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Business Intelligence IT Strategy Report

Description:
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.
 
Contents:
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
 
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