Reportlinker Adds Business Intelligence IT Strategy Report

Mar 24, 2011, 07:04 ET from Reportlinker

NEW YORK, March 24, 2011 /PRNewswire/ -- Reportlinker.com announces that a new market research report is available in its catalogue:

Business Intelligence IT Strategy Report

http://www.reportlinker.com/p0460794/Business-Intelligence-IT-Strategy-Report.html

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

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Nicolas Bombourg
Reportlinker
Email: nbo@reportlinker.com
US: (805)652-2626
Intl: +1 805-652-2626

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