Instrumenting Your Enterprise for Maximum Predictive Power

By James Kobielus

Business is all about placing bets and knowing if the odds are in your favor.

As I noted in my most recent Forrester report, business success depends on your company being able to visualize likely futures and take appropriate actions as soon as possible. You must be able to predict future scenarios well enough to prepare plans and deploy resources so that you can seize opportunities, neutralize threats, and mitigate risks.

Clearly, predictive analytics can play a pivotal role in the day-to-day operation of your business. It can help you focus strategy and continually tweak plans based on actual performance and likely future scenarios. And, as I noted in a recent Forrester blog post, the technology can sit at the core of your service-oriented architecture (SOA) strategy as you embed predictive logic deeply into data warehouses, business process management platforms, complex event processing streams, and operational applications.

The grand promise of predictive analytics—still largely unrealized in most companies—is that it will become ubiquitous, guiding all decisions, transactions, and applications. For the technology to rise to that challenge, organizations must move toward a comprehensive advanced analytics strategy that integrates data mining, content analytics, and in-database analytics. Already, we’ve sketched out a vision of “Service-Oriented Analytics,” under which you break down silos among data mining and content analytics initiatives and leverage these pooled resources across all business processes.

You may agree that this is the right vision but have doubt about whether there is a practical, incremental roadmap for taking your company in that direction.  In fact there is, and it starts with re-assessing the core of most companies’ predictive analytics capability: your data mining tools. As you plan your predictive analytics initiatives, you should avoid the traditional approach of focusing on tactical, bottom-up project-specific requirements. You should also try not to shoehorn your requirements into the limited feature set of whatever modeling tool you currently happen to use.

To become a fully predictive enterprise, you will need to take both a top-down and bottom-up approach to your data mining initiatives. From the top-down, it’s all about building and integrating alternate models of how your business environment is likely to evolve internally and externally. In our recent report on advanced analytics, Boris Evelson, Leslie Owens, and I sketched out the many business processes that can be enriched by predictive analytics.

So how do you instrument your company to become more predictive? For starters, assess whether your analytics tools support the following capabilities for developing, validating, and deploying predictive models:

• Model multiple business scenarios: You should be able to build complex models of multiple, linked business scenarios across different business, process, and subject-area domains, using such key features as strategy maps, ensemble modeling , and champion-challenger modeling.
• Incorporate multiple information types into models: You should be able to develop models against multiple information types, including unstructured content and real-time event streams, while leveraging state-of-the-art algorithm in sentiment analysis and social network analysis.
• Leverage multiple statistical algorithms and approaches in models: You should be able to develop models using the widest, most sophisticated range of statistical and mathematical algorithms and approaches, including regression, constraint-based optimization, neural networks, genetic algorithms, and support vector machines.
• Apply multiple metrics of model quality and fitness: You should be able to score and validate model quality using multiple metrics and approaches, including quality scores, lift charts, goodness-of-fit charts, comparative model evaluation, and auto best-model selection.
• Employ multiple variable discovery and assessment approaches: You should be able to build and validate models using various approaches for variable discovery, profiling, and selection, including decision trees, feature selection, clustering, association rules, affinity analysis, and outlier analysis.

How is this different from predictive analytics as usual? Traditionally, most predictive modeling specialists focus on the latter three capabilities: statistical algorithms and approaches, model quality and fitness, and variable discovery assessment. Most models are built in narrowly scoped business or subject domains—such as customer analytics for marketing campaign management—and only against structured data sources (such as relational tables). Traditionally, few predictive analytics projects have entailed modeling of multiple business scenarios across diverse domains--such as sales, marketing, customer service, manufacturing, and supply chain-- though in the real world these business processes are often quite interconnected. Also, many data mining initiatives fail to incorporate information from unstructured sources—such as text in call-center logs—though this content may be as important as what comes relational databases and other structured sources.

It’s very important to build multi-scenario predictive models against complex information sets, but becoming a fully predictive enterprise demands much more. To instrument your organization for maximum predictive power, you should also tool your advanced analytics to support the following capabilities:

·         DW-integrated data preparation: To speed up and standardize the most time-consuming predictive modeling project tasks, you should be able to leverage your existing data warehouse, extract transform load, data quality, and metadata tools to support a full range of data preparation features. These features include the ability to discover, acquire, capture, profile, sample, collect, collate, aggregate, deduplicate, transform, correct, augment, and load analytical data sets.

·         Deep application and middleware integration: To deliver models deeply into whatever heterogeneous SOA-enabled platform you happen to use, your predictive analytics tool should deploy on and/or integrate with a wide range of enterprise applications, middleware, operating platforms, and hardware substrate. You should be able to deploy models seamlessly into your data warehouse, business intelligence, online analytical processing, data integration, complex event processing, data quality, master data management, and business process management environments. And to play well in your SOA, your predictive modeling tools should support application programming interfaces, languages, tools, and approaches such as Web services, Java, C++, and Visual Studio, as well as emerging languages such as SQL-MapReduce and R.

·         Consistent cross-domain model governance: To avoid fostering an unmanageable glut of myriad models, your predictive analytics solution should support a wide range of tools, features, and interfaces to support life-cycle governance of models created in diverse tools. At the very least, your tools should enable model check in/check-out, change tracking, version control, and collaborative development and validation of models. To realize this promise, it should support a full range of tools, standards, and interfaces for import and embedding of models from other tools, as well as export and sharing of models to other environments.

·         Flexible model deployment: To execute modeling functions--such as data preparation, regression, and scoring—on the widest range of data warehouses and other platforms, your tools should support in-database or embedded analytics. And to scale to the max, your predictive analytics tools should deploy models to massively parallel data warehouses, software-as-a-service environments, and cloud computing fabrics.  Your advanced analytics tools should also support development of application logic in open frameworks—such as MapReduce and Hadoop—to enable convergence of data mining and content analytics in the cloud.

·         Rich interactive visualization: To deliver their precious payload—actionable intelligence—your advanced analytics tools should support interactive visualization of models, data, and results. Ideally, you should be able to visualize all of this in your preferred business intelligence tool, or in the predictive modeling vendor’s integrated visualization layer. Of course, you have every right to expect the full range of visualization techniques--histograms, box plots, heat maps, etc.—regardless of who provides the visualization layer.

As you can see, this goes well beyond data mining as usual. Forrester has a slightly different perspective on the development of the predictive analytics market than you’re likely to get from other sources. We see a robust, flexible, SOA-enabled data mining tools as the centerpiece of advanced analytics for fully predictive enterprises. The competitive stakes are too great for businesses to take the traditional silo-mired approach when implementing this mission-critical technology.

What do you think?

Service-Oriented Analytics: Tapping into the Predictive Smarts of Your Entire Organization

By James Kobielus

Advanced analytics is a key competitive weapon of companies everywhere. Visionary organizations are those that take a future-facing, analysis-driven perspective on new challenges. They do this by grounding management forecasts in solid historical information sets, leveraging and extending companies’ existing investments in data mining and predictive modeling.

To become truly future-focused, organizations must build out their predictive muscles through deepening commitment to these and other advanced analytics technologies, which also include interactive visualization, multivariate statistical analysis, text analytics, and massively parallel enterprise data warehousing. However, enterprises must be careful not to adhere to the common practice of implementing advanced analytics tools in tactical, application-specific silos. One of the downsides of this traditional practice is that diverse predictive modeling teams can find it difficult to share their deep domain expertise, best statistical approaches, and most powerful data exploration and visualization features.

How can companies realize the transformative potential of predictive analytics for business success? For starters, you must get rid of siloes that fragment your data mining initiatives into separate camps. You must also build a bridge between your data mining operations and the teams that manage your text analytics, business intelligence, complex event processing, and business process management efforts. And the key approach for silo-smashing is service-oriented architecture (SOA).

At first glance, SOA may seem like a foreign topic to many analytics professionals, but it shouldn’t be. In the broadest perspective, SOA refers to best practices for encouraging greater reuse, sharing, and cross-platform interoperability among key business resources. Typically, one associates SOA with reuse of one specific type of resource: application functionality that is distributed across heterogeneous, networked platforms. Nevertheless, key SOA principles—such as standards-based service virtualization, reuse, brokering, and governance—are as applicable to predictive models as to any other resource that lives online.

Predictive models empower your product managers, marketing specialists, risk managers, process analysts, senior executives, and other personnel with access to sophisticated forecasting, time-series analysis, and scenario-testing tools. Each model is a statistical encapsulation of your business’ current view of the future in some specific application, subject, or decision-support area. Incorporating the expertise of subject-matter experts, these models allow organizations to gauge the potential impact of future projects, campaigns, and other initiatives, and also to adjust execution of these initiatives in mid-stream.

Predictive analytics need not be a purely blue-sky planning tool. This technology can sit at the core of your SOA strategy, and leading-edge enterprises are in fact doing that. Companies in such verticals as financial and telecommunications are embedding predictive logic deeply into data warehouses, business process management (BPM) platforms, complex event processing (CEP) streams, and operational applications.

What will it take for your company to fully align your advanced analytics efforts with your SOA strategy? Most important, Service-Oriented Analytics requires an executive-level commitment to becoming a predictive enterprise on all levels. From the perspective of your existing predictive modeling teams, this will require an ongoing focus on collaboration across business, function, and subject domains. You should create a culture and offer incentives that encourage modeling professionals to reuse each other’s expertise on problems that cross multiple domains.

Reuse is everything. Here are some high-level guidelines for establishing a reuse-friendly Service-Oriented Analytics practice in your company:

• Reuse modeling best practices:  Start by consulting Forrester’s report on setting up a Business Intelligence Solution Center (BISC), which we defined as “an institutional steward, protector, and forum for BI best practices.” Given that predictive analytics is a key segment of BI, you will find it necessary to incorporate this technology into your BISC’s scope.
• Reuse modelers: Cultivate a professional cadre of predictive modeling experts who are more than just wizards in advanced statistics and mathematics. Encourage subject-matter experts in all business areas to undertake predictive modeling projects and to team with modeling experts in other projects, applications, and business units. Provide incentives for modelers to regularly move between business units and subject domains, thereby spreading their expertise throughout the enterprise.
• Reuse models: You should begin to investigate predictive-model governance tools, which support version control, check-in/check-out, and other controls over models created in and imported from diverse tools. Model governance tools—from vendors such as SAS, SPSS, and KXEN--facilitate reuse, consolidation, combination, and cross-synthesis among disparate models. And you should also investigate options for embedding predictive models within BI, BPM, and other operational applications, thereby leveraging your growing analytic asset into new deployments.

As you deploy predictive models into operational applications, you should provide other applications with SOA-based access to them through Web services, Web 2.0, and other standardized interfaces. In that way, you will be creating a critical bridge to your application development teams and be creating a thoroughly Service-Oriented Analytics environment.

Your first steps down this road are clear. Forrester will be happy to assist you in developing a detailed enterprise roadmap for Service-Oriented Analytics. In so doing, you’ll be better able to tap into the future-facing analytic expertise that exists throughout your business.

Is BAM Relevant In The Age of Lean Processes?

By James Kobielus

In any dynamic business environment, the last thing you want is to indulge in navel-gazing. If you’re finely attuned to monitoring internal processes, but are not adept at responding to breaking events or anticipating the future, you will find yourself marginalized in the new global economy.

In a turbulent world, the most successful companies are those that stay lean--in other words, trimmed of excess overhead, fit for new challenges, and flexible enough to evolve rapidly. Forrester advises customers to make their business processes leaner, through such measures as reducing the number of steps, routing paths, participants, decision points, milestones, and revisions. As you do, you will find concomitant improvements in speed, cost, quality, and customer satisfaction.

Clearly, you can’t do that well if you don’t have, at the very least, the means to monitor a wide range of business processes in unified dashboards, reports, and other applications. That’s where traditional business activity monitoring (BAM) tools, which are a key component of most business process management (BPM) environments, play a critical role. However, critical as BAM may be, it is often deployed in a predominantly navel-gazing process-tracking capacity (remember what that “M” stands for) and, as such, is as much part of the problem as the solution.

Why? Well, for starters, most BAM environments are deployed in conjunction with specific vendors’ BPM platforms--hence can only track the subset of business processes that run on those platforms. But, even more limiting, BAM dashboards are often the stand-alone province of process administrators. Only in a handful of  BAM-using organizations do alerts and metrics escalate beyond the process administrator level and trigger the necessary human and automated workflows necessary to respond to breaking issues--or nip them in the bud before they can become issues.

To stay relevant, BAM needs to evolve in the age of lean processes so that it can become a tool for business agility at all levels. Next week, Forrester BPM analyst Clay Richardson and I will do a podcast on this very topic. In  that podcast, and in a joint presentation that Natalie Petouhoff and I will do at Forrester Business and Technology Forum in Chicago in October, I’ll sketch out a vision for the next-generation of BAM (which I half-jokingly call “uber-BAM”).

Here is a rough sketch of the maturity levels of BAM's evolution to realize this vision:

• BAM Level 1: Business activity monitoring: This is today’s BAM baseline. Essentially, it combines BPM with business intelligence (BI), interactive dashboarding, and complex event processing to deliver process metrics to consoles used solely by human process administrators.
• BAM Level 2: Business action mobilization: This is the BAM level that many forward-looking users and vendors have implemented. It combines all Level 1 functionality with business rules engines (BRE) to drive sophisticated human and automated workflows in response to breaking events. It puts the “immediate action loop” into the BAM equation.
• BAM Level 3: Business adaptability management: This is the promise of truly lean uber-BAM, and is found only in a vanguard of visionary  business environments. It combines all Level 2 functionality with embedded predictive-analytics models, which are developed and maintained by business process architects and/or subject-matter process experts. Continuously mining historical and real-time metrics, these in-process analytics drive automated “recommendation engines” that adapt coordinate response to changing conditions. Recommendation engines enable human and automated decision agents to seize opportunities and neutralize threats well ahead of the curve.

Going forward, this new vision of BAM’s evolution and promise will inform Forrester’s coverage of process agility and optimization.

IBM Goes Deeply Predictive, Announces Acquisition of SPSS

By James Kobielus

IBM dropped a big bombshell at the start of any already action-packed day for the analyst community. At this moment, I’m sitting, along with several dozen of my peers from Forrester and other firms, at the IBM Smart Analytics System launch event in Hawthorne NY. I’ll blog on IBM’s other announcements in a separate items.

The bombshell was IBM’s announcement that it’s acquiring SPSS, a long-established, leading provider of predictive analytics (PA), data mining (DM), statistical analysis, and text mining tools. The acquisition, subject to the usual shareholder approvals and regulatory reviews, is expected to close later this year. But, even in advance of that near-certain consummation, IBM’s bold move has already sent shockwaves throughout the analytics market.

Most important, IBM has acquired the second largest vendor of PA/DM solutions, dwarfed only by privately held SAS Institute. In this segment, IBM’s proposed acquisition is having the same impact that its Cognos buy had on the business intelligence (BI) market two years ago. In many discussions with Forrester customers, SPSS is often mentioned as a key solution provider for predictive modeling and statistical analysis against structured, semi-structured, and unstructured content.

For IBM’s competitive standing in the data management market, this acquisition represents one of the last missing pieces of its Information On Demand (IOD) portfolio. By acquiring SPSS, IBM has acquired a substantial PA/DM brand with a very loyal set of longtime customers who have build their customer churn, supply chain optimization, and other predictive models on its best-of-breed platform. SPSS recently underlined its feature-comprehensive value proposition through re-branding around the “Predictive Analytics Software” (PASW) family name. But longtime customers didn’t need to be reminded, of course, that what used to be known as “Clementine” defines a functional high-bar in this solution segment.

IBM would probably be the first to admit that it took its focus off the PA/DM market over the past several years as it build out the BI, data warehousing (DW), and other pieces of its IOD portfolio. IBM had never really exited the PA/DM market, but casual observers might have thought otherwise. However, the vendor three years ago chose to de-emphasize its Intelligent Miner tools--which support mining of structured data--as stand-alone offerings. It essentially buried these solutions, moving them into its InfoSphere Balanced Warehouse family, where they are now offered as features of its Enterprise Edition DW software, rather than as stand-alone tools that would be enhanced and evolved independently.

IBM and SPSS’s respective customer bases should rest assured that overlaps among their respective product portfolios are not extensive. Once the acquisition closes, IBM is almost certain to build out its SPSS brand and, over the coming 1-2 years, phase out the Intelligent Miner technology within its InfoSphere portfolio. One tricky issue is which text analytics solution family--SPSS’ or IBM’s OmniFind solutions--will prevail as the parent converges these offerings in its IOD portfolio. Another issue is how IBM will integrate the SPSS offerings into its still-evolving in-database analytics roadmap for InfoSphere Balanced Warehouse. Hopefully, IBM will maintain and extend SPSS’ already extensive in-database analytics integration with a broad range of vendor DWs, including such Big Blue rivals as Oracle, Microsoft, Sybase, and Teradata.

Who loses from IBM’s acquisition of SPSS? Fundamentally, one can’t help think that SAP missed the boat by not seizing the opportunity to acquire partner SPSS, whose Clementine technology it OEMs, has integrated with its BI technology, and sells as SAP BusinessObjects Predictive Workbench. PA/DM is an increasingly key component of a full-fledged BI solution stack. However, the remaining field of vendors with stand-alone, horizontally applicable PA/DM vendors consists primarily of vendors who are large but proudly and stubbornly independent (especially, SAS Institute); high-quality but much less widely adopted (e.g., KXEN, ThinkAnalytics); or specialized on customer, financial, scientific, or other specialized analytics (e.g., Unica, Fair Isaac, Accelrys).

Some IBM rivals in the BI space already have strong PA/DM tools, most notably Oracle (Oracle Data Miner) and TIBCO/Spotfire (the Insightful tools). Among BI vendors, Microsoft, MicroStrategy, and Information Builders have PA/DM capabilities, but they are not to a SAS or SPSS level of sophistication. These and other BI vendors should also be scouting for strategic acquisitions.

What do you think? Will IBM’s acquisition of SPSS lead to further merger and acquisition activity in this space as other leading BI players strengthen their PA/DM solutions?

By James Kobielus

IBM dropped a big bombshell at the start of any already action-packed day for the analyst community. At this moment, I’m sitting, along with several dozen of my peers from Forrester and other firms, at the IBM Smart Analytics System launch event in Hawthorne NY. I’ll blog on IBM’s other announcements in a separate items.

The bombshell was IBM’s announcement that it’s acquiring SPSS, a long-established, leading provider of predictive analytics (PA), data mining (DM), statistical analysis, and text mining tools. The acquisition, subject to the usual shareholder approvals and regulatory reviews, is expected to close later this year. But, even in advance of that near-certain consummation, IBM’s bold move has already sent shockwaves throughout the analytics market.

Most important, IBM has acquired the second largest vendor of PA/DM solutions, dwarfed only by privately held SAS Institute. In this segment, IBM’s proposed acquisition is having the same impact that its Cognos buy had on the business intelligence (BI) market two years ago. In many discussions with Forrester customers, SPSS is often mentioned as a key solution provider for predictive modeling and statistical analysis against structured, semi-structured, and unstructured content.

For IBM’s competitive standing in the data management market, this acquisition represents one of the last missing pieces of its Information On Demand (IOD) portfolio. By acquiring SPSS, IBM has acquired a substantial PA/DM brand with a very loyal set of longtime customers who have build their customer churn, supply chain optimization, and other predictive models on its best-of-breed platform. SPSS recently underlined its feature-comprehensive value proposition through re-branding around the “Predictive Analytics Software” (PASW) family name. But longtime customers didn’t need to be reminded, of course, that what used to be known as “Clementine” defines a functional high-bar in this solution segment.

IBM would probably be the first to admit that it took its focus off the PA/DM market over the past several years as it build out the BI, data warehousing (DW), and other pieces of its IOD portfolio. IBM had never really exited the PA/DM market, but casual observers might have thought otherwise. However, the vendor three years ago chose to de-emphasize its Intelligent Miner tools--which support mining of structured data--as stand-alone offerings. It essentially buried these solutions, moving them into its InfoSphere Balanced Warehouse family, where they are now offered as features of its Enterprise Edition DW software, rather than as stand-alone tools that would be enhanced and evolved independently.

IBM and SPSS’s respective customer bases should rest assured that overlaps among their respective product portfolios are not extensive. Once the acquisition closes, IBM is almost certain to build out its SPSS brand and, over the coming 1-2 years, phase out the Intelligent Miner technology within its InfoSphere portfolio. One tricky issue is which text analytics solution family--SPSS’ or IBM’s OmniFind solutions--will prevail as the parent converges these offerings in its IOD portfolio. Another issue is how IBM will integrate the SPSS offerings into its still-evolving in-database analytics roadmap for InfoSphere Balanced Warehouse. Hopefully, IBM will maintain and extend SPSS’ already extensive in-database analytics integration with a broad range of vendor DWs, including such Big Blue rivals as Oracle, Microsoft, Sybase, and Teradata.

Who loses from IBM’s acquisition of SPSS? Fundamentally, one can’t help think that SAP missed the boat by not seizing the opportunity to acquire partner SPSS, whose Clementine technology it OEMs, has integrated with its BI technology, and sells as SAP BusinessObjects Predictive Workbench. PA/DM is an increasingly key component of a full-fledged BI solution stack. However, the remaining field of vendors with stand-alone, horizontally applicable PA/DM vendors consists primarily of vendors who are large but proudly and stubbornly independent (especially, SAS Institute); high-quality but much less widely adopted (e.g., KXEN, ThinkAnalytics); or specialized on customer, financial, scientific, or other specialized analytics (e.g., Unica, Fair Isaac, Accelrys).

Some IBM rivals in the BI space already have strong PA/DM tools, most notably Oracle (Oracle Data Miner) and TIBCO/Spotfire (the Insightful tools). Among BI vendors, Microsoft, MicroStrategy, and Information Builders have PA/DM capabilities, but they are not to a SAS or SPSS level of sophistication. These and other BI vendors should also be scouting for strategic acquisitions.

What do you think? Will IBM’s acquisition of SPSS lead to further merger and acquisition activity in this space as other leading BI players strengthen their PA/DM solutions?

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.

Dislocation Intelligence In A Brutal Economy

By James Kobielus

It's painful to see the auto, newspaper, construction, financial services, and so many other formerly vibrant sectors of the world economy go down the proverbial tubes. One of the most nauseating realities is when millions of people lose their jobs, homes, and communities in a seeming blink.

I grew up in the perpetually recessionary Detroit area. I'm attuned to the regional dislocations that come from depending too much on an industry that has seen better days. Abandoned storefronts, dilapidated housing, vacant lots, tumbleweed-quiet city streets--all of it evidence of a growing ghost town, telltale signs of a marginal economy that depends on government programs, private charity, and low-wage service jobs. During my college years, I was a policy analyst with an urban coalition in downtown Detroit, and I could see that the slide was long-term and nigh irreversible.

Even in relatively well-off areas such as Washington DC, where I've spent close to a quarter-century, we’re not immune to serious economic dislocations. The National Capital Region, so reliant on federal spending, is likely to feel the brunt of whatever cuts Obama will almost certainly make to close this massive deficit. And even here you can’t escape dislocations in sectors that we all depend on, such as retailing, as evidenced by, for example, the ex-Circuit City big boxes that seem even bigger and boxier now that they’re totally empty.

In recent years, corporations have adopted location intelligence solutions to support their market-entry strategies, such as adding new stores and waging marketing campaigns. They also use these tools--essentially, geographic information systems coupled with predictive modeling--to optimize their footprint in existing markets. And to a lesser extent, they also use location intelligence to plan their exit strategy of closings and retrenchments. But when departures are hasty--such as any Chapter 11 proceeding--all we’re left with are vast tracts of vacant real estate, plus many formerly employed people who must find a way to survive amid ruins. Imagine how a General Motors or Chrysler bankruptcy will hit Detroit--it will be a liquidation to rival Hurricane Katrina in its devastation of a major city.

Dislocation intelligence is something that the leaders of the auto companies--and the Obama administration--should exercise when making the tough decisions to restructure this critical industry. People’s pain should be factored into decisions to close and relocate plants, so that, for example, whole cities--such as Flint, Toledo, and Janesville--can make a smooth exit from over-reliance on this one industry. As part of that effort, urban planners should consider the infrastructure that remains behind, so that, for example, whole regions of a city are not suddenly deprived of hospitals, grocery stores, and other basic amenities. Imagine you’re on welfare and have to somehow go 10 miles to buy a loaf of bread.

Mapping tools are just tools, not salvation. Detroit long ago passed a point of no return. The US auto industry will never recover the manufacturing jobs that attracted people from all over the world to southern Michigan, northern Ohio, and other regions.

But we as a country should try to smooth over these economic dislocations so that they don’t completely wipe some places off the map.

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.

Enterprise Mashups Need Complexity To Create Value

By Gil Yehuda
Those who drink the Web 2.0 Kool-aid live in a idealistic world where we can mentally connect a great idea to a great implementation of that idea. We live on faith that the great implementation will come, since there are plenty of smart people out there who will eventually figure out how to make value out of technology building blocks. Sometimes our faith is tested when the killer-app does not show up for a long time. But evidence can restore our faith.

When I first saw mashups, I thought they were pretty cool. The canonical examples of this technology were all about the placement of data points onto a map. With mashups you can visualize where Fortune 100’s top companies to work for are located, and you can find a mailbox nearby. It’s certainly nice to use once in a while, and maybe worth bookmarking. But will this alone transform a business? Likely not.

Sharing location information via an online travels social network, like Brightkite, FireEagle, Bluenity, Dopplr, or TripIt, is also pretty cool. Using the integration hooks into Twitter, Facebook or LinkedIn, I can begin to share location information with those I trust. I can post where I am located now, and where I plan to be in the near future. I believe there could be value in this activity, but it’s not yet transforming the way I use information into business results.

But I am now beginning to see how these services combine to build a useful application. The “aha” moment for me was when I stopped looking for one mashup, or one sharing app, and saw how companies can combine multiple streams to create value out of data. What if you take a map mashup, a calendar mashup, a travel micro-share feed, an events feed, and dataset from a CRM system containing the names of locations of my customers? I trust that smart people can take these and create value. Why? Am I drinking the Kool-aid? No – I see signals that indicate this is happening.

Look at the new LinkedIn application widgets that mash-in LinkedIn data. My TripIt tool reminds me of a future trip to London, and tells me that I have a few LinkedIn contacts there. Based on information in my LinkedIn profile, LinkedIn tells me about relevant upcoming events. I found an interesting event in London. Will my contact attend that event? What if they’ll be traveling to Boston when I’m in London. Doh! So close.

But I see the outline of a new pattern. Whereas each data mashup is interesting, the right combination can transform my work behavior. There’s a who, what, when, and where, that all have to intersect onto a map and onto a calendar.

I recently met Sanjay Vakil of LuckyCal, who understands this pattern well. He is connecting the dots to create transformative value out of data streams. His product has some growing up to do before it is ready for enterprise rollout. But his product today combines a mapping mashup (telling me which of my contacts are where I am going to be), with a calendar mashup (matching when contacts of mine will be near me), and with an events stream (telling me what other events are taking place there at that time). Hey, that's the pattern: a set of data streams intersecting to create valuable information out of available information – but onto multiple mashup surfaces.

Suddenly the neurons start to fire. We need more than a single stream of data pins on a map to get our attention. As the LuckyCal product matures, it can become the paradigm for an enterprise mashup - triangulation. If it adds the data streams that matter most to me, (e.g. my CRM data), along with other streams and network information, it results in new information. The triangulation of these data sets means that I could predict whom I meet and what to do when I plan my trips. Moreover, my manager would be able see where the team’s travelers are now, and where they will be in the near future. My sales manager could see which of us will be traveling nearby other clients, and she may want take advantage of the proximity opportunities. Travel still happens, but we can get more value out of each trip. Enterprises like to hear that.

So, if you combine two mashups and couple of data feeds, you can create transformative value from readily available information. I had faith this could be created, but now that I see signals that others are implementing solutions like this. I have renewed faith in the relevance of mashups to enterprise computing. It's just more complex than splashing a data set onto a map. That's OK, enterprises are used to leveraging complexity to create value. And mashups can be the building blocks to enable their success.

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B

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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C

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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E

Boris Evelson
Research coverage for Information & Knowledge Management professionals
Information and knowledge management, business intelligence, OLAP, data warehousing
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G

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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H

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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K

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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L

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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M

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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O

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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P

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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R

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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S

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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Oh No, Not Another 2.0 -- Database 2.0? Data Warehousing In The Cloud!

By James Kobielus

Boris Evelson's latest post on free BI got me thinking about another type of freedom.

Boris commented on the newly announced beta of a gratis, lightweight, Panorama-powered BI/OLAP-engine add-on to Google's hosted apps. You know, whenever anybody mentions BI/OLAP, I think of analytical databases, hence data warehousing (DW). And when my thoughts turn to DW, I often wonder when these dimensional data stores will be let loose from their earthly tethers and begin to float free in the SaaS cloud. This is no blue-sky speculation, but rather an inevitability in a world shifting to subscription-based SaaS for on-demand delivery of all infrastructure and application services. Where database services are concerned, this trend even has a name in popular circulation: Database 2.0 (aka "cloud databases").

Let it be known that Google is one of the pioneers in Database 2.0, though they haven't tooted their horn or done anything particularly special in this regard (smaller SaaS solution providers such as Trackvia, DabbleDB, and Zoho have more full-featured Database 2.0 offerings than Google, albeit not particularly BI/OLAP/DW-focused). A year or two ago, Google went open beta (still in that phase, actually) with a hosted database service called GoogleBase. Now, from what I've seen, GoogleBase is not a general-purpose transactional or analytical database. And it’s certainly not a DW or data mart in the clouds. Instead, GoogleBase seems to be an online repository -- or rather, depository -- into which external parties submit structured data for Google to crawl and index deeply for access from Google's big whompin' search engine.

Even more noteworthy is Microsoft's recent foray into the Database 2.0 space -- a move that some might consider a "validation" of this approach in the eyes of enterprise I&KM professionals. Microsoft has just rolled out a beta of its hosted SQL Server Data Services. The vendor has started to host services that have heretofore have been available only from SaaS partners. This is, of course, a key piece of the Redmond WA-based vendor's begrudging effort to push more solutions into a Microsoft-hosted SaaS cloud. However, from what I can see so far, Microsoft is simply hosting a subset of the functionality of its general-purpose RDMBS platform for OLTP and OLAP. However, Microsoft has not specifically optimized SQL Server Data Services for OLAP, unlike any truly scalable BI/OLAP/DW platform.

Back to Google for a sec. What I fully expect from them in the coming year or two -- and from every SaaS cloud everywhere before long -- are feature-complete, hosted, subscription-based DW services for high-performance, high-volume, complex analytics. Naturally, this cloud should be called DW 2.0. It should leverage the full virtualized, distributed, scalable, grid-enabled computing fabric that the Googles of this world can bring to bear on the very largest structured data sets, most resource-intensive query-processing tasks, and richest visualizations imaginable. Per Boris's suggestion, it could even serve as a supremely scalable BI, data mining, or predictive analytics "sandbox" for developers and power users who have no other speedy, cost-effective alternatives for procuring the necessary horsepower for various projects and production requirements.

I second Boris’s challenge: Google should consider integrating the Panorama OLAP-engine add-on (remember, it's just a beta) with a more analytics-enabling future version of GoogleBase (which is also still a beta). In so doing, Google -- if it eventually decides to go into full production with all this -- would be able to offer full-featured DW and BI services on a hosted platform that is as infinitely scalable as the concatenated string of Os inside the ever-extensible company name that displays within its multipage search-result screens. I also share Boris's concern that whatever hosted OLAP/BI/DW services Google eventually offers may lack enterprise-grade metadata management, data cleansing, data-source connectivity, security, and other key features.

I also expect Microsoft to evolve SQL Server Data Services in the DW 2.0 direction, an effort that no doubt would intensify if Mr. Ballmer succeeds in grabbing Yahoo. I'd like to see Microsoft cross-synthesize SQL Server Data Services with any hardware-partner-powered OLAP-acceleration approaches it may or may not be developing under its DW appliance initiative. At the very least, I'd like to see Microsoft provision some seriously scalable DW horsepower in its data center, perhaps through a partnership with Teradata.

Clearly, DW 2.0 services will need to be an order-of-magnitude more powerful than what we've come to expect under the first generation of SaaS-based BI/DW offerings on the market. Whether dedicated to a single customer's requirements or divvied up on a shared-tenant basis, DW 2.0 could be the biggest, baddest, most virtual DW "appliance" of them all. And it would be another key step in the progressive virtualization of the entire SOA stack, apps, middleware, hardware, and data services across the  Enterprise 2.0 or Web 2.0 fabric.

Oh yes, yet another 2.0 -- or two -- for you. Wouldn’t it be interesting if Google and/or Microsoft acquired a DW appliance vendor? I would not be at all surprised if announcements such as these precipitated from the cloud of pregnant possibilities.

And is it too far-fetched to imagine that Microsoft might turn around and acquire Teradata if the Yahoo takeover falls through? My crystal ball's still a bit cloudy on the matter.

But, hey, I'm free to speculate.