HP Acquiring Vertica, Ramping Steadily Back Into Enterprise Data Warehousing Market

So you thought Neoview’s demise signaled HP’s exit from the enterprise data warehousing (EDW) market? You could not have been more mistaken. 

Yes, HP recently abandoned that slow-motion train wreck after several years of anemic customer adoption and directionless product management. But you should regard the unlamented Neoview as little more than a blip in HP’s long-running campaign to deepen its presence in all things EDW. And you should consider today’s announcement that they’re acquiring Vertica Systems as a key building block in HP’s emerging new strategy in the EDW market. It remains to be seen what that strategy is, inasmuch as new HP CEO Leo Apotheker has not yet articulated a clear vision. Perhaps he’s playing close to the vest so as not to call attention to further acquisitions in adjacent segments, such as business intelligence (BI) and data integration (DI).

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Waves of Innovation Continue to Transform Enterprise Data Warehousing Market

Every Forrester Wave is an in-depth snapshot of an entire vendor market segment at a particular point in time. 

Yesterday was that point in time for the latest update to the Forrester Wave for Enterprise Data Warehousing (EDW) Platforms. We just published this update after a grueling 8-month process of revisiting the criteria, scales, and weights associated with the most differentiating features of EDW vendors’ complex solution value propositions. Clearly, the EDW market has evolved considerably in the 2 years since we published the first installment of the Wave. 

At the highest level of analysis, what’s new? For starters, more vendors made the crucial inclusion criterion of having at least 100 customers with in-production EDW deployments. The field this time around included all seven vendors in the 2009 Wave, plus Greenplum (now part of EMC) and Vertica. Clearly, big-brand EDW mergers and acquisitions consolidated the original seven down to 5, as IBM acquired Netezza and SAP purchased Sybase. Please bear in mind that, as these acquisitions were reasonably recent, we chose to evaluate the acquired vendors’ offerings separately from their new parents' pre-acquisition EDW offerings in this latest Wave (it's worth noting that neither IBM nor SAP has slightest intention of discontinuing their pre-acquisition EDW portfolios--if anything, they're both now evolving those product families even more aggressively post-acquisition). 

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Predictions And Plans For Business Analytics In 2011

I love reporters. As someone with an M.A. in journalism who then evolved into an analyst, I recognize that both professions occupy approximately the same tier in the industry food chain. In fact, many IT industry analysts were trade press reporters at one point in their careers, and it’s not uncommon for analysts to go back into media institutions later on.

When great longtime IT reporters, such as Computerworld’s Jaikumar Vijayan, call me up to get my thoughts, I’m just as interested in their take on what’s important. Jai recently published an excellent article with my predictions, plus those of another analyst, on the year ahead in analytics. To the jaded reader, these sorts of year-end look-ahead articles may feel like perfunctory rehashes of stuff we’ve been telling them for quite some time, perhaps with a trendy new buzzword thrown in to keep it remotely glance-worthy.

I try not to repeat myself too much. Rather than regurgitate the statements I made in the phone interview with Jai, I’ll highlight how I’m addressing the principal business-analytics trends that I discussed with him — self-service, pervasive, social, scalable, cloud, and real-time—in our 2011 Forrester research agenda:

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An Enterprise Data Warehouse Without A Database—Is That Even Conceivable?

 The Twittersphere keeps chirping with definitional disputes about what exactly constitutes an enterprise data warehouse (EDW). This is the sort of debate that we geekier analysts love to engage in, since it gives us a chance to beat our chests and brandish our superior powers of cogitation. 

Since I have exposed skin in this game, I’ll flex my cognitive muscle a little bit more for those who wonder what Forrester’s position is on all this. Given that my update to the Forrester Wave™ for EDW platforms will come out in a month or so, this is probably a good time to level-set the discussion. In this post, I will also point to some trends that are pushing the boundaries of what an EDW is and can do for you. 

Some have argued that an EDW requires a DBMS, but is not, in itself, a DBMS. I’ve heard it said that a DBMS only becomes an EDW when it incorporates a schema and stores data. Still others argue that an EDW is something entirely distinct from a DW (without the “enterprise”) modifier, a data mart, or an operational data store (ODS). 

I find all of these perspectives hairsplitting and misleading, in that they blur the actual distinctions among these architectural constructs. Paradoxically and obliquely, though, they all hint at the rapid evolution of the EDW into something more protean and virtualized. 

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Want To Change Your CRM Game? Push Customer-Facing Processes Into The Social Cloud

As the year comes to a close, it’s good to put a wrap on it by reviewing all the shifts — both subtle and seismic — that have rocked the world of enterprise architecture (EA). I really enjoyed Gene Leganza’s recent look back — and look ahead — on the top 15 EA technology trends, and not just because he incorporated findings from my recent Empowered reports on social network analysis and analytics-driven engagement in multichannel customer relationship management (CRM).

You can read those Empowered reports to get a deep dive on how those trends evolved in 2010 and what we see on the horizon for 2011 and beyond. Fundamentally, Forrester considers deep customer engagement through social media as a hallmark of the leading-edge customer service operation. A growing range of companies have established social-media-based customer communities for service and support, involving various blends of social media, blogging, and other approaches.

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Advance Your Analytics Strategies: Blog-Ahead Thoughts Re Upcoming Forrester TweetJam

Can you feel it? The Twittersphere is spinning faster on its axis, anticipating the next Forrester TweetJam, Wednesday, December 15, 12:00 p.m. to 1:00 p.m. US Eastern Time. Our topic will be “Advance Your Analytic Strategies.” We’ll have a Forrester tweeting squad that includes yours truly plus any or all of the following Forrester colleagues: Rob Karel, Boris Evelson, Clay Richardson, Gene Leganza, Noel Yuhanna, Holger Kisker, Leslie Owens, Suresh Vittal, William Frascarelli, David Frankland, Joe Stanhope, Zach Hofer-Shall, and Henry Peyret.

Clearly, advanced analytics is an important theme and huge space that sprawls across many Forrester analysts’ focus areas. What I’m about to present are the humble opinions of one Forrester analyst — c’est moi — for whom this is the heart and soul of his focus going forward. These thoughts, presented under each of the proposed TweetJam questions, will give you a foretaste of what I’ll tweet on that session in just a few weeks: 

  • What exactly is and isn’t advanced analytics? 
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Use Advanced Analytics To Spotlight People Who Have The Biggest Impact On Customer Satisfaction

Community is an ideal toward which all social networks should aspire. In a true community, everybody is pulling for everybody else, sharing whatever assistance, expertise, and insight they possess with anybody who might benefit.

We all know that most communities are a bit more one-sided than that. In most communities, most people are essentially there for the ride, contributing little while benefiting from whatever resources the more generous among them have chosen to share. This is not necessarily a criticism of individuals or of society in general, but rather a recognition that as communities scale beyond close personal relationships, the bonds of reciprocity and altruism often grow weak.

This truism applies just as much to customer communities as to any other. Enterprises have avidly adopted social networks as virtual extensions to such customer relationship management (CRM) functions as call centers and user groups. In the new world of social-network customer communities leveraging blogs, Twitter, Facebook, and other channels, it is not uncommon that a handful of individuals post most of the useful content and feedback while the majority simply consume without contributing. And that’s fine, as long as you keep encouraging and incentivizing these actively engaged individuals —whom Forrester refers to as CRM highly empowered and resourceful operatives (HEROes) — to keep the useful content coming. In the final analysis, these are the sorts of individuals — expert customer service professionals, longtime customers, or even highly enthusiastic hobbyists — who can spell all the difference between true community and a haphazard scattering of nominally affiliated strangers.

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IBM Buying Netezza, Sending Clear Shot Across Oracle’s Bow

IBM’s acquisition of Netezza was a must for both vendors if they wish to grow their shares of the data warehousing (DW) market. They have a common archrival, Oracle, that is fielding an increasingly formidable appliance-based product portfolio, threatening both vendors’ long-term positions in this dynamic market. This announcement comes the same week as the annual Oracle OpenWorld conference, and we await word this week from Larry Ellison, Mark Hurd, and company on how they intend to respond (and you better believe they will).

First, here are the facts we have so far on IBM’s acquisition of Netezza. Big Blue is buying the publicly traded Marlborough, Massachusetts-based pure play for $1.7 billion. The deal, which represents $27 a share, 9.8% over Friday’s closing price and a near 85% premium over its 3-month trading average—is set to close in the fourth quarter. Netezza was founded 10 years ago, has around 350 customers in all major vertical industries, and employs around 500 personnel. Netezza has extensive partnerships with complementary technology providers, including, most notably, with IBM, which provides the hardware underlying its TwinFin DW appliances.

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Key Data Analytics Projects For Building Your Process Optimization Program

Rome was not reinvented in a day. Your enterprise business processes won’t turn around overnight either. You’ll need to re-engineer processes while you continue to run an ongoing business concern — albeit one with many buried layers, some splendid ruins, and many construction projects that cause never-ending traffic snarls.

Business process optimization is not a project you can deliver in a fortnight, nor is it a specific architecture or business model. Rather, it’s an ongoing program under which you implement various transformative technical projects in order to enable greater agility, efficiency, and effectiveness throughout key processes.

What are the key components of a business process optimization program? Forrester recommends that you establish an ongoing initiative that involves all business stakeholders, at all levels in the organization. Just as important, you will need to establish tight collaboration between business stakeholders and the myriad change agents, business architects, process architects, business analysts, data stewards, and analytics professionals upon which the success of your optimization efforts depends.

Enterprises should establish cross-functional programs under which to prioritize business process optimization projects around the following key pillars:

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Interdictive Analytics: Catching Baddies At The Pass And In The Nick Of Time

Predictive analytics is not just about forecasting what’s coming down the pike. It’s also about keeping the bad alternative futures from happening. If you can see the nasty things that might happen far enough in advance, you have a better chance of neutralizing or squelching them entirely.

In fact, many real-world applications of predictive analytics are “interdictive,” a term often used in military and law enforcement contexts to refer to tactics that delay, disrupt, or shut down an adversary’s forces or supply routes before they can do damage. Anti-fraud is one of the principal interdictive applications of predictive analytics technology. Companies everywhere rely on data mining to determine who’s been engaging, alone or in groups, in stealing money, supplies, finished goods, cellular airtime, and other valuables — and also where they’re likely to strike next. Likewise, anti-terrorism efforts rely on predictive models to sift through massive collections of historical and real-time intelligence in a Jack Bauer-like race against time and imminent disaster. You best believe that social network analysis is a key weapon in your arsenal for predicting and interdicting these sorts of malignant social patterns.

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