a very different, some would say revolutionary, move for the world’s most powerful proprietary software company.  We had some concerns when we became members of Microsoft’s Azure Startup program BizSpark Plus and subsequently the more exclusive BizSpark One, but it has turned out to be a great experience for us on both the business and technical level.

Our Favorite Reads of 2012

By Mary Ludloff & Terence Craig

Greetings one and all! 2012 was a breakout year for PatternBuilders and we are very grateful to all of you for helping to make that happen. But we would also like to take a minute to extend our condolences and share the grief of parents across the world that lost young children to violence. Newtown was singularly horrific but similar events play out all too often across the globe. We live in an age of technical wonders—surely we can find ways to protect the world’s children.

This is our last post of 2012 and in the spirit of the season, we decided to do something a little different this year. Recently, the Wall Street Journal asked 20 of its “friends” to tell them what books they enjoyed in 2012 and the responses were equally eclectic and interesting. Not to be outdone, Adam Thierer published his list of cyberlaw and info-tech policy books for 2012. Many of the recommendations culled from both sources ended up on our reading lists for 2013 (folks, 2012 is almost over and between launching AnalyticsPBI for Azure and working on our update for Privacy and Big Data, not a lot of “other” reading is going to happen during the holiday season!) and spurred an interesting discussion about our favorite reads of the year. One caveat: Our lists may include books we read but were not necessarily published this year. So without further ado, I give you our favorite reads of 2012!

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

By Terence Craig

For 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.

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.

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?

Big Data: Either Embrace It or Be Among the Walking Dead (Thoughts on Strata West 2012)

By Mary Ludloff

I’ve been meaning to blog about Strata West for the last week or so but felt the need to take a step back and look at the conference objectively. Of course, we’ve also been very busy at PatternBuilders working on our latest release (where correlation is the king and financial services is the queen or vice versa), engaging with potential partners and customers, and all the other activities that make up a startup’s life. In other words, during and after the conference we’ve barely been able to catch our collective breath (as well as get some much needed rest)!

So before I talk about the conference as a whole as well as some of the sessions and folks that caught my eye and of course, our book signing event (yes, Terence and I signed many books for conference attendees), I wanted to give a final shout out to our stellar Big Data and SCM panelists: Lora Cecere, Pervinder Johar, and Marilyn Craig. Thank you all for participating and for taking on this very broad topic! Much ground was covered, including the need for more rigorous cold chain management to ensure the efficacy of drugs, the amount of food that is spoiled and thrown away (one out of every three fruits and vegetables and two out of every five chickens) due to poor logistics management, and how big data can be used to transform the auto repair industry. What I loved about this panel (and yes, I am admittedly biased) was that it focused on real world problems that companies, industries, and societies are dealing with today. By the way, our panel was part of Strata Jumpstart—billed as the missing MBA for Big Data and it certainly lived up to its billing!

Big Data and Cloud not a fit? Comments on Infoworld Article

By Terence Craig

Since Disqus seems to have completely eaten (bleh) my comment on @davidlinthicum’s very interesting InfoWorld post – Big data and the cloud: A far from perfect fit, I decided to just expand my comments and make a short blog post out of it. IMHO the problems that David is describing are more a reflection of problems with batch oriented technologies like Hadoop (more on my take on Hadoop here) in the cloud than a general problem for cloud based big data solutions.

Computing always has, and probably always will have, a bias towards creating batch focused technologies at the beginning of any large paradigm shift.   But as new technologies are absorbed, understood, and move from early adopter to more mainstream use, the batch paradigm will inevitably start to shift to streaming and real-time. We have seen this again and again (from punch cards to touch sensitive tablets, downloaded media to streaming media, DOM to SAX parsers, HTML to Ajax, paper maps to real-time GPS). The reason this evolution almost always occurs is simple: humans live and think in real-time and when our tools do as well we are more productive and happier.  So why do we have this bias for batch processing in our first generation computational technologies? Simply put, because batch processing is a lot easier.