When the Wall Street Journal blasted the news that IBM acquired Netezza earlier today for $1.78 billion, I immediately dropped a note to my brother + soon-to-be brother-in-law to give them a heads up.  You see, the three of us have been talking about the "Big Data" space for a while now (thanks in part to my connections with some great folks at Aster Data).  Both family members are in the world of finance -- James in NYC, Nick in London -- and like to engage in pre- and post-deal talk at crazy times.  Like during a football game or at a wedding.  While I'm not on their level, I certainly appreciate that they include me in their discussions!  

Professionally, I'm fascinated by companies like Aster, Netezza, etc. that allow media companies to perform real-time, in-depth analyses of year-over-year data to optimize digital media "solutions."  But to James + Nick's interest, IBM's acquisition, preceded by EMC's purchase of Greenplum this July, suggests that other tech companies might soon be fielding new calls from potential acquirers.  While I could fill the rest of today's post with my own ideas of who might be next, I'd like to take it in a different direction that ties in with my new role at Bank Director.  

As I see it, the deal reinforces the value of enterprise-wide analytics in and among companies and government agencies.  In the past, I've written about how tools -- like those offered by a Netezza -- continue to gain mainstream support in companies and government agencies.  One such agency interested in the potential power of data analytics is the Comptroller of the Currency, a self-funded agency within the Treasury department (*if you're not familiar with the organization, OCC charters, regulates and supervises all national banks).  Before I left my old tech firm for Bank Director, I'd called on OCC's CIO to explore the promise of agile data management (which ties deep analytics with Big Data tools and data-driven dashboards).  So as I read this morning's press release, I couldn't help but reflect on what I'd learned about OCC's needs -- and the types of companies that might be well positioned to meet them.

Ironically, I switched from the WSJ article to a conference call with my new colleagues who themselves are going through a culture change (having been acquired by the NYSE).  Guess what I heard?  Nothing more than the former head of the OCC agreeing over the weekend to speak at Bank Director's flagship conference: Acquire or Be Acquired in Arizona in early 2011.  So too much of a coincidence not to share the overlap on DCSpring21.  Yes, it will be interesting to hear how he fields growth-oriented questions from sponsors + attendees, especially as government and financial institutions continue to converge around areas and opportunities designed to fuel expansion or acquisition.  But for the purposes of this post, how appropriate that the day starts with an M&A story and ends on the same note?  Albeit, one is about what's already happened, the other, what will be.

 

The technology landscape has rapidly evolved since I moved to D.C. in the summer of 2005.  For many in the industry, such a statement might evoke an image of me crashing through an open door... still, this reality smacked me in the face just a few minutes ago.  With a bunch of boxes scattered around my office, I stumbled upon some aged notes I took on a company called InfoEther during my first few months here at Computech.  I had to pause, as I recalled my introduction to something called Ruby on Rails (at the time, a relatively new open source web framework) and the promise of distributed collaboration and co-creation.
Flash forward to today, and past discussions relative to the pros/cons of open source technologies and frameworks seem quaint, if not obsolete.  Building websites using Ruby on Rails?  The guys at Inkling built their predictive marketing platform using it (just as Hulu, Basecamp and other "must-have-bookmarked" sites on my laptop did).  In organizations of all sizes, crowd sourcing is in.  And so too the rise of web-based communities.  McKinsey recently noted in its "Clouds, big data, and smart assets: Ten tech-enabled business trends to watch:"

In just over two short years, Facebook has quintupled in size to a network that touches more than 500 million users.... More than 4 billion people around the world now use cell phones, and for 450 million of those people, the Web is a fully mobile experience."

Pulling together some thoughts from this morning's Big Data summit, hosted by Aster Data at the St. Regis in Washington, D.C...
In previous posts on Big Data and deep analytics, I've touched on my interest in this space. Simply put, with the explosion of data ingestion in most companies (both in terms of volume and complexity), sales and marketing executives need a better and faster way to identify complex customer trends, purchasing patterns and market segments.  This morning's presentations -- jump started by Curt Monash, the author of the very informative DBMS2 blog -- focused on what companies are trying to do to enable deep analytics.  While the brand recognition of BI heavyweights MicroStrategy and SAS's trumps that of Aster's, the Silicon Valley-based company's CTO + co-founder, Tasso Argyos, really rekindled my appetite for "agile data management"-focused ideas and discussions.

Case-in-point: Tasso shared four common use cases for Aster's software, optimized for advanced analytics running on commodity hardware:

1. Forecasting;
2. Modeling;
3. Customer segmentation; and
4. Clickstream analysis

Over the last few weeks, I've had my hands full with a number of spectrum-related opportunities at Computech.  Be it interest from the independent regulator and competition authority for the UK communications industries (Ofcom) to a new supply agreement to provide spectrum auction services to Industry Canada, an RFP from Ireland's Commission for Communications Regulation to a handful of our auction team working in Mexico City with COFETEL, it seems like my days have been filled to capacity with talk of managing, allocating and regulating this scarce, valuable resource that cannot be seen or felt.

Spectrum Holdings in the 700MHz band range (c/o the FCC's Spectrum Dashboard)

Heeding the advice of my good friend Tien Wong, I've focused more of my personal time on better understanding telecommunication-related issues that are impacted by such governmental interests.  Tien, the CEO of the VC firm Opus8, suggested that my last five years running business development at our firm, coupled with Computech's 16 years of IT-related support at the FCC, affords me a rather interesting perspective on how technology + the telecommunications "industry"intersect (*by which I mean regulators & industry alike).  As my recent posts show, my technical interests trend towards deep data analytics; conceptually, this matches up nicely with our firm's expertise with spectrum management.  Indeed, the balancing act that so many countries' regulatory bodies need to strike continues to play out on the global stage (and yes, we're happy to play a supporting role).  Just as with agile data management, the key for most telecommunication authorities centers around real-time knowledge of spectrum demand, use and oversight.

So, over the next few weeks, I'm going to shift my writing focus from Big Data, data-driven dashboards and data analytics to the technical implications of managing a spectrum lifecycle.  Being that I'm heading up to Ottawa on Tuesday for their annual 20/20 Spectrum conference -- and to Brussels in June for the annual European Spectrum Management conference -- what better time (or place) to start sharing my observations than now on DCSpring21.  While I can't promise specific topics, my travel schedule does provide some general direction.  Due up this week: my observations from Canada based on perspectives shared by senior spectrum, radio and telecommunications officials from around the world.

Plenty of businesses invest money and resources trying to integrate, organize and manage data. Yet too often, it seems they become overwhelmed by it.  Gartner Research says it best: such "efforts result in fragmented views, siloed information and missed opportunities. This incoherent vision can ultimately damage competitiveness and increase IT and business costs. In today’s economic climate is that a risk worth taking?"  So in the spirit of yesterday's post on Big Data, a "simple" rendering of a data management technology stack (at least as we think about it @Computech):

While some might find this diagram abstract, the goals of agile data management are becoming clearer to me:

• Make data in an organization's databases available in an understandable + explorable format;
• Provide easy access & navigation from one subject area to another (e.g. combine data from one application with another application's data); and
• Ensure data flows within an organization become transparent -- regardless of which operational system generates/maintains the data.

Periodically, I look back on what I've written here -- and also on Computech's Concomitantly.  Last night, as I read Full 360's blog on the Agile Data Warehouse Story, I realized I'd jumped headfirst into the agile data management space without really defining an important concept: Big Data. Used to describe the massive quantities of raw and processed data that are being generated today in applications such as retail operations, let me add some color to the Big Data concept by pasting a sentence from the afore mentioned blog:

"Think of all the McBurgers sold every minute of every the day in a dozen different combinations. That purchasing information is valuable - but only if it can be made manageable, and accessible."

True, it’s become relatively easy (and cheap) to collect data. However, organizations of all sizes continue to wrestle with a fundamental challenge: making sense of what they’ve collected given the speed with which new types of data are generated. So as business leaders begin to execute new strategies based on better analytics and information, organizations with individualized, distributed computing and storage environments may soon require a more flexible, agile, and service- oriented solution to improve their proficiency in handling Big Data.  Make sense?  Drop me a line + let me know your thoughts.