Relations with Analysts...the second

All:

Second Q posed to the Forrester AR Council panel on how to relate to the blogosphere, followed by my A:

• Q: Does the manner in which the AR professional deals with blogging change with the size of the organization, e.g., is it harder for AR at larger firms to anticipate and address the myriad of issues coming at them from blogging pundits? Are smaller, more agile firms at an advantage?
• A: Hard to say. If you’re a bigger, more diversified, more dynamic vendor you’re likely to elicit more commentary from more external parties through more channels on more issues more of the time. But you’re also likely to have more of your own people reading--and anticipating--all of this, and preparing/spinning suitable responses. But you’re also more likely, if you’re big, to have more trouble coordinating internally among all stakeholders in order to prepare a concerted response. But, conversely, if you empower more of your people to post replies/counter-attacks through their own blogs, or through your company’s blogs, you can defuse the issues more rapidly. Or, if you’re not careful, light more fuses. And give the appearance that your right hand doesn’t know what your left hand is doing. Gee I wish there were easy answers.

Jim

Relations with Analysts...the first

All:

Whew...quite a string of travels...not through it all yet. In the past month, I’ve been to TIBCO’s TUCON (San Francisco) and SAP’s SAPPHIRE (Orlando), plus a quick IT vendor consult...next week, I do Informatica (Vegas), then the following week Microsoft (Orlando)

Last week, I was at Forrester's IT Forum 2008 in Vegas, where, among other things, I participated in a panel session on blogging, focusing on how analyst relations (AR) professionals should relate to “influencers” in the blogosphere.

Organized by Forrester’s Analyst Relations Council and moderated by Forrester VP Laura Ramos, the panel brought together leading IT industry analyst/bloggers plus those who blog-about-analyst/bloggers: Carter Lusher, president Sage Circle; Dana Gardner, principal analyst, Interarbor Solutions; Bill Hopkins, founder & CEO, Knowledge Capital Group; and Jonathan Eunice, founder and principal consultant, Illuminata. Oh, and a “token” Forrester analyst who’s been kicking around the blogosphere for a few years, including, increasingly, under our information and knowledge management blog (in case you’re wondering why the rate of postings to my personal blog has dropped in the past few months--still searching for the right rhythm and balance and partitioning of the jim-o-spheres, left and right, between the two).

Last week’s Forrester AR Council panel was well-attended, and the questions from council members were excellent. My fellow panelists were everything we could have hoped: smart, informed, opinionated, articulate, provocative. I’ll leave it up to them, in their respective blogs, to repeat what they put forth.

Here now, is the first question that was posed to us, plus generally how Kobielus responded:

• Q: How do AR professionals stay on top of bloggers and determine who to interact with and “influence” and who to ignore?
• A: Simply ask yourself who you read, who your colleagues read, your clients read--whose pieces you/all forward--whose you/all link to--whose ideas stick in your minds--whose names, reputations, and methodologies resonate with everybody in your immediate work environment and/or industry. Those are the indicators of “influence.” To the extent that analyst exerts such influence purely through one channel--blogging--all power to them. But the best analysts have always availed themselves of all channels at their disposal to inject their ideas into the bloodstream of the industry. Chances are that the chief “bloggers” are established analysts who have simply reinforced their brand through this medium. If they’ve made blogging the core of their for-pay business model, cool (and please explain how). Most of us analysts use blogging in various and sundry funky ways to supplement/promote our for-pay gigs.

By the way, this goes without saying (or does it?), this is not the official Forrester position on all of this (we're working through these issues, just as every other analyst firm is, as we go, in the context of our own evolving business model). As I mentioned, I was simply one Forrester analyst asked to share his thoughts.

Jim

BI craves cheap horsepower

All:

Analytic databases are the principal engines driving business intelligence (BI), delivering operational data into reports, dashboards, and ad-hoc queries.

Essential as they may be, analytic databases have been largely overlooked in the BI industry’s recent consolidation spree. Sitting at the core of data warehouses (DWs) everywhere, these data stores have been treated as mere plumbing rather than as differentiating platform components. Instead, most recent BI mergers have been driven by vendors’ desire to beef up their financial analytic applications, or add more sophisticated visualization, search, and other access-oriented features to their BI platforms.

Though often taken for granted, analytic databases will almost certainly become a key BI solution differentiator over the next several years. With the trend toward commoditization of core BI features, more vendors will distinguish their offerings through the speed, scalability, throughput, and mixed-workload support that only a well-tuned analytic database can provide. Every self-respecting BI vendor will boast that their analytic database can handle more concurrent users, process more complex multidimensional queries, load bulk data more rapidly, execute more compute-intensive transforms, and manage more massive data sets than the competition. Just as important, they’ll brag that they can do all this more cheaply than the next guy.

In an increasingly commoditized BI market, analytic price-performance is becoming the principal buying criterion. This trend is fueling the industry’s growing focus on analytic appliances, which are also called BI appliances or data warehousing (DW) appliances. Indeed, most of the leading BI vendors--SAP/Business Objects, IBM/Cognos, Oracle, Microsoft, and SAS Institute--provide their own analytic appliances now, or are developing appliance-based offerings on their own or with partners. Though these vendors will continue to deliver BI/DW solutions as packaged software offerings, they all see the appeal of appliances as turnkey solutions for many customer requirements. Midmarket customers, in particular, are taking a keen interest in appliances, which provide them with quick-deployment pre-optimized solutions and thereby relieve the burden on their limited technical staffs.

As analytic appliances become central to enterprises’ BI strategies, DW appliances will evolve into full-fledged BI platforms in their own right. Appliance vendors such as Teradata, HP, Netezza, Greenplum, DATAllegro, Dataupia, and ParAccel will expand their ability to run “in-database analytics” and other applications developed in-house, or by partners and customers. Appliance vendors will outdo each other in tuning database features--such as indexing, partitioning, in-memory caching, compression, cubing, tokenization, and query-plan optimization--that are geared for managing myriad analytic workloads. And every appliance vendor will beef up their hardware’s scalability through massively parallel processing, clustering, workload management, and other ongoing enhancements.

In addition, every vendor of column-oriented databases--which are exquisitely well-suited to data-intensive query processing--will soon either realign its go-to-market strategy around appliances or get out of the analytics market altogether. The performance advantages of a hardware-optimized column-oriented database over software-only rivals will be too pronounced for the latter to hold onto their market share. And though most appliance vendors currently eschew column-oriented approaches, preferring to tweak traditional row-oriented RDBMSs for multidimensional online analytical processing (OLAP), many will explore this alternative technique in order to eke out further performance improvements.

The growing demand for cheap analytic horsepower will also foster the development of subscription-based DW services, also known as “DW 2.0,” “Database 2.0,” “cloud databases,” and “on-demand databases.” Though not the first entrant in this new arena, Microsoft is the most prominent, having recently rolled out a limited beta of its hosted SQL Server Data Services (SSDS), which is slated for full production release in 2009. Under SSDS, Microsoft hosts a subset of SQL Server’s relational database management system (RDBMS) functionality in support of analytics as well as transactional applications. Though it has not yet specifically optimized SSDS for analytics, Microsoft has stated that it plans to evolve the service in that direction.

As it becomes available from many service providers, DW 2.0 will offer an ever-expanding supply of cheap, plentiful analytic horsepower. Over the coming decade, software-as-a-service (SaaS) providers will begin to offer feature-complete, subscription-based BI/DW services for high-performance, high-volume, complex analytics. These clouds will leverage the full virtualized, distributed, scalable, grid-computing fabric that Microsoft, Google, and other SaaS behemoths can bring to bear on data mining, performance optimization, and other compute- and data-intensive tasks.

Over time, we’ll come to take DW 2.0 for granted. We’ll call it up on demand, a utility for processing any and all decision-support tasks, large or small, throughout the business world or in our daily lives.

Jim