Business Intelligence IT Strategy Report
Ovum Plc
March 7, 2011 196 Pages - SKU: OV6196630
|
|
Introduction
The demands on business responsiveness and operational speed and flexibility for enterprises competing in today’s economically challenging environment make BI a necessity rather than a luxury. Survival depends on visibility into operations and making the right decisions, and BI initiatives continue to top CIO agendas.
Features and benefits - BI is a growth industry with a predicted spend of over $9.1bn in 2014
- Underlining this growth are significant changes to the way in which BI systems are built and deployed.
- The core business imperatives for implementing and benefiting from BI and analytics software holds firm in both a recession and growth economy.
Highlights
The emerging implementation and technology trends that impact how BI systems are being built, packaged, and deployed. Why new deployment models promise to lower the complexity and cost of implementing BI systems. How predictive analytics can squeeze greater valuable insights from BI data using forward-looking analysis.
Your key questions answered - The business forces and trends that are driving the corporate adoption of BI and analytic technologies today.
- How to identify and evaluate the essential building blocks of a BI and analytics system.
|
- Executive Summary
- 1.1 Executive summary
- Catalyst
- Key findings
- Ovum view
- 1.2 Report objectives and structure
- Chapter 2 - The business imperative for BI
- Chapter 3 - Building a successful BI system
- Chapter 4 - Implementation and technology trends impacting BI
- Chapter 5 - Making BI smarter with predictive analytics
- Chapter 6 - Convergence opportunities for search and BI
- Chapter 7 - Accelerating BI insights in-memory
- Chapter 8 - Understanding event processing and BI analysis
- Chapter 9 - How columnar databases benefit BI
- Chapter 10 - Customer intelligence in retail banking
- Chapter 11 - Improving the telecoms customer experience using BI
- Chapter 12 - BI in the public sector
- Chapter 13 - BI makes the smart utility more intelligent
- THE BUSINESS IMPERATIVE FOR BI
- 2.1 Summary
- Catalyst
- Ovum view
- Key messages
- 2.2 Business trends driving BI and analytics
- Overview
- Rationalizing and reducing operational costs
- Improving the customer management process
- Maximizing operational agility
- Enhancing business performance alignment across the enterprise
- Minimizing risk exposure and ensuring adherence to regulatory compliance
- 2.3 The customer is still king in BI
- Overview
- 2.4 BI is relevant for a bear or a bull economy
- BI is relevant for a bear or bull economy
- 2.5 Recommendations
- Recommendations for enterprises
- BUILDING A SUCCESSFUL BI SYSTEM
- 3.1 Summary
- Catalyst
- Ovum view
- Key messages
- 3.2 Mapping BI technology to business needs
- Think business strategy before technology
- Functional considerations that impact BI technology selection
- 3.3 Anatomy of a BI system
- BI systems are built on a four-layer architecture
- 3.4 Evaluating BI products
- Ovum's evaluation model
- 3.5 Deployment and management considerations
- BI projects can be high risk, but also high reward
- Best practices for implementing BI
- Common barriers and pitfalls
- 3.6 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- IMPLEMENTATION AND TECHNOLOGY TRENDS IMPACTING BI
- 4.1 Summary
- Catalyst
- Ovum view
- Key messages
- 4.2 Enterprise BI user trends
- Enterprises are scrutinizing their current BI suppliers more closely
- Enterprises are looking to standardize on a single BI platform
- Enterprises are pushing to make BI more pervasive across the enterprise
- Enterprises are considering the benefits of setting up a BICC
- Enterprises are finally waking up to the value of location intelligence
- 4.3 Technology trends
- Disruptive technologies that BI cannot ignore
- Cloud computing makes large-scale BI analysis a more cost-effective option
- Open source BI solutions are expanding in functionality
- Enterprise 2.0 offers opportunities to make Bi a collaborative discipline
- In-memory analytics reduces BI latency
- Predictive analytics squeezes greater value from BI investments
- Event stream processing
- Virtualization
- Location intelligence
- 4.4 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- MAKING BI SMARTER WITH PREDICTIVE ANALYTICS
- 5.1 Summary
- Catalyst
- Ovum view
- Key messages
- 5.2 Getting more from your data with predictive analytics
- The business value of predictive analytics
- Predictive analytics has cross-industry benefits
- Market drivers
- Predictive analytics is different from BI
- 5.3 Technology analysis
- What is predictive analytics?
- Predictive techniques and algorithms
- Understanding supervised and unsupervised learning techniques
- 5.4 Implementing predictive analytics
- Predictive analysis is an iterative cycle
- Stage 1: Data preparation
- Stage 2: Data modeling
- Stage 3: Model deployment
- Stage 4: Model management and refinement
- 5.5 Enabling technologies
- Enabling technologies
- Data integration is key
- So too is performance
- 5.6 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- CONVERGENCE OPPORTUNITIES FOR SEARCH AND BI
- 6.1 Summary
- Catalyst
- Ovum view
- Key messages
- 6.2 BI and search convergence
- BI systems are hard wired to work with structured data
- Pulling unstructured data into the analytic mix
- ESR is one response to querying unstructured data
- ESR vendors are slowly adapting to structured analysis
- Expanding the scope of unstructured data analysis
- 6.3 Business benefits of convergence
- Bridging data from disparate applications
- Business use case drivers
- Benefits also extend to vendors from both sides
- 6.4 Integration approaches
- Federated search
- Query transformation
- Guided navigation
- 6.5 Technology options
- Market consolidation is driving convergence
- Examples of ESR-BI consolidation
- Integration is happening at various levels
- ESR vendors
- BI vendors
- Open source solutions
- Security is important
- 6.6 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- ACCELERATING BI INSIGHTS IN-MEMORY
- 7.1 Summary
- Catalyst
- Ovum view
- Key messages
- 7.2 Accelerating time to insight using in-memory analytics
- A faster way to access information
- Hardware advances are making in-memory more viable
- Users have high expectations about information access and response
- Improving self service through analytic flexibility
- Supporting specialized business analytic requirements
- Reducing the IT burden
- 7.3 In-memory BI architectures
- Architectural approaches vary
- 7.4 Do in-memory databases offer anything new?
- The two perspectives of in-memory
- Why is in-memory so much faster?
- In-memory databases - what has changed?
- Cost, performance and functionality benefits will spur uptake
- 7.5 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- UNDERSTANDING EVENT PROCESSING AND BI ANALYSIS
- 8.1 Summary
- Catalyst
- Ovum view
- Key messages
- 8.2 What is complex events processing?
- Brief technology primer
- Parallels and differences with BI
- CEP tools are getting easier to use
- Symbiotic relationship with BI
- Convergence is now happening
- What is the business value that CEP drives?
- A volatile economy points to different use cases
- 8.3 The myths and realities of CEP
- IT users are weary of anything complex
- Myth 1: CEP is a single kind of product
- Myth 2: CEP is complex
- Myth 3: CEP is prohibitively expensive for many organizations
- 8.4 CEP and BI market convergence
- Market development scenario
- Industry examples
- CEP or operational BI?
- Possible convergence scenarios
- Data quality is the Achilles heel of CEP
- 8.5 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- HOW COLUMNAR DATABASES BENEFIT BI
- 9.1 Summary
- Catalyst
- Ovum view
- Key messages
- 9.2 The analytic case for columnar databases
- The difference between row and column based databases
- The rationale for going columnar
- Benefits of columnar database processing
- Columnar critique
- 9.3 Choosing the right columnar database
- Columnar databases are not new technology
- Not all columnar databases are built equal
- 9.4 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- CUSTOMER INTELLIGENCE IN RETAIL BANKING
- 10.1 Summary
- Catalyst
- Ovum view
- Key messages
- 10.2 Market context
- Increased customer satisfaction requires greater customer understanding
- Customer intelligence is key to gaining the required level of customer understanding
- Banks should focus on high retention levels to improve sales and profitability
- Effective profitability analyses require a solid data foundation
- Retention of profitable customers is a major focus area
- Banks must focus on maximizing existing relationships
- Trust is the key element in client retention and acquisition
- Customers are now more likely to change their primary banking services providers
- 10.3 Business focus
- Multichannel integration is required to achieve consistency
- Legacy infrastructure is the biggest challenge to channel integration
- Effective marketing will drive channel utilization
- Banks need technology
- Customer intelligence guides go-to-market strategy
- Access to trusted data is the fundamental requirement for CI
- 10.4 Technology focus
- The goal: Getting a single view of the customer
- Managing customer data involves people, process and technology
- Key Technologies enabling CI
- 10.5 CI Market development
- Demand for CI solutions is expected to increase
- Predictive bank to customer relationship entails coherent data for accurate and full customer analysis
- Customer data yields insight
- 10.6 Recommendations
- Recommendations for enterprises
- Recommendations for vendors
- IMPROVING THE TELECOMS CUSTOMER EXPERIENCE USING BI
- 11.1 Summary
- Catalyst
- Ovum view
- Key messages
- 11.2 Business imperatives for telecoms providers
- Telecoms providers face many challenges
- Knowing and understanding your customer is key
- 11.3 Telecoms data challenges
- Telecoms data is often siloed
- Coping with data explosion
- 11.4. Business benefits of BI for the service provider
- BI helps to break through data silos
- Case study: Orange UK
- Case study: Telstra
- Case study: BT Retail
- 11.5 BI vendors targeting telecoms
- The big four dominate
- Niche players
- 11.6 Recommendations
- Recommendations for telco providers
- BI IN THE PUBLIC SECTOR
- 12.1 Summary
- Catalyst
- Ovum view
- Key messages
- 12.2 BI imperatives in the age of austerity
- Making the right decisions in uncertain times
- Challenges faced by public sector organizations
- 12.3 Unlocking the value of public sector data with BI
- BI leverages increasing data volumes
- BI can help to break public sector data silos
- 12.4 BI benefits
- Public sector organizations are starting to use BI
- 12.5 The state of the public sector BI market
- Macroeconomic downturn has impacted BI spending
- Public sector is relatively unpenetrated by BI
- 12.6 Public sector organizations using BI
- Presenting two cases studies
- 12.7 Recommendations
- Recommendations for public sector organizations
- Recommendations for BI vendors
- BI MAKES THE SMART UTILITY MORE INTELLIGENT
- 13.1 Summary
- Catalyst
- Ovum view
- Key messages
- 13.2 Key business challenges faced by utilities
- Utilities are under pressure to reconcile consumer demand with resources
- Utility pricing is key
- Utilities need to adapt their IT infrastructures
- 13.3 Smart meters and the BI opportunity
- Smart metering
- Smart meters create a huge data analysis opportunity
- Smart grid applications require BI and analytics to create intelligence
- 13.4 Benefits for the utility value chain
- Benefits across the utility value chain
- Retail-side benefits
- Operational benefits
- Benefits for energy trading
- 13.5 Convergence of BI and GIS
- BI and GIS are highly complementary
- 13.6 Recommendations
- Recommendations for enterprises
- Recommendations for BI vendors
- APPENDIX
- Glossary
- Activity Based Costing (ABC)
- ActiveX Data Objects (ADO)
- Analytic Application
- Business Activity Monitoring (BAM)
- Business Process Management (BPM)
- Collaborative Business Intelligence (CBI)
- Component Object Model (COM)
- Common Object Request Broker Architecture (CORBA)
- Corporate Performance Management (CPM)
- Common Warehouse Metamodel (CWM)
- Enterprise Application Integration (EAI)
- Extract, Transform, and Load (ETL)
- Master Data Management (MDM)
- On-Line Analytical Processing (OLAP)
- Straight-Through Processing (STP)
- Further reading
- Methodology
- Author(s)
- Ovum consulting
- Disclaimer
Share this report
Other tasks Related Markets Application Software Reports Free Alert Me service Receive bi-weekly email alerts on new market research Sign Up Today!
|
