BI, Analytics, And CEP: Some Fruitful Potential Follow-Ons From Software AG’s Acquisition Of IDS Scheer

 by James Kobielus

Yes, of course, Software AG is buying IDS Scheer primarily for the latter’s ARIS family of business process management (BPM) tools. I’ll leave it to my Forrester colleagues who focus on BPM--on both the IT and TI sides of the house--to call out the ramifications for Software AG’s positioning in that market.

But, believe it or not, this deal will also launch Software AG into the growing markets for business intelligence (BI), analytics, and complex event processing (CEP) solutions. We bet you didn’t realize that IDS Scheer has ARIS solutions in these fast growing markets, but in fact they do--and they’re continue to evolve those offerings.

It’s no surprise that IDS Scheer’s BI, analytics, and CEP offerings supplement and extend its BPM portfolio. Its CEP solution, ARIS Process Event Monitor, supports business activity monitoring (BAM). Its analytics offerings, ARIS Process Performance Management and ARIS Performance Dashboard, support visualization, dashboarding, scorecarding, drilldown, and alerting on process key performance indicators (KPIs), both historical and real-time. And its forthcoming BI offering, ARIS MashZone, will support self-service user development of reports, dashboards, and other views of process and business metrics.

IDS Scheer has little market share in these non-core segments. And the vendor is no immediate threat, by itself or under its future corporate parent, to the leaders in the BI, analytics, and CEP segments. Indeed, its forthcoming mashup-oriented BI offering only provides a subset of the features available from market leaders such as SAP Business Objects, IBM Cognos, and MicroStrategy. But the fact that Software AG will soon be able to provide its own offerings in those segments, rather than rely wholly on partners, represents an important step in its attempt to field a full service oriented architecture (SOA) solution stack.

As noted in a blog entry a year and a half ago, BI is the crown jewel in any comprehensive SOA solution portfolio. SOA suites cannot be considered feature-complete unless they incorporate a comprehensive range of BI features. This acquisition continues the ongoing SOA solution build-out strategy that motivated Software AG to acquire webMethods in 2007.

But it’s not clear yet whether Software AG plans to flesh out its BI, analytics, and CEP strategies going forward and thereby confront SAP, Oracle, IBM, Microsoft, and other SOA full-stack vendors head-on in these segments. It is also unclear how much effort or expense Software AG would incur in extricating the IDS Scheer offerings from the larger ARIS portfolio in order to make them more general-purpose and less BPM-centric. Nevertheless, Software AG will at the very least have a strong set of enabling technologies to support any such strategy in the near future.

What’s most exciting, and potentially differentiating, about the Software AG/IDS Scheer BI portfolio is the combination of CEP with mashup and an in-memory architecture to support truly real-time, interactive analytics. In other words, Software AG/IDS Scheer could take a page out of the book of another SOA full-stack vendor: TIBCO and its Spotfire product group. In doing so, Software AG/IDS Scheer would also be well-positioned to duke it out with SAP, IBM, Microsoft, and Oracle, all of which are beginning to emphasize in-memory CEP-enabled BI strategies. As we noted in a report from late 2008, in-memory architectures are coming to dominate the BI arena. Likewise, Forrester has called attention in to the growing adoption of CEP for truly real-time BI.

Whether Software AG capitalizes on the opportunity to expand its SOA solution stack into BI remains to be seen. Considering that it took Oracle more than a year to publicly declare how it will position BEA’s CEP and data federation technologies within its own SOA stack, we may have to wait a while before Software AG and IDS Scheer craft an equivalent roadmap--if they ever do.

But if they wait too long, the newly merging vendors may find that the dynamic SOA, BI, and CEP markets have passed them by.

BI Mashup Maturity Model? Oxymoron? Au Contraire Mon Frère!

By James Kobielus

In one of my recent tweets, I commented that Forrester has developed a maturity model for enterprise adoption of mashup-style, self-service development of business intelligence (BI) applications. Indeed, we have, and it will appear in my forthcoming Forrester report, “Mighty Mashups: Do-It-Yourself Business Intelligence for the New Economy.”

Another tweeter--an astute, but sadly, non-Forrester BI analyst--scoffed that “BI mashup maturity model” is an oxymoron. Respectfully, I must disagree. Enterprises are adopting self-service BI approaches for many reasons--principally, to cut costs in a tight economy, to unclog the development backlog, and to speed delivery of actionable, targeted intelligence to decision makers. Also, companies are providing users with BI tools to do interactive, deeply dimensional exploration of information pulled from enterprise data warehouses (EDW), marts, cubes, transactional applications, and other systems. Furthermore, organizations everywhere have adopted browser-oriented BI environments that leverage the full Web 2.0 interactivity and collaboration.

Sitting at the convergence of those trends is BI mashup, which Forrester sees as the new paradigm for truly pervasive decision-support systems. What throws off some people is the term “mashup,” which sometimes gets pigeonholed as simply referring to using, say, Google Maps to display geocoded performance metrics and sundry Internet-sourced data in a browser-based dashboard. Yes, BI mashup encompasses that approach to presenting and integrating diverse data, but its application is much broader.

Just as important, BI mashup is not bleeding-edge. Rather, BI mashup leverages the in-memory BI clients, semantic virtualization layers, data federation middleware, automated data discovery, and other next-generation BI tools and platforms.

No one vendor or user has yet put together an end-to-end BI environment that is entirely focused on mashup-style self-service development. However, Forrester sees the BI industry converging toward as mashup-oriented architecture over the coming 2-3 years. With that in mind, we sketched out a BI maturity model that encompasses the following four levels (the first 3 of which are represented in case studies in the upcoming report):

• Level 1: Lightweight presentation mashup against transactional applications: This basic maturity level is for companies that have no prior BI or EDW; have little in-house BI expertise; and are comfortable with allowing casual users to use their browsers to customize parameterized reports from data from packaged business applications.                                                                
• Level 2: Deep presentation mashup against EDW: This level is for organization that do have prior BI and centralized EDWs, but have an understaffed BI development group and/or  power users and data modelers urgently require the ability to mashup and explore historical and current data within sophisticated BI workspaces.
• Level 3: Full BI mashup in federated environment: This level is for organizations that have decentralized, dynamic data management environments, and have the expertise to design reusable, composite data services to seamlessly mashup internal and external information.
• Level 4: Full collaborative mashup with IT governance: This level is for organizations that want to encourage subject  matter experts and operational users to collaborate on analytics created through mashup, but who are also concerned that all mashups be controlled, governed, and monitored in accordance with enterprise policies and best practices.

As I said, it will take a few years before we see a substantial number of enterprise case studies that implement the pinnacle of collaborative mashup with tight governance. Nevertheless, when you follow the evolution of next-generation solution portfolios from leading BI vendors such as SAP, IBM, Microsoft, and others, it’s clear that self-service user-centric mashup, to varying degrees, is a core theme.

BI mashup has such a strong business case that we’re confident it’s more than simply a “down economy” theme. It will almost certainly grow in importance for information and knowledge management professionals as the economy improves.

Database Religions Dissolve Into The Big Billowing Virtual Data Cloud

By James Kobielus

Virtualization is a venerable old computing concept that has achieved new life in recent years.

Virtualization brings to life a new world of more flexible service provisioning while cleverly emulating the old world that is being replaced. Virtualization refers to any approach that abstracts the external interface from the internal implementation of some service, functionality, or other resource.

The promise of virtualization is that, no matter how scattered and diverse, all pooled resources behave as if they were a single unified resource, both for usage and administration. In a sense, this is the practical magic that Arthur C. Clarke identified with advanced technology. The external interface may conceal various facts about the implementations of the underlying resources. The virtualized resources may:

•    run on diverse operating and application platforms;
•    have been deployed on nodes in diverse locations;
•    have been aggregated across diverse hosting platforms (or partitioned within a single hosting platform, either through virtual machine software, separate CPUs, or separate blade servers); and have been provisioned dynamically in response to a client request.

When Noel Yuhanna and I presented on enterprise database virtualization last week at Forrester IT Forum, we took pains to point out that is not a radically new paradigm. In fact, database administrators (DBAs) have been doing virtualization for a long time and not realizing it. We’re all familiar with such database virtualization approaches as policy-based server clustering, massive parallel processing database grids, and enterprise information integration. In these environments, you can identify the virtualization layer as “single system image,” “semantic abstraction,” or some other approach.

What all these approaches share is that they make two or more repositories behave as if they were a single database for unified access, query, reporting, predictive analytics, and other applications. If you wish, I could drill down further into the layers of database virtualization—data virtualization, transaction virtualization, and platform virtualization—but that would be too much for a mere blog post.

One twist that I didn’t have time to explore in depth last week is the notion that the traditional hub-and-spoke enterprise data warehousing (EDW) architecture is itself a form of database virtualization. The hub-and-spoke model transforms analytic data to a common “spoke-side” semantic access model, such as star schema or columnar. As such, this approach abstracts from the data models (usually 3NF relational) implemented at the EDW hub tier, the staging tier (perhaps file-based), and OLTP sources (perhaps hierarchical, XML, or what have you).

When you realize that each data-persistence approach has its optimal deployment sphere, you’re thinking database virtualization. At that point, you start to realize that the various database religions—relational is supreme, columnar is king, and so forth—are not absolute truths. They’re simply sectarian texts in a tradition of longer vintage: the evolution of truly all-encompassing data virtualization clouds.

Yes, I’m using “cloud” in this context because it best describes this new paradigm. Cloud-based virtualization is beginning to seep into analytic infrastructures. To support flexible mixed-workload analytics, the EDW, over the coming five to 10 years, will evolve into a virtualized, cloud-based, and supremely scalable distributed platform.

What are the outlines of this new paradigm? The virtualized EDW will allow data to be transparently persisted in diverse physical and logical formats to an abstract, seamless grid of interconnected memory and disk resources and to be delivered with sub-second delay to consuming applications. EDW application service levels will be ensured through an end-to-end, policy-driven, latency-agile, distributed-caching and dynamic query-optimization memory grid, within an information-as-a-service (IaaS) environment. Analytic applications will migrate to the EDW platform and leverage its full parallel-processing, partitioning, scalability, and optimization functionality. At the same time, DBAs will need to make sure that cloud-based DW offerings meet their organizations’ most stringent security, performance, availability, and other service-level requirements.

I won’t opine here and now on how much enterprise data will be persisted in public clouds vs. private environments that incorporate many of the same platform virtualization technologies. I’ll save that discussion for the upcoming Forrester reports that Noel and I are developing in virtualization of transactional and analytic databases, respectively.

Expect those in Q3 or thereabouts. Thanks everybody who attended our preso last week in Vegas!

Self-Service Business Intelligence Depends on Automated Data Discovery

 By James Kobielus

If you tuned into my Forrester teleconference yesterday, you heard me discuss the end-to-end infrastructure necessary to fully support mashup-style self-service business intelligence (BI).

One of the key features for BI mashup is automated source-data discovery, which spares information workers from having to find new data sources or fresh updates from existing sources. Instead, the user simply relies on the BI and back-end data virtualization infrastructure to perform these critical activities as ongoing background tasks. Once new sources and feeds are discovered, transformed to a common semantic model, and published to a BI-mashup registry, all the user needs to do is drag and drop them visually into their mashed-up reports, dashboards, and other analytics.

Automated discovery is not only key to BI mashup, but to trustworthy data as well, because it helps detect and remediate anomalies across disparate data sources. Only a few vendors on the market today provide strong features for automated source discovery. One of them is Composite Software, which recently released an appliance that performs these functions. Another is Exeros, which is the closest thing to an automated-data-discovery pure-play in the market today.

Or, rather, was the closest thing, until IBM announced this morning that it is acquiring Exeros. I’ve been following Exeros for several years and have long considered them a strong candidate for acquisition by a leading BI, data warehousing (DW), data integration (DI), or data quality (DQ) vendor. On IBM’s part, this acquisition makes great sense as a complement to its InfoSphere and Optim portfolios on the data management and governance side of the house.

It will also fit nicely with IBM’s Cognos portfolio as a key enabler, potentially, for BI self-service mashup. As I stated on my teleconference, some vendors are further ahead on putting together a completely mashup-enabling end-to-end BI solution, and Cognos is among them. You can download the teleconference slides from Forrester’s website, listen to my streaming audio, and/or wait for my forthcoming report for more in-depth thoughts on this topic.

Now the ball’s in IBM’s rivals’ courts regarding whether, when, and how they plan to add automated source discovery to their BI portfolios.

Inmon’s Vitriolic Slap At “Virtual Data Warehousing” Does Not Withstand Scrutiny

By James Kobielus

In a recent article, Bill Inmon incinerates a straw man concept that he refers to as “virtual data warehousing (DW).” For those unfamiliar with Inmon, he is generally considered the founder of DW as a data management discipline, has been at it since the 70s, and has more published books and articles to his name than most mortals. So he clearly may be considered an authority on the topic of DW.

But methinks Mr. Inmon doth protest too much on this “virtual DW” bugaboo, however defined (we’ll get to that in a moment). Also, he attacks this concocted notion with such emotional vehemence that it’s clear he considers it a threat to the centralized EDW paradigm upon which he has built his career and reputation.

For starters, his definition of this concept is oddly vague and questionably narrow: “a virtual data warehouse occurs when a query runs around to a lot of databases and does a distributed query.” Essentially, Inmon defines “virtual DW” as the ability to a) farm out a query to be serviced in parallel by two or more distributed databases, b) aggregate and join results from those databases, and c) deliver a unified result set to the requester.

That’s an important query pattern, but not the only one that should be supported under (pick your quasi-synonym) data federation, data virtualization, or enterprise information integration (EII) architectures. Inmon’s definition excludes the many federated queries that may only hit on a single database, with no joins and results aggregation, and with the EII fabric handling the necessary on-demand transformation from that source’s schema to an abstract semantic model.

Per my data federation report from last fall, Forrester has a broader perspective on the topic than does Mr. Inmon. Data federation is any on-demand approach that queries information objects from one or more sources; applies various integration functions to the results; maps the results to a source-agnostic semantic-abstraction model; and delivers the results to requesters. Nothing in the scoping of data federation necessarily requires the multi-source aggregation and joining that Inmon puts at the heart of “virtual DW.”

Putting Inmon’s narrow scoping of “virtual DW” behind us for the moment, let’s consider his chief objections to this approach. First, it requires the “analyst to integrate data” (as if that’s something analysts are ill-suited for or regard as some inordinate burden). Second, it consumes resources, experiences suboptimal performance, and “shuffles a lot of data around the system that otherwise would not need to be moved” (as if centralized DWs don’t consume resources, experience performance bottlenecks, and move data). Third, it is “limited to the [historical] data found in the [source] databases.” Fourth, it suffers from “no reconcilability of data...[hence] no single version of the truth for the corporation.”

It’s a fairly straightforward matter to dispatch these objections:

First, data integration--through ETL, EII, and other approaches--is a core job function for DW professionals, not some alien function outside their core competency.

Second, data federation is often the optimal approach for low-latency BI (just check out the case studies in my data federation and really urgent analytics reports). Federated environments can be tuned to provide top-notch performance and minimize source-system impacts when “shuffling” data around in a decentralized fabric.

Third, the source databases in a federation environment often include DWs, which, per their core function, usually manage a considerable amount of historical data. Once again, see my data federation report with discussion of case studies for a) Federation of Local DWs via Centralized EII Infrastructure and b) Federation of Dispersed EDW and ODS Data Into Siloed BI Environments.

Fourth, data federation is not totally incompatible with data reconciliation. In fact, federation environments can be architected for single version of the truth, data governance, and master data management. However, it can indeed be tricky to manage data quality in federated environments (see Rob Karel’s coverage of MDM and DQ for a deep dive on that issue).

My basic objection to Inmon’s line of discussion is that he treats data federation as mutually exclusive from the enterprise DW (EDW), when in fact they are highly complementary approaches, not just in theory but in real-world deployments. Yes, data federation can be deployed as an alternative to traditional EDWs, providing direct interactive access to online transactional processing (OLTP) data stores. However, data federation can also coexist with, extend, virtualize, and enrich EDWs, as well as other data-persistence nodes such operational data stores (ODS) and online analytical processing (OLAP) data marts. The case studies in the cited reports bear that out.

Inmon’s arguments are worth consideration. The centralized EDW model he touts is useful for illuminating some traditional best practices. But by no means can it do justice to the stubbornly heterogeneous, distributed, mixed-latency BI and DW requirements of most enterprises.

After So Many Years Of Ballyhoo, Semantic Web Still Searching For Killer App

By James Kobielus

Cynics might call Semantic Web a technology looking for a solution. And they might have a point.

Semantic Web refers to a long-running World Wide Web Consortium (W3C) initiative that is working toward an ambitious--some might say hopelessly Utopian--goal. At heart, it is a vision for how the World Wide Web should evolve to realize its full interoperability potential.

People vary widely in how they interpret the scope of the Semantic Web initiative. The tech industries are swarming with a wide range of projects, products, and tools that implement different variants of this vision. What vision is that? In the broadest sense, Semantic Web refers to an all-encompassing metadata, description, and policy layer that can, potentially, support universal, automatic, comprehensive end-to-end interoperability across every macro or micro entity—including data, components, services, applications, and services—on every conceivable level.

Whew!!! If that’s not the working definition of “pie in the sky” or “boil the ocean” (pick your metaphor), I don’t know what is. In fact, I’m hard-pressed to refer to Semantic Web as a definable market or solution segment. However, it’s not entirely vacuous.

For starters, organizations can implement W3C-developed semantic description standards—such as Resource Description Framework (RDF) and Web Ontology Language (OWL)--to make the meaning of content unambiguously comprehensible to services, applications, bots, and other automated components. Second, there is a reasonably robust market for “ontology” tools to support RDF/OWL-based modeling of application semantics. Finally, there is some incremental adoption of these tools and concepts in established IT segments, such as:

• Enterprise content management (ECM): Semantic approaches can support more powerful discovery, indexing, search, classification, commentary, and navigation across heterogeneous stores of unstructured and semi-structured content. Semantic search—driven by concepts, not mere text strings--is regarded by some as a primary Semantic Web application. Indeed, many Semantic Web vendors are primarily implementing the technology in search engines that leverage ontology-based concepts to improve search accuracy and reduce spurious hits.

• Enterprise information integration (EII):Semantic approaches enable consolidated viewing, query, and update of structured data that has been retrieved from diverse sources. Indeed, most commercial EII environments present an abstract semantic layer that mediates access to heterogeneous data, such as enterprise resource planning and customer relationship management applications, converging it all to a common presentation-side schema. A handful of those EII vendors have begun to support Semantic Web standards, primarily through third-party software plug-ins

• Enterprise service bus (ESB):Semantic approaches can facilitate multilayered application, process, and service interoperability across disparate environments. To date, there has been little production implementation of Semantic Web standards in the ESB arena, though some vendors have adopted semantics, ontologies, and RDF to describe the conceptual models implemented by application endpoints, agents, and intermediary nodes within ESB-like middleware approaches such as event stream processing.

But Semantic Web approaches are still on the periphery of these markets. 10+ years into its inception, Semantic Web still has no clear killer app. It’s not clear if or when that app will emerge.

Lean Information Management Strategies for Lean Times

By James Kobielus

When the going gets tough, the tough get lean, focused, and flexible. To help organizations survive the bad times and thrive in all climates, their information management initiatives must remain agile and adaptable.

If you feel your information management strategy is anything but lean, you’re not alone. Many organizations struggle to gain control over information infrastructures that have become too bloated, rigid, and slow to realign with new business drivers.

Lean information management practices are essential for corporate survival. They are far more than belt-tightening exercises. They also help you build analytic muscle for excelling in any business environment. Here are some basic pointers for keeping your information management strategy lean:

• Trim your information infrastructure of excess cost. Lean means you should cut excessive, budget-busting overhead from your information management environment. Careful cuts are best, because they optimize your existing operations without gutting the core information, analytics, and applications that underpin your core competencies. Silo, server, database, and application consolidation should be your principal approaches. Also, you should re-evaluate vendor-sourcing strategies and renegotiate licenses at more favorable terms. And you should investigate lower-cost alternatives, such as software-as-a-service, to address business intelligence, business performance solutions, enterprise data warehousing, master data management, enterprise content management, and other information management requirements.
• Fit information initiatives to key business imperatives. Lean also means you fit, focus, and fully align your information management initiatives to mission-critical business imperatives. Strategic alignment ensures that you leverage information assets across diverse application domains and business processes, rather than allow that intelligence to languish underutilized in silos. To sustain this approach, you should establish an information management framework, such as a Business Intelligence Solution Center, that enables ongoing collaboration between business and IT stakeholders. You should engage all key business and technical groups in information management planning discussions.
• Flex information architectures to changing circumstances. Finally, lean means maintaining an approach that is flexible and adaptable, able to shift course as your needs and environment change. In yoga terms, lean is all about building, toning, and stretching analytical muscle to keep it from tearing when you need to transition rapidly from one strategic alignment to the next. You need the flexibility to swing between centralized information management infrastructures and decentralized or federated environments. For end-to-end data management environments, Forrester has developed an architecture decision support tool that helps information managers to determine which of several topologies is best suited to their needs: centralized enterprise data warehouse, hub-and-spoke, independent data marts, data federation, and information-as-a-service.

Considered as a comprehensive strategy, these lean practices are true bloat-busters and recession-beaters. They allow organizations to deliver practical insights that address all pain points, even--especially!!!--within strict budgets.

Governance Risk Compliance Agenda....Critical in Turbulent Economy, But Conspicuously Missing from IBM’s IOD Go-To-Market Message

By James Kobielus

If it’s October, it must be time for IBM’s annual Information On Demand (IOD) conference. Initiated 3 years ago, IOD has become an indispensable event for any Information and Knowledge Management (I&KM) professional who has deployed IBM’s sprawling data management solution portfolio.

And IBM doesn’t disappoint: each annual conference is jampacked with important announcements that improve the vendor’s positioning in the forefront of today’s information-driven economy. If anything, IOD has become so crowded with IBM announcements that some important events or themes can easily be overlooked or given less emphasis than they deserve.

IBM realizes this, which is why the vendor works hard in advance of the show to define a coherent set of themes that not only address key customer requirements but also tie to key new product initiatives or releases. This year, that overarching theme is “Information Agenda,” which, you’ll notice, I blogged on several weeks ago. At heart, Information Agenda refers to IBM’s IOD solution focus: positioning its offerings as key customer enablers for business agility, transformation, optimization, and efficiency.

It’s a great theme: very empowering, hopeful, and solution-focused. But, sitting here at IOD, it occurs to me that another key theme is looming in the background, threatening to eclipse it all. The approaching storm is the worldwide financial meltdown, economic slowdown, and the very real likelihood of sharp cuts in IT budgets everywhere. It manifests itself in the increased economic volatility and risk we all face, and for which we’re all trying to hedge all of our strategies and plans. It’s clamping down on us in the increasing government regulation, control, and monitoring being imposed on a growing swath of the world economy. It will result in government-mandated deployment of governance, risk, and compliance (GRC) solutions, which will need to leverage companies’ investments in business intelligence (BI), performance management, and data warehousing (DW) solutions.

Oddly, IBM paid precious little attention to GRC at this year’s IOD--though its Cognos group rolled out  enhancements to its financial performance analytics portfolio,  its enterprise content management (ECM) portfolio enhanced its e-discovery and records management features, and it deepened its already comprehensive Optim portfolio for information lifecycle management (ILM).

And it’s not as if IBM has no GRC strategy. On previous occasions, IBM has put forth a credible strategy that ties its ECM, identity management, and other offerings into a portfolio that is roughly on a function par with Oracle, SAP, and other diversified software vendor.

But not at this year’s IOD. To IBM’s credit, many of their execs discussed the economic climate soberly and insightfully. But nobody, it seemed, wanted to rain on the IOD parade--in which the sunny “business optimization” theme prevailed--by bringing the teeth-gritting GRC theme into it in any way.

My primary beef with Information Agenda? As noted in that previous post, it’s a bit too vague, and a tad too blandly optimistic. Yes, there are always business opportunities, but we’re now in a business landscape where the threats are even more salient.

So, IBM, let’s also discuss customers’ Governance Agendas....their Risk Agendas...their Compliance Agendas...their Demonstrate-to-the-Feds-That-Our-Books-Are-Shipshape Agendas...their Son-of-SarbOx Agendas...their Survive-and-Avoid-Government-Takeover Agendas. How does the IOD portfolio support those agendas?

The big bad recession. Everybody from Ben Bernanke on down says it’s here or coming soon. So why not address it head-on, soberly, with solutions, or, at the very least, approaches that leverage solutions in which customers have already invested?

On a related note, IBM failed to fully address another key concern in a sour economy: cutting IT costs without compromising investments in core BI, analytics, and operational data assets. On the DW appliance side of the equation, IBM did not present a strategic response to recent announcements by Oracle and Teradata, under which those competitors are now providing highly affordable petabyte-scale DW solutions. IBM has strong DW appliance offerings in its InfoSphere Balanced Warehouse portfolio, but it had no new announcements that match Oracle’s and Teradata’s big splashes.

So, if anything, those were the prime disappointments at this year’s IOD show. It was a missed opportunity for IBM both to counter the competition and also address a key concern of its budget- and survival-stressed I&KM customers.

Agenda Politics -- Information Shifts The Balance Of Policy And Influence In Any Organization

By James Kobielus

Yes, like anyone who got a liberal arts degree (me: B.A., Economics), I had to take Political Science 101. And like anyone who sat and thought about what exactly politics is, I soon realized that it's anything but a science. Some call it the "art of the possible," and that strikes me as exactly right.

Or, more to the point, it's the art of engineering consensus and coalition around issues, leading (hopefully) to effective action. Which brings me to the one useful kernel of wisdom that I took away from Poli Sci 101: that the most effective coalition builders are those who engineer a clear, compelling agenda for shaping collective action over the long run. Ironically, Al Gore may have had a greater, lasting impact on the world by losing the 2000 election than if the U.S. Electoral College, Supreme Court, and Floridian perforations had all swung his way. He spearheaded a powerful agenda-based coalition of considerable momentum, focusing the human race on our collective responsibility for global warming. And his chief tool was information: an "inconvenient" but totally science-based truth, reflecting the overwhelming consensus of those who study climate change for a living.

Clearly, no political agenda can succeed in the long run without a potent information agenda -- or, at least, people who are adept at using all available channels to build consensus and spur action. Honestly, when IBM started to use the term "Information Agenda" in their go-to-market messaging, I wasn't sure if I agreed with what they were doing. I understood the notion of, say, a "business optimization," "agility," or "green" agenda, because those terms point (albeit vaguely) to desired results. But information is a tool for building and maintaining an agenda -- it's not an end in its own right.

But then I realized that's not entirely true. Information technology is a precious corporate resource, as are the business intelligence and performance management applications that flow through those channels. So, in that very important sense, an "Information Agenda" makes great sense. Every organization -- public or private sector -- must build and sustain a strong IT, BI, analytics, and performance management capability. Sometimes those assets are wielded for corporate transformation or optimization (by Al Gores of the corporate world). And sometimes -- usually -- they're in the hands of grassroots personnel, who are simply trying to keep their organizations humming smoothly and on an even keel (hopefully, the next U.S.president can keep this big bruised ship of ours from capsizing).

Of course, we all recognize that actionable intelligence is fundamental. Every organization's Information Agenda must revolve around the need to keep that intelligence trustworthy, current, and relevant. So that all decisions -- no matter how humdrum and mundane -- may be grounded solidly in unimpeachable truth.

No matter how inconvenient.

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William Band
Research coverage for Business Process & Applications professionals
Customer relationship management applications, customer experience management, stakeholder alignment, enterprise CRM
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Matthew Brown
Research coverage for Information & Knowledge Management professionals
Marketing and advertising, enterprise portals, intranets and extranets, information and knowledge management
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Peter Burris
Research coverage for Technology Product Management & Marketing professionals
Enterprise marketing platforms, marketing automation, high-tech, application development
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Bobby Cameron
Research coverage for CIOs
IT governance, risk, and compliance; the marketing of IT; serving the business; security and risk
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Marc Cecere
Research coverage for CIOs
Designing IT organizations, changing the culture of an IT organization, IT strategic planning
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Patrick M. Connaughton
Research coverage for Business Process & Applications professionals
Supply chain management services, supply chain management applications, enterprise mobility, RFID
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Alex Cullen
Research coverage for CIOs
IT organization; IT strategy, planning, and governance; organizational design and change management, IT management
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Boris Evelson
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, business intelligence, OLAP, data warehousing
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Chip Gliedman
Research coverage for Business Process & Applications professionals
Customer relationship management, help desk/service desk, customer service and support, packaged applications
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Paul D. Hamerman
Research coverage for Business Process & Applications professionals
ERP, human capital management, financial management, business performance solutions
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Brian W. Hill
Research coverage for Information & Knowledge Management professionals
eDiscovery, archiving, records and retention management, enterprise content management (ECM)
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Bradford J. Holmes
Research coverage for Vendor Strategy professionals
Tech marketing tools and best practices; government, high-tech, tech marketing strategies
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Rob Karel
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, integration technologies, metadata management, extract
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Rob Koplowitz
Research coverage for Information & Knowledge Management professionals
Information Workplace, collaboration strategy, collaborative platforms, SharePoint
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George Lawrie
Research coverage for Business Process & Applications professionals
Retail information technology; consumer goods supply chain; pricing, promotions, and revenue optimization; collaborative processes such as trade promotions management and sales; and operations planning
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Sharyn Leaver
Research coverage for Business Process & Applications professionals
Packaged applications, business process management, ERP, application strategy and selection
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Craig Le Clair
Research coverage for Information & Knowledge Management professionals
ECM, BPM, output management, document processing services
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Pete Marston
Research coverage for Business Process & Applications professionals
Customer relationship management, sales force management, software-as-a-service, outsourcing
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Kyle McNabb
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, document imaging, eForms and information capture, enterprise content management
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Thomas Mendel, Ph.D.
Research coverage for Vendor Strategy professionals
Product portfolio strategies, mobile services, business service management, data center management
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Connie Moore
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, business process optimization, IT organization, enterprise content management
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Leslie Owens
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, taxonomy and classification, enterprise search platforms, text mining and analytics
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Natalie L. Petouhoff, Ph.D.
Research coverage for Business Process & Applications professionals
Customer service and support, customer experience, customer experience management, business strategy for customer experience
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Lisa Pierce
Research coverage for IT Infrastructure & Operations professionals
Voice services, telecommunications services by region, remote access infrastructure, networking
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Tom Pohlmann
Research coverage for CIOs
Business models, high-tech, corporate strategy, tech sector economics, product and solutions strategies
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Stephen Powers
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, digital asset management, enterprise content management, Web content management
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Stefan Ried, Ph.D.
Research coverage for Vendor Strategy professionals
Enterprise architecture, Service-oriented architecture, application platforms and programming strategy; application development
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Ted Schadler
Research coverage for Information & Knowledge Management professionals
Real-time collaboration tools (instant messaging, presence, document sharing, etc.), cloud-based collaboration and email, mobile collaboration tools and applications, virtual worlds for the enterprise
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Claire Schooley
Research coverage for Information & Knowledge Management professionals
eLearning, information and knowledge management, videoconferencing, Web conferencing, enterprise collaboration, new workforce, retiring workforce
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Scott Tiazkun
Research coverage for Business Process & Applications professionals
Financial management; governance, risk, and compliance; financial management applications; security and risk
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Zach Thomas
Research coverage for Business Process & Applications professionals
Human resources management applications, compensation, recruitment strategies, packaged applications
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Tim Walters, Ph.D.
Research coverage for Information & Knowledge Management professionals
Web content management, enterprise content management, digital asset management, information and knowledge management
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R "Ray" Wang
Research coverage for Business Process & Applications professionals
Enterprise apps and ERP, software contract negotiations, software partnerships and ecosystems, customer data integration
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Doug Washburn
Research coverage for IT Infrastructure & Operations professionals
Green IT, IT organization, IT infrastructure and operations, IT management
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Gil Yehuda
Research coverage for Information & Knowledge Management professionals
Enterprise Web 2.0 and Social Computing; collaboration strategy, tools, and culture; virtual communities of practice; virtual team collaboration
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