hype cycle for business intelligence and performance...

ResearchPublication Date: 23 July 2007 ID Number: G00149421

© 2007 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartner's research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

Hype Cycle for Business Intelligence and Performance Management, 2007 Andreas Bitterer, Kurt Schlegel, Bill Hostmann, Bill Gassman, Nigel Rayner, Neil Chandler, Mark A. Beyer, Gareth Herschel, John Radcliffe, Andrew White, Tim Payne, Whit Andrews, David Newman

This Hype Cycle examines the evolution of business intelligence over the last year. More traditional BI technologies have become increasingly mature, while new technologies relating to broader performance management concepts have emerged.

Publication Date: 23 July 2007/ID Number: G00149421 of 35

© 2007 Gartner, Inc. and/or its Affiliates. All Rights Reserved.

TABLE OF CONTENTS

Analysis ............................................................................................................................................. 4 What You Need to Know ...................................................................................................... 4 The Hype Cycle .................................................................................................................... 4 The Priority Matrix ................................................................................................................ 6 Off The Hype Cycle .............................................................................................................. 7 On the Rise........................................................................................................................... 7

Entity Resolution and Analysis ................................................................................ 7 Customer Relationship Performance Management ................................................ 8 Integrated Business Planning.................................................................................. 9 Product Performance Management ...................................................................... 10 Closed-Loop Performance Management .............................................................. 11 Open-Source Business Intelligence Tools ............................................................ 12 SaaS - Business Intelligence................................................................................. 13 SOA-Based Analytic Applications ......................................................................... 14 In-Memory Analytics .............................................................................................. 15 Master Data Management ..................................................................................... 16

At the Peak ......................................................................................................................... 17 Real-Time Best Next Action .................................................................................. 17 Search Capabilities for Business Intelligence ....................................................... 17 Business Activity Monitoring.................................................................................. 18

Sliding Into the Trough .......................................................................................................19 Profitability Modeling and Optimization ................................................................. 19 Interactive Visualization......................................................................................... 20 CPM Suites............................................................................................................21 Business Application Data Warehouses ............................................................... 22 Excel as a Business Intelligence/CPM Front End ................................................. 23

Climbing the Slope ............................................................................................................. 24 Real-Time Data Integration ................................................................................... 24 Dashboards/Scorecards ........................................................................................ 25 Web Analytics........................................................................................................ 26 Data-Mining Workbenches .................................................................................... 27 Data Quality Tools ................................................................................................. 28 Budgeting, Planning and Forecasting for CPM ..................................................... 29 Business Intelligence Platforms ............................................................................ 30

Appendices......................................................................................................................... 31 Hype Cycle Phases, Benefit Ratings and Maturity Levels .................................... 33

Recommended Reading.................................................................................................................. 34

LIST OF TABLES

Table 1. Hype Cycle Phases ........................................................................................................... 33 Table 2. Benefit Ratings .................................................................................................................. 33 Table 3. Maturity Levels .................................................................................................................. 34



LIST OF FIGURES

Figure 1. Hype Cycle for Business Intelligence and Corporate Performance Management, 2007... 5 Figure 2. Priority Matrix for Business Intelligence and Corporate Performance Management, 20077 Figure 3. Hype Cycle for Business Intelligence and Corporate Performance Management, 2006. 31



ANALYSIS

What You Need to Know As the concept of business intelligence (BI) becomes increasingly pervasive and focuses more on performance management (PM), the technologies and analytic applications required to support a broad BI and performance management strategy become more complex to understand and manage. CIOs and BI strategists should use this Hype Cycle to help prioritize their investments in BI and performance management. BI and PM should not be seen as separate initiatives and technologies, but as integral parts of an organization's IT infrastructure, and accepted elements of the organization's culture. While BI and PM technologies are mature, there are several emerging technologies that will have a dramatic impact on how BI and PM are delivered.

The Hype Cycle This year, the BI and PM Hype Cycle has been expanded to reflect the fact that BI is now becoming vital for better performance management across multiple dimensions of an organization and its business processes. A number of new analytic applications focused on performance management have emerged, and some examples (Customer Relationship Performance Management and Product Performance Management) have been included to show how analytic applications are becoming an increasingly important part of an overall BI and PM strategy. These applications also show companies' increasing focus on other areas of business operations (including customer management and the supply chain) when compared with corporate performance management (CPM), which focuses more on the corporate and CFO-level view of business performance. CPM continues to mature rapidly as a concept, and CPM suites are approaching the Slope of Enlightenment as they become more widely deployed. The boundaries between BI platforms and CPM suites are becoming increasingly blurred, as shown by Business Objects' acquisition of ALG Software and Cartesis, and Oracle's acquisition of Hyperion.

The technologies required to support BI and PM continue to evolve. Some technologies, like BI Search Capabilities, are maturing rapidly and are reaching the Peak of Inflated Expectations. Emerging technologies, such as Entity Resolution and Analysis, have been added. Other technology profiles have been updated to reflect changing usage in the market. For example, what used to be called Hosted BI on last year's Hype Cycle has now been renamed as SaaS - Business Intelligence. Similarly, Advanced Visualization has been renamed Interactive Visualization to more accurately reflect how this technology is deployed and used. Overall, the markets that deliver BI and PM technologies continue to show healthy growth, with stable core technologies complemented by continuous innovation.



Figure 1. Hype Cycle for Business Intelligence and Corporate Performance Management, 2007

Technology Trigger

Peak ofInflated

ExpectationsTrough of

Disillusionment Slope of Enlightenment Plateau of Productivity

time

visibility

Years to mainstream adoption:less than 2 years 2 to 5 years 5 to 10 years more than 10 years

obsoletebefore plateau

As of July 2007

Business Intelligence PlatformsBudgeting, Planning and Forecasting for CPM

Data Quality ToolsData-Mining Workbenches

Web AnalyticsDashboards/Scorecards

Real-Time Data Integration

Excel as a Business Intelligence/CPM Front End

CPM Suites

Interactive Visualization

Profitability Modeling and Optimization

Business Activity Monitoring

Search Capabilities forBusiness Intelligence

Real-Time Best Next Action

Master Data ManagementIn-Memory Analytics

SOA-Based Analytic ApplicationsSaaS — Business Intelligence

Open-Source BusinessIntelligence Tools

Closed-Loop PerformanceManagement

Product PerformanceManagement

Customer Relationship Performance Management

Entity Resolutionand Analysis Business Application Data Warehouses

Integrated Business Planning

Technology Trigger

Peak ofInflated



time

visibility

Technology Trigger

Peak ofInflated



Technology Trigger

Peak ofInflated



time

visibility

time

visibility





As of July 2007

Business Intelligence PlatformsBudgeting, Planning and Forecasting for CPM

Data Quality ToolsData-Mining Workbenches

Web AnalyticsDashboards/Scorecards



CPM Suites




Search Capabilities forBusiness Intelligence


Master Data ManagementIn-Memory Analytics

SOA-Based Analytic ApplicationsSaaS — Business Intelligence

Open-Source BusinessIntelligence Tools

Closed-Loop PerformanceManagement

Product PerformanceManagement


Entity Resolutionand Analysis Business Application Data Warehouses


Source: Gartner (July 2007)



The Priority Matrix For the second year in a row, Gartner's 2007 survey of CIOs showed that BI applications were again their highest technology priority, so it comes as no surprise that BI investments are increasing. Organizations should focus on technologies with high or even transformational impact, but they must still keep an eye on technology triggers and non-mainstream technology areas, as the BI market continues to show a lot of innovation that could have a considerable impact.

To help organizations prioritize investments in relation to their level of impact — even though applicability, budget, the time to implement and receive payback, and returns on investment will enter into the equation — we provide the following priority matrix. The priority matrix describes some of the technologies with the biggest impact, and those worth considering for adoption.

Analytic applications that support PM have a high or transformational impact because they allow executives to implement transformational strategies more effectively. CPM suites are the most mature applications in this space, and should be a high priority for all organizations in the next two years. However, product-centric companies are likely to gain similar benefits from Product Performance Management, and although this is still emerging it should be on the medium-term planning horizon for these organizations. However, CPM suites, BI platforms and other related BI technologies will be affected by the emergence of service-oriented architectures (SOAs), especially Data Services Architectures, which are expected to transform the way in which organizations and vendors build, extend and integrate analytical components into applications and processes.

Data quality is a challenge for almost every organization and should be considered a top priority in every BI or CPM initiative. Poor data quality has a negative effect on an organizations' efficiency, and can jeopardize the often large investments in data warehousing, BI and CPM. Data quality tools are maturing, and investment in this area — accompanied by the necessary cultural changes and the introduction of a data stewardship program — has an obvious impact on the quality of decision making, increasing the trust in information provided by reporting, analysis and PM applications.

More and more vendors are also exploiting the benefits of having no memory constraints in 64-bit hardware environments, thereby allowing them to move more data into memory where it can be analyzed much more rapidly than in a traditional, mostly disk-based approach. The falling prices of blade servers and RAM will continue the trend whereby BI platforms provide high-performance reporting services on large volumes of data.

Open-source BI still has a very low adoption rate and faces a long road to maturity. For most Global 2000 enterprises it hasn't come up as a topic in BI and PM initiatives, as the technology gap between commercially available BI platforms and CPM suites and the equivalent open-source offerings is too broad for most companies to try open-source solutions for significant and critical deployments. In addition, while some vendors position themselves as open-source providers, getting full functionality from the BI platform means paying a license or maintenance fee.

Another relatively new approach to BI is through software as a service (SaaS). Only very few mainstream vendors are delivering their reporting, analysis or "dashboarding" capabilities to the market using this hosted model, and this makes up only a tiny part of their business. Market demand from end-user organizations is similarly very low — large organizations, in particular, do not plan to outsource the data warehouse and its affiliated technologies. It's mostly small and midsize businesses that use hosted applications such as customer relationship management (CRM) or Web Analytics, and that are using the reporting services as part of the hosted model.



Figure 2. Priority Matrix for Business Intelligence and Corporate Performance Management, 2007

Open-Source Business Intelligence Tools

low

years to mainstream adoptionbenefit

moderate

high

transformational

As of July 2007

Business Application Data Warehouses



Saas — Business Intelligence

SOA-Based Analytic Applications

Web Analytics




In-Memory Analytics


Master Data Management

Entity Resolution and Analysis



Search Capabilities for Business Intelligence

Budgeting, Planning and Forecasting for CPM

Business Intelligence Platforms

Dashboards/Scorecards

Data-Mining Workbenches

Data Quality Tools

Closed-Loop Performance Management

Product Performance Management

CPM Suites

more than 10 years5 to 10 years2 to 5 yearsless than 2 years

Open-Source Business Intelligence Tools

low

years to mainstream adoptionbenefit

moderate

high

transformational

As of July 2007

Business Application Data Warehouses



Saas — Business Intelligence

SOA-Based Analytic Applications

Web Analytics




In-Memory Analytics


Master Data Management

Entity Resolution and Analysis



Search Capabilities for Business Intelligence

Budgeting, Planning and Forecasting for CPM

Business Intelligence Platforms

Dashboards/Scorecards

Data-Mining Workbenches

Data Quality Tools

Closed-Loop Performance Management

Product Performance Management

CPM Suites

more than 10 years5 to 10 years2 to 5 yearsless than 2 years


Off The Hype Cycle A few technology profiles have been dropped from the Hype Cycle, including CPM and Compliance, Enterprise Information Management and Spreadsheet-Based BI, because they were considered obsolete, were transferred to another Hype Cycle, or were subsumed by other technology profiles.

On the Rise Entity Resolution and Analysis Analysis By: Mark Beyer

Definition: Entity resolution and analysis is the capability to resolve multiple individuals, products or other noun classes of data into a single resolved entity when pseudonyms, alias names or other synonym-style constructs exist. While most prevalent in detecting perpetrators of criminal or illegal activity, more-commercial applications exist too. Mining an entity resolved data set detects affinity networks. An affinity network is a group of nouns (people, materials and organizations, for example) that cooperate as a group (fraud networks, supply chains and bill of materials, for



example) to complete an activity or series of events. Mining for criminal activity or security threats discovers fraud networks or is used in counter-terrorism and criminal racketeering investigations. More commercial information mining takes place in discovering the true use or affinity of materials or supplies as opposed to relying on specified lists and procedures.

Position and Adoption Speed Justification: Entity resolution and analysis was previously an obscure, but gradually developing, technology that has come to the forefront as a result of world events and market forces. One primary driver includes counter-intelligence, both in government and in the commercial world. While counter-intelligence is not a term frequently applied to commerce, it does exist in the detection of fraud networks, racketeering investigations and money-laundering. Government, national and international security issues have raised awareness regarding the issues of resolving entities within information sets. Another driver is market intelligence and intelligence validation. Alias operators and common terminology that replaces official branding and model designations with slang phrases is a benign driver. Because of the enormous benefit across nearly all markets, this technology will move rapidly toward maturity and already exists in its many parts. The technology will reach an inflated peak of expectation sometime during late 2008, when the quality of data used with these tools will reveal a weakness in their utilization.

User Advice: End-user organizations can begin the implementation of these technology tools, but must maintain highly qualified and available metadata regarding how the evaluation engines operate, the data inputs provided, the data quality of those inputs and qualification of alternative outputs from these style of analytics. Through 2009, commercial applications of this technology will be challenged by data quality issues. In using these evaluation engines, all government and commercial organizations are cautioned to utilize these mining and analytic results only when subjected to significant human oversight. Use of this technology should include the adoption of data stewardship and dedicated analysts to interpret the results.

Business Impact: Benign applications (alternate bill of materials analysis and consumer reference networks, for example) will see rapid results from this technology, in which market affinities are quickly identified and supplier issues surface rapidly. For example, "Kleenex" is actually a facial tissue reference for any other manufacturer than Kleenex. Mitigation efforts for more malevolent activities will be challenged as the purveyors of criminal activity will exercise their creativity in defeating these systems. Linking this technology with voice and speech recognition solutions will assist in staying ahead of criminal counter-measures to defeat this technology. Additionally, analytics technology that extracts identifying information used in reconciling entities from audio, video, image and other formerly unstructured data types is needed for the technology to reach its inherent potential.

Benefit Rating: High

Market Penetration: 1% to 5% of target audience

Maturity: Adolescent

Sample Vendors: Firstlogic; IBM; Informatica; Netrics; Similarity Systems

Recommended Reading: "Are Federated Metadata Approaches to Business Service Repositories Valid?"

"Government 2020 Scenarios: Status Quo Development"

Customer Relationship Performance Management Analysis By: Gareth Herschel



Definition: An integrated set of technologies to identify the key performance indicators (KPIs) in the customer relationship (across marketing, sales and service functions), track and provide reports and alerts to key stakeholders about their status, and assist with the planning of an effective strategy to respond to deviations from expected performance.

Position and Adoption Speed Justification: Performance management exists at many levels in an enterprise, from the corporate level, typically concerned with financial and compliance metrics, to individual functional-level metrics, such as marketing, sales or customer service. However, the performance of the customer relationship must also be considered at an intermediate level that deals with nonfinancial metrics, such as customer satisfaction and customer value, and recognizes that the customer relationship is defined by the interaction of the customer across the entire enterprise, not just one channel or function at a time. Although subjectively accepted by most enterprises, the technologies to enable this holistic view of the customer relationship are still immature, with enterprises attempting to assemble this view from a variety of discrete solutions.

User Advice: The technology is still immature and fragmented, so enterprises must have a clear view of what they are trying to achieve before beginning any type of technology selection. Although based on a reporting and alerting capability, technologies such as data mining can provide quantitative identification and validation of KPIs, as well as forecasting and simulation of likely performance. A variety of tools, such as feedback management; market research; voice and text mining, may be required to build a full understanding of customer opinions and satisfaction, while data mining, scoring engines and activity-based costing solutions may be required to estimate aspects of customer value, such as profitability, wallet share or lifetime value.

Business Impact: Correct identification of the key drivers of the customer relationship enables the enterprise to align activity with the enterprise's objectives and financial performance. Accurate and timely tracking of the enterprise's performance enables early identification of changing market conditions or a better understanding of market dynamics. An overview of the performance of the organization across marketing, sales and service gives executives greater confidence in the enterprise's ability to compete effectively and meet its commitments. Simulation and forecasting of possible responses to changing conditions enable executives to make rapid and confident adjustments to the enterprise's strategy and tactics to take advantage of the opportunity or compensate for the problem.


Market Penetration: Less than 1% of target audience

Maturity: Embryonic

Sample Vendors: KXEN; Oracle-Siebel Systems; Satmetrix; Teradata (a division of NCR); Utopy

Recommended Reading: "Get the Best Performance Out of Your CRM Metrics"

Integrated Business Planning Analysis By: Tim Payne

Definition: Integrated business planning (IBP) is a set of systems, processes and competencies that forms the strategic alignment and modeling capability that is missing from the traditional operationally focused ales and operations planning (S&OP) processes. IBP links corporate performance management to S&OP, with capability for strategic and financial modeling and analytics.

Position and Adoption Speed Justification: IBP is developing as the "big brother" to the S&OP process. S&OP has been around for many years, and is particularly well-known in manufacturing



organizations. S&OP was intended to reconcile business strategies as well as operational plans. However, the strategic dimension is mostly missing from S&OP today. Therefore, the concept of IBP is developing to contain strategic modeling and reconciliation capabilities as a set of systems, processes and competencies that sits over the operational S&OP processes and links into the organization's corporate performance management initiatives.

User Advice: IBP is a collection of capabilities and, as such, is not something that can be sourced totally from one vendor. However, different classes of vendors are progressively developing capabilities that are moving toward IBP capabilities (for example: CPM, supply chain planning, enterprise resource planning [ERP] and best-of-breed vendors). If you need to develop IBP capabilities, then approach this with a best-of-breed strategy for the next few years prior to more integrated solutions emerging. Work with your S&OP vendors to see which areas of IBP it is able to support as well as specialized strategic modeling vendors.

Business Impact: These capabilities will enable companies to model and align business strategies to operational strategies, ensuring significantly improved supply chain and business performance.



Maturity: Emerging

Sample Vendors: Interlace Systems; River Logic; SmartOps

Product Performance Management Analysis By: Andrew White; Tim Payne

Definition: Product performance management (PPM) includes the processes used to manage products across the supply chain performance (such as customer service, product profitability and total landed costs); the methodologies that drive some of the processes (such as the balanced scorecard or value-based management); and the metrics used to measure performance against strategic, operational and tactical performance goals.

However, PPM also consists of the convergence of business and analytical applications that provide the functionality to support these processes, methodologies and metrics, which are targeted at strategic users through tactical day-to-day decision making.

Position and Adoption Speed Justification: Most business applications are transactional in nature and business intelligence uses a duplicate and aggregated set of this data. Although users seek to make better and smarter business decisions concerning how products perform across the supply chain, the architecture used enforces a separation in role, technology and process between business applications and business intelligence. This leads to multiple data and process silos that make answering complex business problems hard to do and leads to supply chains that significantly underperform. Demand planning was an early example of the convergence of analytical and business applications since it was in demand planning (the business processes) where users needed to leverage analytics and data mining (business intelligence) to improve the results of their demand planning process. The business intelligence and business application vendor community is aware of this experience, but is only slowly bringing this unified functionality to market. However, this vision, for the most part, is predicated on high risk and long-term service-oriented architecture (SOA) strategies. Smaller vendors may have a better chance in the next couple of years of bringing this functionality to market in limited areas (such as demand planning).



User Advice: There are no comprehensive platform solutions for enterprisewide PPM, although larger vendors are moving in this direction. If you need to make a smarter decision in the supply chain, then approach this with a best-of-breed strategy for the next couple of years as larger platform offerings emerge and mature. Align your vendor partners with specific business pain points related to improving decision making across the value chain.

Business Impact: The use of business applications that embed business intelligence on a unified information-centric platform will help enterprises improve the timeliness and quality of decision making across the value chain, as well as align tactical day-to-day decision making with strategic (corporate performance management) efforts. Product performance across the value chain will be improved.

Benefit Rating: Transformational


Maturity: Embryonic

Sample Vendors: Blue Agave Software; Coreprocess; InfoNow; QlikTech; RiverLogic; Spotfire; Vendor Managed Technologies; Vision Chain

Recommended Reading: "Confusion Escalates in SCM Demand Planning Market"

"SCM Requires the Alignment of Decision-Making Solutions"

Closed-Loop Performance Management Analysis By: Neil Chandler; Nigel Rayner

Definition: Performance management is the discipline of "taking action" on the results of performance monitoring and blending them with desired plans and goals to drive business value and impact. Closed-loop performance management systems encompass multiple areas of business, including financial, sales, marketing, human resources, IT and operational functions. A key aspect of success in performance management is closing the loop between the results of monitoring performance and applying those results to drive and/or adjust business, people and process actions. This process also needs to feed back into the evaluation and definition/redefinition of business objectives. Often, this is described in terms of operational and strategic feedback loops.

Closed-loop performance management is achieved through a combination of management processes, a metrics framework and applications that support real-time access to operational and strategic data, coupled with collaboration and alerting technologies that enable users to define, agree on and monitor actions based on feedback.

Position and Adoption Speed Justification: Most organizations are in the early stages of maturity in enterprise-level performance management, typically focused on monitoring performance. They are also focused on technologies such as reporting and dashboards. As a result, most organizations find themselves with a lot of information noise that can be hugely chaotic. Few organizations have been able to effectively link their strategic and operational decision making with their performance management systems.

Many vendors claim to offer a "complete" performance management solution, but in reality, almost all vendors focus on a particular area of performance management. A closed-loop performance management solution requires a combination of analytic applications, BI infrastructure, business activity monitoring (BAM) and workplace tools. It also requires links to business process management (BPM) tools to enable business processes to be optimized as part



of the closed-loop process. BI and corporate performance management vendors are expanding their solutions to support strategic and operational planning, along with feedback mechanisms, and are also building BAM and BPM capabilities through acquisition and/or partnership. BPM vendors are also adding BI and BAM capabilities to address the same issue. However, no one vendor can yet deliver all the capabilities required to support closed-loop performance management. Organizations that are furthest down the track toward closed-loop performance management have typically built supporting systems by working in partnership with vendors to build specific solutions.

User Advice: Users should be cautious of vendor claims to offer closed-loop performance management solutions, because there is strong marketing hype. Users need to focus on aligning their strategic and operational objectives (and their IT and business resources) to define who, how and what needs to be measured. Organizations should learn to perform closed-loop performance management incrementally, optimizing the factors that most affect the business goals. Individual loops can then be linked together to form bigger loops. This approach will provide direction to expand BI platforms into BI and PM frameworks. Before rushing into technology purchases, users must also create a framework of metrics to represent the relationships between operational and financial key performance indicators. Many organizations tend to fall back on an overreliance on financial measures (because this is what markets and regulators use to measure performance). The BI competency center should also play an important role, because this is a forum in which different parts of the business can work together to identify the appropriate technology solutions.

Business Impact: Closed-loop performance management will be highly valuable to all enterprises because it will "close the loop" between strategy and operational execution in real time. By employing closed-loop PM to link operational and strategic measures, organizations can ensure that the business strategy is well communicated and that everyone is making decisions in support of the same strategic objectives. Finally, performance against chosen strategies can be monitored more closely, enabling executives to make changes (where necessary) more quickly in response to measured outcomes.




Open-Source Business Intelligence Tools Analysis By: Bill Hostmann

Definition: Open-source BI tools refers to BI technologies and application development components that are available as part of open-source application development, application servers or database platforms. They are often provided for free, subject to the chosen open source's license terms.

Position and Adoption Speed Justification: Interest in open-source BI technology is high as companies seek alternatives to higher-priced, commercially available products. Although the technology is being adopted by software vendors developing their own applications for resale, the adoption rate as a replacement to commercial BI platforms within enterprises is still low and growing slowly. This is due to the additional development skills required to realize needed capabilities and integration (such as security, scalability and administration, end-user self-service and metadata) available from commercial BI platform products.



User Advice: Licensing and indemnification of open-source BI technologies for business use varies widely. Advanced application development skills may also be required. Enterprises should evaluate these, as well as the technologies.

Business Impact: Most of the open-source BI technologies are developer components for adding low-volume reporting into applications, and are free and available with open-source development environments such as Eclipse. The skills required to develop, maintain and integrate these technologies can be much higher than many of the more-complete commercially available BI platform products.

Benefit Rating: Low


Maturity: Emerging

Sample Vendors: Actuate; JasperSoft; Pentaho

SaaS - Business Intelligence Analysis By: Bill Hostmann; Neil Chandler

Definition: SaaS for BI is defined as BI functions and applications that are supported by a vendor as a service, accessed over the Internet, without the need to deploy and maintain an on-premises solution. SaaS-based BI enables customers to quickly deploy one or more of the prime components of BI without significant IT involvement. These prime components are:

Analytic Applications — Support performance management with prepackaged functionality for specific solutions, such as WebTrends for Web analytics, Adaptive Planning for CPM and WebApps for workforce analytics

BI Platform — Provides a development and integration environment, information delivery, and analysis, such as Crystal Reports for reporting, and broad platforms from SeaTab, LucidEra, Oco and Pentaho

Information Management Infrastructure — Provides the data architecture and data integration infrastructure, such as Informatica for data and application integration, and Pervasive for broad data management

Position and Adoption Speed Justification: SaaS-based BI enables business users (particularly midsize organizations or departments of enterprises) to quickly and easily implement solutions, with front-end reporting and analysis, as well as back-end integration and data management, without significant IT involvement. Lower costs, easier support without IT involvement and easier deployments, rather than sophisticated functionality, are driving SaaS-based BI adoption. There are only a few vendors offering BI as a service. To date, most offerings have focused on CRM applications such as salesforce.com and Siebel CRM On Demand, and Web analytics software such as WebTrends and Google Analytics. Recently, vendors have started to gain momentum in other areas, such as BI and CPM with, for example, Adaptive Planning for budgeting-led CPM solutions. However, if these early service offerings are successful, the market may grow quickly, especially in the midmarket and at the departmental level.

User Advice: Only enterprises with straightforward requirements should consider using BI as a service. Those with more complex requirements should evaluate traditional vendor offerings and may consider consuming on-premises software, but on a hosted basis. However, on-demand BI poses an integration challenge for enterprises to export data to and extract data (and metadata)



from the service provider, so that they can incorporate it into their own BI infrastructure. IT managers must ensure that, if chosen, SaaS vendors provide assurances through adequate service-level agreements (SLAs), govern how a SaaS-based BI application will coexist with internal applications and infrastructure, and overcome privacy and security issues.

Business Impact: Business users are becoming increasingly frustrated with the long deployment cycles, high costs, complicated upgrade processes and IT infrastructure demanded by traditional BI solutions. SaaS-based BI offers a quick, low-cost and easy alternative that has proved popular, particularly in small and midsize businesses and departments of large enterprises that either lack or do not want to support internal BI resources. As SaaS-based BI solutions mature and become more widely adopted, they will enable organizations to compose their BI infrastructure from a range of on-demand providers, but probably with less flexibility than on-premise alternatives. This will have a major business impact by transforming the way that organizations choose, buy and deploy their BI solutions.

Benefit Rating: Moderate


Maturity: Embryonic

Sample Vendors: Adaptive Planning; Business Objects/Crystal; Informatica; Oco; SAS

Recommended Reading: "Hybrid SaaS Deployment Models Should Cause Concern"

"Employ a Coordinated Approach to Business Intelligence and Corporate Performance Management"

SOA-Based Analytic Applications Analysis By: Neil Chandler

Definition: This is the provision of analytic applications (for example, corporate performance management, marketing performance management, sales performance management, contact center performance management, employee performance management, product performance management, supply chain planning, and sales and operations planning) that are based on a SOA. Analytic applications use business intelligence technology combined with specific packaged functionality to support specific business processes (such as financial budgeting or sales forecasting). The emergence of SOA-based analytic applications enables users to consume functionality from vendors as services and to create their own composite applications that reflect the way they need to perform these business processes.

Position and Adoption Speed Justification: Enterprises are increasingly looking to buy, rather than build, analytic applications. However, the majority of analytic applications offered by vendors have a traditional architecture and are increasingly sold as vendor-centric suites. SOA-based analytic applications offer a component-based approach to performance management, so users will be able to consume analytic applications without having to buy integrated suites. The applications will enable users to create composite applications that might, for example, connect sales forecasting from one vendor with financial budgeting from another. Although SOA makes it easier to integrate analytic applications with corresponding enterprise applications, this integration will be easiest within a single ecosystem. This means that an organization's infrastructure strategy is fundamental to building a portfolio of analytic applications.

User Advice: Users considering new analytic applications, and who have planned or already deployed SOA-based applications in other areas, should include an assessment of the vendors' SOA strategy as part of their evaluation criteria.



Business Impact: SOA-based analytic applications will change the way that users build and deploy performance management solutions. As more SOA-based analytic applications are developed and available as services, they will enable the delivery of solutions that embed analytic functionality into composite enterprise applications, thus reducing latency, fostering more widespread use of analytical insight by enterprise application users, and enabling an increase of automation around business processes.



Maturity: Emerging

Sample Vendors: IBM; Oracle; SAP

Recommended Reading: "Application Development Suites Will Be Critical to SOAs"

In-Memory Analytics Analysis By: Kurt Schlegel

Definition: In-memory analytics is an alternative BI performance layer where detailed data is loaded into memory for fast query and calculation performance against large volumes of data. This alternative obviates the need for building relational aggregates and pre-calculated cubes.

Position and Adoption Speed Justification: Declining memory prices, coupled with the widespread adoption of 64-bit computing, which provides large addressable memory space, will prime the market for in-memory analytics. More vendors are providing BI platforms that compile to 64-bit. However, very few use in-memory storage as the main data repository. Most of these vendors use the larger addressable memory space to supplement their disk-based storage architecture to boost performance. Currently, several BI platform vendors don't compile to 64-bit, nor have they designed their software to take advantage of the larger addressable memory space.

User Advice: For response-time issues and bottlenecks, IT organizations should consider the performance improvement that in-memory analytics, especially when run on 64-bit hardware, can deliver. Users should be careful to use in-memory analytics as a performance layer, not as a substitute for a data warehouse. This technology has the potential to subvert enterprise standard data integration efforts.

Business Impact: All levels of the enterprise benefit from fast response times of memory-based BI. The reduced need for database indexing and aggregation further enables database administrators to focus less on optimization of database performance and more on value-added activities. In addition, it will enable better self-service reporting and analysis because there will be less dependence on aggregates and cubes.



Maturity: Emerging

Sample Vendors: Applix; Panoratio; QlikTech; SAP; Spotfire

Recommended Reading: "BI Applications Benefit From In-Memory Technology Improvements"



Master Data Management Analysis By: John Radcliffe; Andrew White; David Newman

Definition: Master data management (MDM) is a workflow-driven process in which business units and the IT organization collaborate to harmonize, cleanse, publish and protect common information assets that must be shared enterprisewide. MDM ensures the consistency, accuracy, stewardship and accountability for the core information of the enterprise, enabling organizations to eliminate endless debates about "whose data is right." MDM moves the organization closer to long-sought-after data-sharing objectives in the application portfolio. An MDM program is a key part of an enterprisewide commitment to information management and helps organizations break down operational barriers, enabling greater enterprise agility and simplifying integration activities.

Position and Adoption Speed Justification: Comprehensive technology solutions for supporting enterprise-level, cross-domain MDM programs are still in their early stages and will take several years to mature. The current technologies labeled "MDM products" are either: a combination of separately developed customer data integration (CDI) hub and product information management (PIM) products, mainly focused on the "upstream" operational environment, and undergoing integration and broadening; CDI hub or PIM products that have a degree of generic capability and have been repositioned to address wider MDM issues, but need broader and deeper functionality; or cross-domain MDM products that mainly focus on the demands of the "downstream" analytical and reporting environments.

User Advice: Use MDM techniques and technology to achieve consistency, accuracy and integrity of information assets at a strategic level in upstream, operational environments. Most organizations are focusing on drill-down domain requirements, using CDI hub or PIM technology, but they must plan how to leverage that expertise into other domains. Also, there is a potential role for using tactical MDM technologies for solving semantic inconsistency issues in downstream, business intelligence, analytical and corporate performance management environments. Over time, organizations will need to rationalize these activities with their MDM initiatives in the operational environment. All MDM initiatives will need to be aligned with the objectives of the organization's enterprise information management (EIM) program. When addressing MDM issues by subject or domain area (such as customer or product), leverage expertise to expand into other domains. MDM efforts can originate in any function, but, for maximum value, initiatives must be consolidated into a comprehensive EIM program.

Business Impact: By 2010, 70% of Fortune 1000 organizations will use MDM as a disciplined process to achieve consistency of commonly shared business information for compliance, operational efficiency and competitive differentiation purposes.



Maturity: Emerging

Sample Vendors: IBM; Kalido; Oracle; SAP; Siperian; Tibco Software

Recommended Reading: "Mastering Master Data Management"

"Vendors Have Different Approaches to Implementing Master Data Management"

"How to Choose the Right Architectural Style for Master Data Management"



At the Peak Real-Time Best Next Action Analysis By: Gareth Herschel

Definition: Real-time best next action combines predictive analytic and decisioning capabilities to identify the optimal next action to take in a process such as a customer service interaction. The analysis can be based on a variety of approaches (for example, product affinities or customer behavior predictions), but the solution must include an arbitration (rules) capability to select the optimal (based on the enterprise's strategy) of several possible treatments.

Position and Adoption Speed Justification: This capability is moving from a niche capability offered by a few best-of-breed vendors to a standard component of larger suites offered by the mainstream vendors. The application of this technique to cross-sell recommendations is increasingly mature, but the possibilities for a wider variety of treatments, such as insurance claims processing, field-service dispatch, warranty analysis or customer retention, have not yet been broadly adopted.

User Advice: Consider this approach initially for high-risk interactions (that is, interactions that are difficult to reverse if improperly handled), such as customer churn, fraud or risk assessments. Cross-selling is the easiest approach to cost-justify, but the need for real-time analysis and offer selection is not as pressing as those interactions associated with risk. Long term, consider this technology for domains beyond CRM, such as quality control or supply chain management.

Business Impact: Early adoption has primarily focused on the contact center, turning a purely service-oriented interaction into a blend of marketing/sales and service. Other channels of interest include retail stores (and bank branches) and Web sites. The application of this technology to offline, but process-oriented decisions (such as credit approvals) is also likely to have a profound impact on the way that enterprises make decisions, with more competitive differentiation on the creation of insight to feed the decision, and the business logic of how to make a decision, rather than the mechanical operation of the decision itself.



Maturity: Emerging

Sample Vendors: Chordiant Software; Fair Isaac; Infor CRM Epiphany; SPSS; ThinkAnalytics

Recommended Reading: "How to Achieve Real-Time CRM"

"Select Customer Data-Mining Vendors Based on Focus and Vision"

Search Capabilities for Business Intelligence Analysis By: Andreas Bitterer; Whit Andrews

Definition: Search capabilities — to index and retrieve unstructured information — added to BI solutions enable users to find reports, metrics and other objects in the BI metadata repository.

Position and Adoption Speed Justification: BI platforms have improved to the point where search (indexing/retrieval) capabilities can be applied to find semi-structured information that exists within the metadata of a BI repository. Several BI platform vendors have taken advantage of this opportunity and have partnered with search vendors to release a BI search capability. However, end-user adoption of search capabilities in a pure BI context is still relatively low.



User Advice: BI search capabilities are new. BI platform vendors have only recently been announcing these capabilities and there are few working examples of this technology. As the technology matures and becomes more useful, we expect it to become a common capability of BI platforms. Therefore, recent announcements about BI search capabilities should not have a major effect on the selection process for BI platform vendors.

Business Impact: Initially, BI search capabilities will make it easier for users to find existing BI content — making it possible for virtually any user to become a BI user. This technology can also enable users to explore information from report to report without predefined drill paths created by IT. Eventually, a more-advanced application of the technology will attempt to create new reports "on the fly" by applying search keywords to BI metadata objects.



Maturity: Emerging

Sample Vendors: Attunity; Business Objects; Cognos; Endeca; Fast Search & Transfer; Google; Information Builders; SAS Institute

Recommended Reading: "Don't Wait for Search and BI to Become the Same Product"

Business Activity Monitoring Analysis By: Bill Gassman

Definition: BAM describes the processes and technologies that provide event-driven, real-time access to and analysis of critical business performance indicators. BAM is used to improve the speed and effectiveness of business operations by raising awareness about issues as soon as they can be detected. BAM applications issue alerts about a business opportunity or problem and, in most cases, drive a dashboard with metrics, historical information, an event log and drill-down features to help the business operations staff process them. The processing logic of a BAM system may use simple stream or complex event processing.

Position and Adoption Speed Justification: BAM has crossed over the hump of the Hype Cycle, and although growth in the market is steady, marketing will be done with less fanfare than before. There is no single BAM market. The hype is distributed across multiple areas, which makes it appear less exciting than it really is. Looking across all avenues of deployment, there are signs that there will be more deployments of BAM applications this year than last. Application vendors, including those selling SaaS, are building real-time metrics into their products. Business process management vendors continue to partner with BAM vendors or build process-monitoring features, a form of BAM, into their products. Enterprise service bus vendors continue to enhance and promote BAM. BI vendors are waking up to the topic. For example, in January 2007, Cognos acquired Celequest, one of the pure-play BAM vendors. The remaining pure-play vendors are growing, although most business is going to multifaceted vendors. There is awareness of BAM in the IT community, but business users need to drive the projects and many are still unaware of the possibilities or value of BAM. In addition, there is terminology confusion. Other marketing terms, such as operational BI and real-time BI, are blending with the perception of what BAM offers. The vendors building complex event-processing engines are enjoying market growth and will likely add dashboards and human-alerting features, overlapping with and becoming part of the BAM market. In addition, we are seeing enterprises build BAM applications out of BI tools and other piece parts. We believe that three to five years is a realistic, although perhaps a bit optimistic, expectation for the market to reach the plateau, which is defined as more than 20% adoption by the target market.



User Advice: Begin adoption now to gain experience. Start with simple projects, or expand the use of BAM if early projects have been successful. Allow time for resources to learn to trust the system and to take action based on real-time alerts. Match current and future system performance requirements with product capacity. Depending on specific industry or process needs, look for BAM products that include specialized knowledge, such as supply chain, check clearing, compliance monitoring or fraud detection. Promote success and share best practices with other groups in the enterprise. Products will come and go, but it takes time to build a culture that can understand how to use real-time alerts and information in its processes.

Business Impact: BAM provides real-time situational awareness and detects anomalies in the processes of supply chain operations, event-based marketing, business-to-business value-added networks, compliance activities and orchestrated business processes. Anywhere an enterprise has a time-sensitive business process, automated or manual, it can deploy BAM to better understand, monitor and generate alerts when problems or opportunities arise.




Sample Vendors: Cognos; Information Builders; Microsoft; Oracle; Progress Software/Apama; SeeBeyond; SeeWhy; Software AG; Syndera; Systar SA; Tibco Software

Recommended Reading: "MarketScope for Business Activity Monitoring Platforms, 3Q06"

"Selection Requirements for Business Activity Monitoring Tools"

Sliding Into the Trough Profitability Modeling and Optimization Analysis By: Nigel Rayner; Gareth Herschel

Definition: Profitability modeling and optimization includes activity-based costing (ABC) applications that determine and allocate costs at a highly granular level (for example, to determine the cost of each task or activity that an agent may perform across all channels in a customer service contact center). This information can be applied to various "cost objects," including products, customers or customer segments, to help determine product and customer profitability.

Activity-based management applications take this approach further. They provide modeling capabilities to enable users to model the effect of different cost and resource allocation strategies on profitability. More-sophisticated applications have moved beyond the traditional ABC focus to enable revenue to be allocated in a similar manner, which, in some industries with complex sales models (such as those selling through intermediaries), can be as complicated as the costing model. This approach can help model optimal product and service offerings in terms of packaging, bundling and pricing, and can optimize channel strategies.

Position and Adoption Speed Justification: Profitability modeling has become an increasing focus of some CPM suite evaluations during the past year. Some larger companies are focusing their CPM strategies around an understanding of the drivers of profitability. Also, specialist vendors, such as River Logic, are deploying sophisticated profitability optimization solutions that link constraint-based optimization algorithms with financial modeling capabilities. Other CPM suite vendors are focusing more on profitability modeling: Business Objects purchased ALG Software, and SAP announced a partnership with Acorn Systems. However, there is still a tendency to



deploy these solutions on a stand-alone basis rather than integrate them with the planning processes to create true driver-based planning and budgeting systems, and many public sector organizations still remain focused on "traditional" ABC deployments. This means that profitability modeling is rarely integrated with operational and financial planning at the current time and therefore fails to deliver the full potential benefits of changing the way business performance is understood and managed. However, as the offerings and user adoption in this area mature, we expect profitability modeling and optimization to become a key foundation of most CPM implementations.

User Advice: Profitability modeling and ABC should be considered part of an overall CPM strategy. They can help identify how value is created in an organization. Organizations focusing on customer profitability should consider profitability modeling as one of the ways of measuring customer value, but not the only way.

Business Impact: Profitability modeling and optimization will enable organizations to understand which products, customers and services drive profitability. For commercial organizations, this is crucial to understanding overall financial performance and how shareholder value is created. In public-sector organizations, this helps them understand how effective they are in delivering value to their internal customers and constituents. In all cases, this understanding can lead to new and more-effective ways of conducting business and delivering services.



Maturity: Early mainstream

Sample Vendors: Acorn Systems; Business Objects; Oracle; SAS

Recommended Reading: "How to Assess Whether Activity-Based Costing Is Needed for Customer Value Analysis"

"ABC Provides the Basics of Corporate Performance Management"

Interactive Visualization Analysis By: Kurt Schlegel

Definition: Interactive visualization technology displays numerous aspects of multidimensional data using interactive pictures and charts instead of rows and columns. The color, size, shape and motion of objects in the visual represent the multidimensional aspects of the data. These products provide an array of visualization options that go beyond pie, bar and line charts, often including heat maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting with the visual representation of it. For example, users can filter, drill or pivot the data by clicking on the visual. Another feature of these tools is the use of slider bars to easily filter the data set.

Position and Adoption Speed Justification: Interactive visualization techniques have been used in the academic and scientific communities for years, but have not reached mainstream use in the business world. BI platform vendors are now starting to promote these technologies as a way of differentiating traditional reporting and online analytical processing capabilities. However, internal BI teams lack the knowledge and understanding of how use this technology. Nevertheless, interactive visualization will reach the plateau of productivity in two to five years with the widespread adoption of Flash, Ajax and other Web 2.0 technologies that enable animated, interactive displays of data.



User Advice: Many vendors are promoting their products as easy to use without the help of IT. BI competency centers must ensure that visualization technologies are well-integrated with their BI architecture and easy for the enterprise to use. Just as IT groups are struggling to rein in "spread marts," IT organizations must ensure that visualization applications are supported and developed within established BI governance policies.

Business Impact: Enable more users to perform sophisticated analyses by interacting with visuals instead of learning to use more complex analytical tools.




Sample Vendors: Advizor; FYI; Spotfire; Tableau

Recommended Reading: "Magic Quadrant for Business Intelligence Platforms, 1Q06"

CPM Suites Analysis By: Nigel Rayner

Definition: CPM suites are collections of applications from a single vendor that cover at least three of the main aspects of CPM functionality, namely budgeting, planning and forecasting, scorecarding, profitability modeling and optimization, financial consolidation, and financial and statutory reporting (see "Corporate Performance Management Applications Explained"). These aspects ideally share infrastructure components, such as common functions, shared data, unified metadata and combined business processes. Vendors that provide CPM application suites are covered in Gartner's "Magic Quadrant for CPM Suites, 2006."

Position and Adoption Speed Justification: Vendors' offerings continued to mature during the past year, while the pace of acquisitions has accelerated as vendors seek to strengthen their suite offerings. Business Objects in particular has been a major acquirer, buying ALG Software and, more recently, Cartesis. This indicates another trend, which is the convergence of CPM suites with BI platforms (see "Employ a Coordinated Approach to Business Intelligence and Corporate Performance Management"). Coupled with Oracle's purchase of Hyperion, these acquisitions show the rapidity with which CPM suites are maturing and becoming part of a broader BI and performance management strategy, rather than a specialist "point" solution. Although the CPM suites are maturing rapidly, buyers still lag a little in their purchasing behavior, with most still focusing on point solutions for budgeting, planning and forecasting. However, there is a growing number of larger-scale CPM suite implementations, with more than 2,000- to 3,000-user deployments becoming increasingly common. Consequently, CPM suites are passing through the Trough of Disillusionment and are likely to plateau within two years.

User Advice: Users considering CPM should avoid tactical point solutions and approach CPM strategically. This means considering all areas of CPM functionality during an evaluation and prioritizing them according to business needs. Users should consider CPM as part of a broader BI and performance management strategy and ensure their chosen CPM suite vendor complements established BI tools and technology. Ensure that the focus of any CPM evaluation extends beyond the needs of the finance function. Consequently, vendors should be evaluated according to their CPM breadth and focus, as well as their strategy to deliver other performance management applications to provide an enterprisewide solution. Industry knowledge and capabilities are important here.



Business Impact: CPM applications help to manage performance at the corporate level and create the foundation for an enterprisewide approach. They are key in linking strategy to operational execution; they also leverage BI investments to bring consistency to financial and operational reporting, which can improve corporate governance and help address compliance issues.




Sample Vendors: Applix; Business Objects; Clarity Systems; Cognos; Infor Global Solutions; Longview Solutions; Oracle; OutlookSoft; SAS

Recommended Reading: "Magic Quadrant for CPM Suites, 2006"

"Corporate Performance Management Applications Explained"

"Employ a Coordinated Approach to Business Intelligence and Corporate Performance Management"

Business Application Data Warehouses Analysis By: Bill Hostmann; Andreas Bitterer; Kurt Schlegel

Definition: Business application data warehouses are BI and data warehousing capabilities that have been pre-built and pre-integrated for use with a business application vendor's offerings, such as enterprise resource planning, customer relationship management or supply chain management. Business application data warehouses are packaged analytic applications and consist of pre-defined data extractors, such as ETL functions, from the underlying business application; pre-defined data models; and pre-defined business content, including report templates, pre-defined queries, cubes, key performance indicators, business rules and workflows tailored for business roles. These data models are often the basis for corporate performance management offerings from business application vendors.

Position and Adoption Speed Justification: Through 2007, there has been a significant adoption of these packages, most often from SAP and Oracle. Extensive customer feedback shows that they are not easy to implement and roll out. For SAP, it is difficult to get the performance that was initially assumed without the BI accelerator option.

User Advice: Evaluate specific, packaged BI and performance management applications from business application vendors. Do not assume they will meet your needs in terms of types of user or analysis supported, flexibility and customizing, or scale and performance. It is important to define the role that packaged BI solutions will play in your enterprise's overall BI strategy. The key decision is whether to use these solutions as the enterprise data warehouse or just as a data mart for a particular subject area.

Business Impact: This technology affects accelerated development and deployment of BI and performance management applications.




Sample Vendors: Oracle; SAP



Excel as a Business Intelligence/CPM Front End Analysis By: Nigel Rayner

Definition: Here, we delineate the use of Excel as one of the information delivery and output capabilities of BI platforms, as well as its use for entering data into and manipulating results from CPM applications. This functionality is provided by vendors as an add-in to Excel, or as an integral part of the technology used to build their CPM applications (for example, Microsoft's PerformancePoint Server 2007). This gives end users a secure and controlled way to use Excel for data entry and retrieval.

Position and Adoption Speed Justification: In the past, many users created CPM applications on a stand-alone basis, employing Excel because of its ease of use and its formatting capabilities. In response to this, most vendors provide Excel add-ins for their CPM applications and BI platforms, or integrate with Excel as an output capability for data extracts and additional reporting. Some CPM vendors have based their technology platforms on Microsoft products and use Excel to implement and extend their CPM solutions. Some BI platform vendors offer spreadsheetlike capabilities that provide much of the same functionality, as well as the look and feel of Excel, but integrate them as native components of their BI platform server capabilities. This emulates the capabilities of Excel, but, increasingly, users are demanding native Excel functionality rather than something that only feels like it.

Throughout the past year, vendors have continued to enhance and extend their Excel integration, but the biggest shift in maturity has been Microsoft's delivery of Excel 2007 (along with Excel Services), which is designed to be better-suited for BI (see "Microsoft Strengthens Business Intelligence and Performance Management Offerings"). Furthermore, Microsoft announced that its CPM suite, PerformancePoint Server, is planned to be available by the end of September 2007; this will heavily leverage Excel. These developments rapidly will increase the adoption of Excel as part of a BI and performance management strategy, rather than as a stand-alone alternative.

User Advice: The Excel integration capabilities of BI platforms and CPM applications should be part of any evaluation. This represents an ideal opportunity to reduce an organization's reliance on uncontrolled spreadsheets. The BI/CPM vendor offerings that support Excel differ widely; so carefully evaluate the management, security and administration of these capabilities. Users evaluating CPM vendors that make heavy use of Microsoft technology should identify how the release of PerformancePoint Server will impact their product road map. IT organizations should ensure that all stand-alone, Excel-based in-house CPM applications are put on a road map for replacement with packaged CPM applications. It is no longer an acceptable practice to allow end users to develop these applications natively in Excel.

Business Impact: Using Excel integrated with BI platforms and CPM applications, rather than on a stand-alone basis, will have a beneficial effect on information quality, because it will reduce the amount of analysis performed by users in spreadsheets that are not directly connected to underlying data sources. Using integrated Excel also will have a positive impact on compliance, because the proliferation of uncontrolled (and, therefore, difficult-to-audit) spreadsheets is one of the most common failings reported in Sarbanes-Oxley disclosures.




Sample Vendors: Business Objects; Clarity Systems; Microsoft; OutlookSoft



Recommended Reading: "Microsoft Strengthens Business Intelligence and Performance Management Offerings"

Climbing the Slope Real-Time Data Integration Analysis By: Bill Gassman

Definition: Real-time data integration involves the low-latency detection, processing and loading of events and data changes from multiple sources into one or more destinations (dataset or application).

Position and Adoption Speed Justification: "Real time" describes latency requirements that range from milliseconds to as much as 15 minutes, depending on the nature of the application and business process. The technology to reduce latency in data integration has matured to an early mainstream state, with new solutions and increased interest year over year.

Real-time replication of data without transformation is a stable technology. There is a plethora of agents to poll application data on demand. Change data capture agents for database management systems now tie into real-time messaging systems, which allows rapid introduction of data changes into the data integration process. Record-level transformation and quality verification can be accomplished in-line, using service-oriented calls to these functions. In addition, an enterprise service bus messaging backbone can publish transformed messages to application subscribers. Application tagging along with network probes are being used to capture Web site interactions as they occur, and this approach is expanding to other applications. Drivers for real-time data integration include application data synchronization, process-driven business intelligence and real-time analytics, both within an enterprise and between enterprises — for example, knowing what a customer did on your Web site in the last few minutes, while you are on the phone with the customer.

Although there are challenges to adopting real-time data integration, it is quietly gaining momentum within enterprises. Additional adoption will be driven by lower prices for data integration tools that can address real time requirements, new applications that can process and display in real-time, and more business requirements for up-to-date information.

User Advice: Tie each request for low latency to a specific business objective, rather than making all data integration low latency. Address requirements early in the development cycle because it is easier to architect low latency than it is to retro-fit for it. Minimize the number of agents and adapters by looking for integration vendors which support multiple integration technologies that can service a range of latency requirements. Monitor the presence, timeliness and accuracy of data movements that have low-latency requirements. Build applications to detect and adapt to latency failures in the data integration layer. Consider application performance issues at all stages of data movement.

Business Impact: Real-time data integration improves decision support accuracy, synchronizes cross-organizational knowledge, drives "opportunity and threat" models at a finer-grained resolution, is useful for process monitoring and maintains application synchronization for high-availability.






Sample Vendors: DataMirror; IBM; Informatica; Information Builders; Oracle; Tibco Software

Recommended Reading: "Options Proliferate for Real-Time Data Integration Technology"

Dashboards/Scorecards Analysis By: Neil Chandler; Andreas Bitterer

Definition: A scorecard or dashboard will help improve decision making by revealing and communicating a greater insight into business performance:

Dashboards display KPIs or business metrics using intuitive visualization, including dials, gauges and traffic lights that indicate the state of various KPIs against targets. They enable users to drill down to successive levels of detail to explore why a KPI may be off target.

Scorecards have all the capabilities of dashboards, but also enable users to link KPIs in a strategy map with hierarchical cause-and-effect relationships between the KPIs. These often support specific performance management methodologies, such as the balanced scorecard.

Position and Adoption Speed Justification: Dashboards/scorecards provide a presentation layer for BI tools that is visually attractive to users. However, they are often implemented as tools that are not properly connected with the underlying data sources and systems. In these circumstances, they will fail to deliver much benefit and will fall into disuse (much like the executive information systems of 10 to 15 years ago). Increasingly, users are realizing that dashboards/scorecards are only of value when they are implemented as part of a broader BI and performance management strategy. During the next 12 months, many companies will continue to implement these on a stand-alone basis. Dashboards are more widely adopted than scorecards because they are easier to implement and do not require aligning KPIs with strategic objectives.

User Advice: Dashboards/scorecards should form part of performance management initiatives because they are good formats for presenting financial and nonfinancial information to senior executives. Most of the factors that make a successful dashboard or scorecard implementation are technology-independent, such as defining and measuring the right metrics, ensuring sufficient senior management involvement and considering the deployment as part of a wider BI initiative.

Business Impact: Dashboards/scorecards can make it easy for senior executives and business users to quickly understand how the organization is performing against its business objectives. They can be deployed at any level of the organization, and are good tools for fostering discussion about action plans to achieve goals and targets. They can also be used to promote collaboration outside the enterprise by sharing KPIs with customers, suppliers and partners. When widely adopted in an organization, scorecards are an effective way to lead an organization and align people and resources to meeting strategic objectives. This can have a significant positive impact on corporate performance.



Maturity: Mature mainstream

Sample Vendors: Business Objects; Cognos; CorVu; Hyperion Solutions; Information Builders; SAS

Recommended Reading: "Scorecard or Dashboard: Does It Matter?"

"Recommendations for Implementing Successful Scorecard and Dashboard Initiatives"



Web Analytics Analysis By: Bill Gassman

Definition: Web analytics are specialized reporting and analytical tools that are used to understand and improve Web site visitor acquisition and actions. The tools are used by marketing professionals, content developers and the Web site's operations team.

Position and Adoption Speed Justification: The market consolidated during the past two years to the point that fewer than 10 vendors represent over 90% of the market revenue. Overall revenue is growing at greater than 40%. The Google Analytics offering, free for moderate use, is changing the low and mid levels of the market with its user base of over 100,000 users. This is educating the market and helping to drive high-end sales. The leaders have rapidly growing technology partner programs, and each is building an "ecosystem" of Internet-based marketing firms tied together with an analytics platform. Partner integration includes data and process, and in some cases, analytics are tied, in real time, with Web content management systems. Delivery of Web analytics solutions continues to be biased toward SaaS rather than in-house products. Over 80% of total revenue comes from a SaaS subscription model with several leading vendors bringing in 100% of their revenue that way. The biggest challenges that the market faces as it moves toward the plateau of productivity are user maturity, keeping up with Web 2.0 requirements, support of portals and packaged applications, and the lack of instrumentation standards.

User Advice: Most enterprises with a Web site use a reporting package, but there is a gap between basic reporting and the potential value that the analysis features in the tools offer. The strategic value of an enterprise's Web site can act as a guide for how much investment in skills and process is warranted. Business users should be the primary users of the tools, with support from the IT organization in the areas of instrumentation, data integration, process management and complex report generation. If the currently used tools are over 18 months old, perform a strategy review and evaluate them. Consider using a consulting group (external or vendor) to accelerate or re-energize the use of Web analytics. Those with ongoing programs should make sure they have the appropriate skills for site and marketing campaign optimization, and then start looking toward integrating cross-channel data, such as the call center. For advanced enterprises, start building a user-experience management ecosystem that blends analytics with search, content management and outbound marketing.

Business Impact: Using Web analytics has significant implications for marketing enterprises. They can collect, analyze and monitor customers' behavioral activities on Web sites. The results of e-mail campaigns, cross-sell or upsell targeting, and search engine optimization can be measured and refined through Web analytics. Customer data can be gathered and incorporated into marketing campaign decisions (such as profitability analysis and segmentation), and leveraged for every interaction channel in a campaign management strategy. It is not uncommon for business metrics of Web channels to double over baseline within six months of starting a Web analytics program. It takes as much as three years to achieve advanced skills, at which point a continuous improvement process is in place.




Sample Vendors: ClickTracks; Coremetrics; Google; Nedstat; Omniture; SAS; Unica; Visual Sciences; WebTrends

Recommended Reading: "MarketScope for Web Analytics, 2Q06"



"How to Choose an Advanced Solution for Web Site Analytics"

"World-Class Building Blocks for Multichannel Campaign Management"

"Campaign Management Needs E-Marketing Functionality"

Data-Mining Workbenches Analysis By: Gareth Herschel

Definition: Data-mining workbenches address a diverse range of data-mining needs, rather than a specific application requirement. They provide a selection of analytic functions and mining processes from which analysts can model virtually any data for predictive or exploratory insights. Data-mining workbenches also facilitate the data preparation steps that need to be performed prior to analytic modeling.

Position and Adoption Speed Justification: The technology is mature, with a long history and widespread sets of best practices. The integration of text mining (to expand the scope of the data available for analysis) with database platforms that improve the efficiency of data access and model deployment activities indicates a technology that is increasingly being incorporated into the mainstream of enterprise analysis.

User Advice: Organizations should compare data-mining workbenches with the emerging class of data-mining applications (also known as predictive analytics). Data-mining workbenches are mature, with significant numbers of experienced users and implementation consultants. Data-mining workbenches support extensive reuse: The same tool that currently analyzes the risk of customer churn can be used in the future to analyze credit defaults or perform warranty claim analysis.

In contrast, data-mining applications have user interfaces and a subset of functionality designed to deliver rapid return on investment regarding a specific business issue. These applications are typically linked to processes in specific business functions, such as marketing, logistics or the finance organization. Most organizations will eventually rely on a combination of data-mining workbenches and applications, identifying the correct portfolio of tools and the number of applications that will be needed to complement the "core" data-mining workbench. Such determinations will differentiate enterprises' adoption and use of this technology.

Business Impact: When used effectively, data-mining workbenches can have significant impact. Data mining is most useful when attempting to understand large volumes of data with multiple factors involved in determining the outcome. As such, it is used extensively in situations such as the analysis of customers (for marketing, retention and risk assessments), products (for product development, quality control, and support) and corporate activities, such as identifying key performance indicators and forecasts for performance management activities.

Data-mining workbenches are particularly useful when dealing with heterogeneous types of analysis —multiple issues from different parts of the enterprise in which there may not be a "standard" approach to the analysis, or in situations with heterogeneous data sources (text analysis combined with clickstream analysis and demographic data). Data-mining workbenches offer enterprises the greatest potential for discovering unique insights unavailable to competitors. Although they come with a correspondingly higher level of difficulty, compared with packaged data-mining applications, they provide valuable assistance in assembling the correct population of skilled users and deploying the results of the analysis to effect business change.


Market Penetration: More than 50% of target audience




Sample Vendors: Angoss Software; Insightful; KXEN; Microsoft; Oracle; SAS; SPSS; Teradata (a division of NCR)

Data Quality Tools Analysis By: Andreas Bitterer

Definition: Data quality is the process and technology for identifying and correcting flaws in the data that supports operational business processes and decision-making. Packaged tools are available, which include a range of critical functions for data-quality initiatives such as profiling, cleansing, matching, enrichment and monitoring.

Position and Adoption Speed Justification: Data quality has long been overlooked as a critical factor in successful BI, CRM or any other enterprise application scenario. Many organizations are making the connection between accurate data and good decision-making, process efficiencies and increased revenue. As a result, they are beginning to focus strongly on data quality within their BI, data warehouse or CRM initiatives. Regulatory compliance is a particularly important driver in deploying data quality tools. Therefore, speed of adoption will increase and the Plateau of Productivity will be reached in less than two years.

User Advice: Identify problem areas in data quality and assess the corresponding impact. Look into data profiling, cleansing, matching, validation and enrichment technologies to increase the value of corporate data. Make data-quality tools part of the corporate software portfolio, and leverage profiling and cleansing capabilities in more than one corporate initiative, such as BI, business-to-business data exchange and application integration. Without complete and accurate data, making critical decisions based on data warehouse and BI applications will be flawed, and operational processes, such as in CRM applications, will not deliver the expected efficiencies.

Business Impact: BI and data warehousing, or any other corporate application initiative, may fail without a solid focus on data quality. Without trust and confidence in the data, acceptance by business users will be limited and benefits will not be achieved. Organizations should refrain from trying to cleanse data through their own development efforts (for example, with COBOL or C++ routines), but should redeploy and train developers to leverage available toolsets from the market, which are mature and typically find more data issues than custom applications. In addition, organizations are often unable to keep up with international postal standards or address changes, and so on, while tool providers do this automatically.




Sample Vendors: Business Objects; Datactics; DataFlux; DataLever; Datanomic; Fuzzy Informatik; Group 1 Software; Human Inference; IBM; Informatica; Innovative Systems; Trillium Software; Uniserv

Recommended Reading: "Organizing for Data Quality"

"Gartner's Data Quality Maturity Model"

"Strategic Focus on Data Quality Yields Big Benefits for BT"

"Data Quality Methodologies: Blueprints for Data Quality Success"



Budgeting, Planning and Forecasting for CPM Analysis By: Nigel Rayner

Definition: Planning, budgeting and forecasting are the most commonly deployed aspects of CPM. It includes strategic planning, financial budgeting and high-level operational planning. CPM applications also include a sophisticated financial model that enables users to create multiyear financial forecasts based on plan and budget assumptions. This differentiates CPM applications from the more generic planning and forecasting capabilities sometimes found in business intelligence tools. Although CPM applications can't meet the needs of specific operational planning functions (such as supply chain planning or marketing campaign planning), they should be deployed to provide links between the aggregated, corporate financial budget and operational business plans.

Most CPM budgeting, planning and forecasting applications support a driver-based approach to planning, which links key operational planning drivers (such as discounts, sales volumes and unit prices) to financial budget amounts and classifications. This provides a framework for an enterprisewide approach to planning. CPM applications also have capabilities that support planning from multiple perspectives — for example, top-down, high-level planning (which sets goals at a corporate/business unit level and allocates these to lower-level organizational elements) and bottom-up budgeting (which creates corporate budgets by aggregating lower-level organizational unit budgets). Most applications include powerful forecasting and modeling capabilities, coupled with the ability to maintain an audit trail of changes.

Position and Adoption Speed Justification: Budgeting, planning and forecasting applications from CPM vendors are mature and sophisticated in functionality, and this area continues to be the driver for most CPM evaluations. However, user adoption still focuses too much on replacing Excel-based financial budgeting processes and doesn't pay enough attention to strategic or operational planning. Progress is being made, with some leading organizations implementing planning, budgeting and forecasting as part of a broader approach to performance management, rather than a finance-specific function. It is still common, however, to find large and midsize companies relying heavily on Excel for budgeting, planning and forecasting, but most CFOs are aware that this is not a suitable platform for such an important corporate process. Microsoft's pending release of the PerformancePoint Server will encourage further deployment of CPM applications to support budgeting processes (see "Microsoft Announces Performance Management Applications").

User Advice: Users immediately should replace Excel-based systems or manual processes with budgeting, planning and forecasting applications from CPM vendors. Many sophisticated solutions are available to address financial budgeting needs and to support an enterprisewide approach to planning. CIOs can ensure that the implementation does not focus solely on the needs of the finance function by verifying that other functional and line-of-business needs also are represented properly. CFOs and finance professionals must become more mature in their deployment strategies and must extend their focus beyond the needs of the finance department.

Business Impact: The process of budgeting, planning and forecasting is slow and unresponsive in most organizations, and it involves a lot of clerical effort by managers and accountants. Implementing budgeting, planning and forecasting applications from CPM vendors will reduce the manual effort required to prepare budgets, shorten planning cycle times and support the adoption of more-proactive budgeting processes (such as the "beyond budgeting" methodology). These applications also will enable the quick and easy re-creation of forecasts and will significantly improve governance by keeping an audit trail of business assumptions, underlying forecasts and forward-looking statements.






Sample Vendors: Applix; Business Objects; Clarity Systems; Cognos; Longview Solutions; Oracle; SAP; SAS

Recommended Reading: "Extend Budgeting and Planning Projects Beyond Finance to Maximize Benefits"

"Microsoft Announces Performance Management Applications"

Business Intelligence Platforms Analysis By: Kurt Schlegel

Definition: BI platforms enable enterprises to build BI applications by providing capabilities in three categories: analysis, such as online analytical processing (OLAP); information delivery, such as reports and dashboards; and integration, such as BI metadata.

Position and Adoption Speed Justification: BI platforms are widely used by most large enterprises to build numerous analytical applications and service most information delivery requests. The BI platform market is well-defined with many established vendors. Most BI platform capabilities such as reports, ad hoc queries, and OLAP are quite mature, without much differentiation across the different vendors' offerings. However, the market is still dynamic because many emerging technologies, such as in-memory analytics, interactive visualization, SOA, SaaS and search, are and will have an effect on how this technology is delivered and which vendors will dominate the market.

User Advice: Enterprises should standardize their BI platform capabilities as much as possible and look to balance BI platform capabilities to deliver analysis, integration and information delivery. To date, most BI platform deployments focus primarily on the information delivery capabilities. Analysis and integration capabilities need to be bolstered.

Business Impact: BI platforms enable users, such as managers and analysts, to learn about and understand their business. Increasingly, BI platforms will be used by a wider audience inside and outside the enterprise. In addition, BI platforms will have a dramatic effect on the business by changing the focus from primarily reporting to include process optimization and strategic alignment.


Market Penetration: More than 50% of target audience


Sample Vendors: Actuate; Business Objects; Cognos; Hyperion; Information Builders; Microsoft; MicroStrategy; Oracle; SAP; SAS

Recommended Reading: "Magic Quadrant for Business Intelligence Platforms, 1Q07"



Appendices

Figure 3. Hype Cycle for Business Intelligence and Corporate Performance Management, 2006



Technology Trigger

Peak ofInflated



time

visibility



As of July 2006

Financial Consolidation

Applications

Business Intelligence PlatformsPlanning, Budgeting and Forecasting

Data-Mining WorkbenchesWeb AnalyticsData Quality Tools

Business Application Data WarehousesSpreadsheet-Based Business Intelligence/CPM

Dashboards/ Scorecards

Excel as a Business Intelligence/CPM

Front End

Advanced Visualization

Profitability Modeling and OptimizationCPM Suites

CPM and ComplianceReal-Time Decisioning


SOA-EnabledBusiness Intelligence

Text Mining

Intangible Assets and CPMEnterprisewide Real-Time CPM

Enterprise Information Management

In-Memory Analytics on 64-Bit Hardware

CPM Infrastructure Components

Open-Source Business Intelligence

Hosted Business Intelligence Search Capabilities for Business Intelligence

Analytical Process Controlling

Technology Trigger

Peak ofInflated



time

visibility

Technology Trigger

Peak ofInflated



Technology Trigger

Peak ofInflated



time

visibility

time

visibility





As of July 2006

Financial Consolidation

Applications

Business Intelligence PlatformsPlanning, Budgeting and Forecasting

Data-Mining WorkbenchesWeb AnalyticsData Quality Tools

Business Application Data WarehousesSpreadsheet-Based Business Intelligence/CPM

Dashboards/ Scorecards

Excel as a Business Intelligence/CPM

Front End

Advanced Visualization

Profitability Modeling and OptimizationCPM Suites

CPM and ComplianceReal-Time Decisioning


SOA-EnabledBusiness Intelligence

Text Mining

Intangible Assets and CPMEnterprisewide Real-Time CPM

Enterprise Information Management

In-Memory Analytics on 64-Bit Hardware

CPM Infrastructure Components

Open-Source Business Intelligence

Hosted Business Intelligence Search Capabilities for Business Intelligence

Analytical Process Controlling




Hype Cycle Phases, Benefit Ratings and Maturity Levels

Table 1. Hype Cycle Phases

Phase Definition

Technology Trigger A breakthrough, public demonstration, product launch or other event generates significant press and industry interest.

Peak of Inflated Expectations During this phase of overenthusiasm and unrealistic projections, a flurry of well-publicized activity by technology leaders results in some successes, but more failures, as the technology is pushed to its limits. The only enterprises making money are conference organizers and magazine publishers.

Trough of Disillusionment Because the technology does not live up to its overinflated expectations, it rapidly becomes unfashionable. Media interest wanes, except for a few cautionary tales.

Slope of Enlightenment Focused experimentation and solid hard work by an increasingly diverse range of organizations lead to a true understanding of the technology's applicability, risks and benefits. Commercial, off-the-shelf methodologies and tools ease the development process.

Plateau of Productivity The real-world benefits of the technology are demonstrated and accepted. Tools and methodologies are increasingly stable as they enter their second and third generations. Growing numbers of organizations feel comfortable with the reduced level of risk; the rapid growth phase of adoption begins. Approximately 20% of the technology's target audience has adopted or is adopting the technology as it enters the Plateau.

Years to Mainstream Adoption The time required for the technology to reach the Plateau of Productivity.

Source: Gartner (January 2007)

Table 2. Benefit Ratings

Benefit Rating Definition

Transformational Enables new ways of doing business across industries that will result in major shifts in industry dynamics

High Enables new ways of performing horizontal or vertical processes that will result in significantly increased revenue or cost savings for an enterprise

Moderate Provides incremental improvements to established processes that will result in increased revenue or cost savings for an enterprise



Benefit Rating Definition

Low Slightly improves processes (for example, improved user experience) that will be difficult to translate into increased revenue or cost savings

Source: Gartner (January 2007)

Table 3. Maturity Levels

Maturity Level Status Products/Vendors

Embryonic In labs None

Emerging Commercialization by vendors Pilots and deployments by industry leaders

First generation High price Much customization

Adolescent Maturing technology capabilities and process understanding Uptake beyond early adopters

Second generation Less customization

Early mainstream Proven technology Vendors, technology and adoption rapidly evolving

Third generation More out of box Methodologies

Mature mainstream Robust technology Not much evolution in vendors or technology

Several dominant vendors

Legacy Not appropriate for new developments Cost of migration constrains replacement

Maintenance revenue focus

Obsolete Rarely used Used/resale market only Source: Gartner (January 2007)

RECOMMENDED READING

"Magic Quadrant for Business Intelligence Platforms, 1Q07"

"Magic Quadrant for Business Intelligence Services, North America, 2007"

"Magic Quadrant for CPM Suites, 2006"

"Magic Quadrant for Data Quality Tools, 2007"

"Business Intelligence Platform Capability Matrix"

"Understanding Gartner's Hype Cycles, 2007"



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