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Technology Executives ClubEmerging Trends in Business Intelligence July 2009

© 2009 KPMG LLP, a U.S. and a member firm of the KPMG network of independent member firms affiliated with KPMG International, a Swiss cooperative. All rights reserved. KPMG and the KPMG logo are registered trademarks of KPMG International, a Swiss cooperative.

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Agenda

• Speaker Introduction • Today’s Environment• Emerging Trends and the Pragmatic Approach to Application

– Data Visualization– Predictive Analytics– Reporting on Unstructured Data

• Social Media Analytics– BI Enterprise Rationalization

• An Enterprise BI Framework• Case Study: BI Enterprise Rationalization• Questions• Contact Information


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Agenda






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Introductions

George HaenischSenior ManagerKPMG [email protected] (office)630-240-2130 (cell)

George has over 14 years of leadership and extensive technical experience in Business Intelligence and Performance Management.

He has assisted clients with Financial and Operational Planning, Budgeting and Reporting, Data Warehousing, Enterprise Resource Planning, Enterprise Architecture, IT Strategy, Customer Relationship Management, Supply Chain Management, Service Oriented Architecture, and Application Development.

Thomas is a CPA and MBA, with has over 14 years in financial reporting, systems integrations, and financial process controls. He specializes in financial data analysis and BI deployments and leads the Midwest’s practice around SAS 99 and financial data analytics.

Since starting with KPMG in 2006, Thomas’s work experience has included the use of data analytics for the collection, management and reporting of financial, accounting, and operational-level data.

Thomas A MatarelliManagerKPMG [email protected] (office)312-545-6290 (cell)

mailto:[email protected]

mailto:[email protected]


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Agenda






Today’s Environment

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1 Source: Accenture study of over 1,000 managers of large companies in the UK and US, January 20072 Source: IBM CFO Study, December 20053 Source: Bill Inmon, “Information Management: Charting the Course: Little White Lies,” DM Review, August 2001 4 Source: R Kaplan and D P Norton, “Creating the Office of Strategy Management”, Harvard Business Review, April 2005

• More than 50% of the information [Managers] obtain has no value to them 1

• 69% of all CFOs rank Measuring/Monitoring Business Performance as their top priority 2

• Data Warehousing projects have a 70-80% failure rate 3

• Organizations often fail to execute their strategy – failure rates may range from 60-90%. 4

Enterprises today are starved for information to manage their business


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Gartner Predictions for Business Intelligence (2009)

In 2009o Collaborative decision-making will emerge as a new product category that combines social

software with BI platform capabilities

By 2010

o 20% of organizations will have industry-specific analytic application delivered as software- as-a service

By 2012

o Business Units (not IT) will be held responsible for more than 40% of the total budget for BI projects

o More than 35% of the top 5000 global companies will make uninformed decisions due to underinvestment in information infrastructure and business-user tools.

o 1/3 of analytic applications applied to business processes will be delivered through large- grained application mashups

5 Source: L. McKay, “Gartner Gives BI a High 5”, destinationCRM.com, February 2009

“The economic crisis will reveal which enterprises have a sound information infrastructure and which do not” 5


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Agenda






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Emerging Trends

Data VisualizationDefinition: The communication of information through graphical means. Historically, data visualization was achieved through executive dashboards with graphs and widgets, but it includes any graphic that conveys information.Benefits• Simplifies interpretation of data for

non-technical users• Enables efficient identification of trends

in performance over time• May emphasize points of concern for

management and enable timely responses

Source: Time Magazine


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Emerging Trends Pragmatic Application

Pragmatic Application of Data Visualization1.Pilot Effectively: Identify a subset of users that are currently doing analytical work. This small community can become the change agents for this new tool, it will also provide a community of experts to leverage when rolling out the technology to a broader user group.2.Data Linkage and Network Analysis : Link analysis has been used extensively in the public sector. Crime reduction and national security have been two areas where the process has been quite successful, which has lead to a mainstream analysis model for revealing relationships and associations that are often hidden in corporate data. Rich, interactive relationship diagrams are a key part of a complete visualization solution with the ability to bundle networks of similar objects to assess disambiguity and visualize large data sets.3.Geospatial mashups: Geospatial analysis is becoming critical to virtually every application domain. Visualization should include integrated geospatial analysis through integration with major third party providers such as Google, ESRI and Microsoft.4.Training :It is important to remember that changing an analytic process implies changing the way that people have been working. Comprehensive training will be a critical success factor to the adoption of next generation approaches.


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Emerging Trends

Predictive AnalyticsDefinition: A Set of BI techniques that uncovers relationships and patterns within volumes of data to make predictions about future behaviors based on current and historical data. Predictive Analytics is forward-looking. Common examples include the credit scoring system and insurance underwriting.Benefits• Enables proactive decisions instead

of only reacting to past events• Enables management to anticipate

negative events and lessen their impact on the business

• Improves effectiveness of marketing campaigns by predicting customer behavior and targeting appropriately


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Pragmatic Application of Predictive Analysis1. Utilize a methodology for the program: 25% of the effort is data preparation, 18% is data

exploration, and 25% is data modeling (6) so following a plan to stay on course2. Utilize business analysts that understand the interplays of variables across narrow

business processes and can model the business, for example…Source Risk ResponseMachine maintenance Breakdowns Preventative maintenance programsCustomer payments Bank loan defaults Early detection programsCustomer purchases Ineffective marketing Cross sell/upsellPurchase history Fraud Purchasing red flags

3. Integrate the business analysts (modelers) into the BI Team and prioritize their requirements: satisfy their insatiable need for data access, powerful tools, and capacity

4. Leverage the enterprise data warehouse: utilize a single source to prepare and store the data

5. Demonstrate the value to the business! Predictive analysis that can forecast trends will require a high degree of confidence; consider parallel modeling to provide the results

6 – Source: TDWI “Extending the Value of Your Data Warehouse Investment” by Wayne W Eckerson


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Emerging Trends

Reporting on Unstructured DataData can be described as follows:

Structured: the extreme of the continuum, is found in relational databases, often with standard tables and fields accessed via SQL

Semi-Structured: data stored in spreadsheets, flat files, EDI, RSS feeds and XML documents

Unstructured data: the other extreme, these are mostly documents like word processing files, reports, emails, web portals, instant/test messaging, voice recognition, and wikis

Unstructured and Structured Data in Warehouses Today

Figure 1. 7 From TDWI study of 370 respondents.

• A recent study by TDWI (7) indicates 47% of a companies’ data is structured (and available to traditional BI platforms) while 53% is unstructured or semi-structured, indicating that important information is missing from enterprise data and BI

• Companies are trending towards more unstructured data sources• Therefore, the enterprise BI solution may not be complete without a strategy to

incorporate the information contained in semi and unstructured data sources7 – Source: TDWI Quantifying the Data Continuum By Phillip Russom


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Pragmatic Application for Utilizing Unstructured Data1. Provide meaning to unstructured/semi-structured data:

• Change the way data is modeled via metadata tags, ETL, parsing, and attributing to ‘structure’ the data in include into the enterprise data warehouses and BI reporting

2. Provide tools for text analytics: where software “transforms” the text i.e. parses text and extracts facts (names, accounts, complaints) about important entities (employees, products, customer) and stores them into a structured format

3. Investigate BI-search capabilities:– BI tools are now allowing Google-like search for documents, reports, and data artifacts that

may be useful to the user– Discovers multiple possibilities rather than exact answers– Increases ease of use; BI applications tend to be rather complex; BI Search can be used by

novices to find information quickly– Anyone who browses the web can associate search with unstructured data – finding files, web

pages, and documents4. New vendor technologies: Vendors are now offering tools to search unstructured/semi-structured

data and combine it with other data for analysis. Intelligent Search can search multiple sources at once – including Web sources (Google, Yahoo, etc.), subscription sources (LexisNexis, Factiva), and custom/internal sources (connectors can be written to anything with a search box or API)


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Emerging Trends

Social Media Analytics – An Example of Unstructured DataDefinition: The use of business intelligence technologies to identify, track, and participate in web conversations about a particular brand, product or issue, with emphasis on quantifying the trend in each conversation's sentiment and influence.Benefits• Enables tracking and participation in

web conversations about your product or service

• Attempts to quantify brand loyalty and public perception of your product

• Provides timely market research and insight for new product development


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Pragmatic Application for Social Media Analytics1. Look in the mirror: identify the tools you already have. Active blogs, community sites,

internal forums, and external forums are the first place to start2. Website Statistics: gigabytes of clickstream, usage, page view, and other web stats

are sitting idle on your web servers (or your outsourced providers). Request the data, and start to integrate with corporate communications and marketing objectives

3. Buy a tool: multiple offerings are available from purchasing to hosting, to one time projects to assess the data. For a product or service based company, you can quickly identify ‘sentiment analytics’ to see your product of brand is perceived in the marketplace, without using traditional focus groups or surveys.

4. Do an internal study of usage: you will be surprised how often internal data and intellectual property will ‘leave’ your companies walls.

5. Update your internet usage policy: by assessing your current environment you will have the support to make an effective modification to not you usage polices to not only protect your assets, but to leverage new business models.


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Emerging Trends

BI Enterprise RationalizationDefinition: Implementing BI standards to consolidate overlapping tools, technologies, lower costs, and drive an enterprise strategy that maximizes the benefits of BI

Marketing

Sales

Finance

Other Divisions

Manufacturing

BI Product 3

OLAP Analysis 2

Data warehouse 1

Data Mart 1

BI Product 1

Dashboard 1

OLAP Analysis 1

Data warehouse 2

BI Product 2

A Typical Company Portfolio• Its it not uncommon for a company to have 6+

BI, ETL, and data warehouses vendors, and different solutions for various departments

• BI solutions may be localized, with multiple IT and business groups planning unique solutions

• The result is multiple, overlapping technologies• Multiple versions of the truth and excessive

support costs • Data elements are used in multiple systems• The company lacks an enterprise strategy• Costs of fragmentation are increasing• No enterprise wide strategy, and not linked to

major initiatives


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Pragmatic Application for BI RationalizationIdentifying a corporate imperative such as ERP consolidation (or new implementation) as a stimulus to create an enterprise BI strategy and implementation roadmap which consolidates and centralizes BI into a standard integrated stack.

Marketing

Sales Finance

HR

Manufacturing

Benefits of Rationalization1. Consolidate vendor applications2. Reduce costs and realize ROI, economies of

scale, enterprise licenses agreements3. Provide an true enterprise view by combining

multiple systems4. Consistency across applications5. Increase user capabilities6. Create centers of excellence (specialization)7. Increase control over the underlying information

via an enterprise grade infrastructure8. There are barriers to rationalization -

organizations need a plan St

anda

rd, I

nteg

rate

d St

ack

Data Marts

Data Warehouse

Executive Dashboards

OLAP/BI/Search

Enterprise


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Agenda






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An Enterprise Business Intelligence Framework

Frameworks help communicate a multi-dimensional .view of Business Intelligence


An Enterprise Business Intelligence Framework How the Framework is Used

Enterprise Frameworks help organizations:• Drive organizations to address required

components of Business Intelligence• Help reduce the risk of project failures • Align organizational thinking around the

definition of Business Intelligence

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Agenda

• Speaker Intro and background• Today’s Environment• Emerging Trends and the Pragmatic Approach to Application


• Social Media Analytics• BI Text Search

– BI Enterprise Rationalization• KPMG Framework• Case Study: BI Enterprise Rationalization• Contact Information


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Example Case Studies of Enterprise Rationalization Global Health Sciences Company

Client Background and ChallengeOur client was in the process of consolidating multiple global ERP platforms down to four regional instances. The key benefit of this initiative was increased business intelligence (BI) capabilities, providing transparency in reporting.

In order to achieve this benefit, the client faced numerous challenges:• Lack of a comprehensive strategy for reporting across multiple ERP and other source systems• Lack of alignment between business and IT around enterprise business strategy• Maintenance, support and integration of:

– Multiple disparate Data Warehouses/Marts, and spread marts– Multiple disparate systems and separate reporting environments/strategies– Multiple enterprise application integration tools

• Master data not defined in a centralized fashion or at consistent levels• Inability to drill-down and through to the source data in transaction and operational data stores• Inability to seamlessly integrate supply chain data with financial data


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Example Case Studies of Enterprise Rationalization Evolution of the Strategy

The establishment of the Global Health Sciences Company Global BI Framework and Strategy is the first element in the evolution to the end state

Global Framework

EstablishFoundation

ImplementBI Strategy

ProvideFeedback

Define the FutureIdentify the roadmap:• Assess current

state• Define conceptual

end-state • Define roadmap to

achieve

Prepare for SuccessBuild foundational elements including:• Architecture/tools• Resources/skill sets• Governance and

ownership• Change

Management

Empower UsersThink Big, Act Local• Tactical pilots

driving business results and early success

• On going implementations

EnhanceContinuous Improvement• Capture feedback• Analyze root

causes• Make course

corrections

CommunicateFoster interaction; inform end users of the goals, direction and available functionality of the BI program

Evangelize

Define VisionEstablish the vision:• Establish BI guiding

principles• Provide high-level

direction of BI environment (people, process, technology)


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Business Strategy Alignment

Alignment of Business Intelligence to enterprise strategy will drive consistency through the organization.

Business Strategy Alignment

Obj

ectiv

es a

nd P

erfo

rman

ce

Sense and Respond

Summary

Detail

EventsProcess Enterprise Environment Periphery

Measure and Optimize

Measure and Decide

Align and Manage Strategy Driven

Analysts Driven

Process Driven

Discover and Innovate

Planning, Budgeting, Forecasting

Dashboards and ScorecardsSpreadsheets

OLAP

Management Reporting

Ad-hoc Query

Operational Analytics

BAM

In-Memory

Data Mining

Search &Text Mining

Visualization

Transactional ReportingERP Reporting

BI

Model 1

Model 2

• BI initiatives will align with IT strategies, Business strategies, and Corporate Goals

• BI initiatives will be evaluated based on their alignment, both in-flight/planned and new initiatives

• ERP Transactional Reporting will be pulled directly from ERP; other reporting will be pulled from BI applications


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Governance

Governance provides oversight, direction, and accountability to the enterprise, this is facilitated through a Business Intelligence Competency Center (BICC)

• BICC formalizes BI oversight, governance, and prioritization of initiatives

• BICC supports end-users and promotes BI efforts to the organization

• BICC communicates member activity and data governance standards

• BICC to align with ERP governance while leverage existing knowledge

• BICC will have IT and business representation supported through executive sponsorship

Model 3

Model 4

Governance


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Performance Mgmt Process and Reporting

Metrics and KPI's gauge strategic and operational performance; Building Blocks will foster data transparency through standard, regional, and local BI applications.

• Performance Management process discipline provides aligned metrics for each level of the organization. This defines executive, management, analysis and operational metrics globally, regionally and locally.

• BI Applications which are used by all the regions are defined as “standard” BI applications

• Regional and local BI Applications will be defined based on the standard template

• Reconciliation occurs amongst all applications

Model 5

Model 6

Performance Mgmt Process and Reporting


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Integrated Information Management

Integration Information Management is the process and technology that ensures the data foundation is trusted and traceable to the source.

• A Global BI Data Model will be determined at the global level

• Conformed dimensions transform data into global, regional and local information

• Aligned with the Global MDM initiative, the Master Data System feeds regional and global warehouses with Master Data.

• Metadata is centralized into one Metadata Management application, providing both business and technical data references

• Global Metadata is managed at the global level; region specific metadata is managed at the regional level (including in-region local metadata)

Model 9

Model 10

Model 7 Model 8

Integrated Information Mgmt


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Business Intelligence Platform

The Business Intelligence Platform provides the right information in the right tool through the right channel.

• A Formal Process to determine global BI tools should be implemented• BI application suites• ETL/EAI/EII tools• DW tools

Business Intelligence Platform


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Example Case Studies of Enterprise Rationalization Global BI Framework Definition: Infrastructure Infrastructure

Model 13

Model 8

• Global data warehouse contains summary information from regional data warehouses

• Regional Warehouses contain detailed (transactional) level data from regional source systems

• Global and regional warehouses strive for daily data refresh and are in-sync at summary levels.

• The BI Data model is replicated into each regional warehouse

• Within the warehouse(s), data is organized by Subject Areas to support functional areas

Infrastructure delivers trusted, secure, integrated data infrastructure for analysis and decision making.


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Infrastructure(“Data Warehouse Infrastructure”)

Business Intelligence Platform(“Tools”)

Integrated Information Management(“Data”)

Performance Management Process and Reporting (“Applications”)

Governance

Business Strategy Alignment

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Example Case Studies of Enterprise Rationalization Approach: Establish an Enterprise BI Framework

Current complexity Future BI State

Alignment of MetricsFew Metrics defined, no alignment

BICCNo Global Governance

Some Regional Governance

Standard Building Blocks / ApplicationsNo Standard Applications

One single data model / MDM (Master Data Mgt)

Specific data models. Some Data Masters but not

for all

Standard tools already defined by GIP (Global Information

Platform)

Cognos understood as the standard, but not used in a

consistent way

4 Regional Data Warehouses 1 Summary Global Data

Warehouse

No standards around data warehouse

Leverage

Leverage

Leverage

Complex environment and structure Organized simplified environment

The BI Strategy is adapted from the KPMG enterprise framework


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Example Case Studies of Enterprise Rationalization Approach: Create a Future State Vision

The regional template provides a consistent approach across the regions and a structure that can be leveraged in development. Below is the source to decision model for a typical region


3232

Example Case Studies of Enterprise Rationalization Approach: Create a Roadmap

ArgentinaArgentina

Q1 09 Q3 09 Q4 09 Q2 10Q2 09 Q1 10 Q3 10 Q4 10 Q1 11 Q2 11 Q3 11 Q4 11 Q1 12 Q2 12

ER

P CanadaCanadaAustralia Australia

UCANUCAN

APACAPAC

EUEUGlobal ModelGlobal Model

SpainSpainRegional

ModelRegional

Model

LALA

Building Blocks EnhancementsBuilding Blocks Enhancements

DW EnhancementsDW Enhancements

Data Model CombinationData Model Combination

GDWBuildGDWBuild

DW Template

DW Template

RDW- UCAN DevRDW- UCAN Dev

RDW- LA DevRDW- LA Dev

RDW- APAC DevRDW- APAC Dev

RDW- EU DevRDW- EU Dev

Foun

datio

n

Global BuildingBlocks Dev

Global BuildingBlocks Dev

LA - Building Blocks Dev

LA - Building Blocks Dev

UCAN –BuildingBlocks Dev.

UCAN –BuildingBlocks Dev.

APAC –BuildingBlocks Dev.

APAC –BuildingBlocks Dev.

LA Interim SolutionLA Interim Solution

EU -Building Blocks Dev.

EU -Building Blocks Dev.

BICC initiationBICC

initiation

Sta

ndar

diza

tion

Gov

BICC /Data Gov Build outBICC /Data

Gov Build out

BI Strategy Evangelization and IntegrationBI Strategy Evangelization and Integration

RE

APAC Interim SolutionAPAC Interim Solution

Building Blocks Validation

Building Blocks Validation

Potential EU Interim SolutionPotential EU Interim Solution

UCAN Interim SolutionUCAN Interim Solution

Implemen’nPlan

Implemen’nPlan

MDM & BI CoordinationMDM & BI

Coordination

BICC /Data Gov ManagementBICC /Data Gov Management

BICC Roadshow

BICC Roadshow

A roadmap serves as a guideline for when each element of a BI solution should be implemented


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Example Case Studies of Enterprise Rationalization Expected Outcomes

Client Outcomes• The end of the project was only the beginning of the journey. The client is now in the

process of implementing the ERP, as well as the BI components.• The BI strategy enabled the client to identify the proper stakeholders, and people

structure to understand how to extract value from the ERP consolidation.• It also enabled the unlocking of key data in the organization and modifying the culture to

become more decision oriented, rather than report focused.• The global nature of the project led to greater collaboration between all regions, and

fostered shared innovation across each region.• The enhanced visibility will be realized some time in 2011, however foundational

components and change agents have already set in.


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Agenda






Summary and Conclusion

Multiple trends are emerging and available, there is a pragmatic approach to addressing each one:1.Stay focused on business strategy, and the areas of focus that will help you achieve it.2.BI projects are not just a ‘tool’ implementations.3.Identify the appropriate ‘change stimulus’ to make sure maximum success is achieved4.Look to standardize, consolidate, and organize at an enterprise level5.Utilized an enterprise framework to conceptualize and encapsulate a complex topic.

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Questions?


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Agenda






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The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.

Contact

KPMG LLPSuite 1900303 E. WackerChicago, IL 60601

[email protected]

George HaenischSenior Manager, KPMG LLPAdvisory Services

Office (312) 665-2850Cell (630) 240-2130

KPMG LLP, a Canadian limited liability partnership.

KPMG LLPSuite 1900303 E. WackerChicago, IL 60601

[email protected]

Thomas A MatarelliManager, KPMG LLP

Advisory ServicesOffice (312) 665-1834

Cell (312) 545-6290

KPMG LLP, a Canadian limited liability partnership.

tech executives club webinar kpmg final v72209

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