Building A Bi Strategy

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Building a Business Intelligence Strategy

Agenda

• What is a BI Strategy?
• Elements of a BI Strategy
• Support Corporate Goals and Metrics
• BI Maturity Model
• Activity Based Business Intelligence
• BICC
• ROI – Build vs. Buy
• Executing on the Vision

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What is a Business Intelligence Strategy?

• A business intelligence strategy begins with a comprehensive view of the

entire organization from both a technical and business standpoint. A true

business intelligence strategy cannot be effective if it does not support

both. The strategy should consist of long term and short term goals but

ensure the short term supports the long term. A comprehensive view

includes data readiness, organizational readiness, build and buy

components, ensuring metrics across the organization are fully

understood and accounted for in the strategy.


What are the elements of a BI strategy?

• Alignment to key corporate objectives
• Understanding where your organization fits on the BI Maturity Model
– Data structure and readiness
– Data Quality
– Data integration
– Organizational readiness
• Identifying the “true” BI needs of the organization. Activity Based
Business Intelligence (ABBI)
• Alignment of metrics across the organization
• Business Intelligence Competency Center
• Build or buy?
• A BI strategy to support short term and long term objectives



Support Corporate Goals and Metrics

Support Corporate Goals and Metrics

Target Area
Reports KPI’s Analytics Reports KPI’s

Alignment of metrics and goals

Business Area
Finance Corporate Business Unit
Alignment of metrics and goals Marketing
Operational
Reports KPI’s Operational
Objectives
Objectives


BI Maturity Model

BI Maturity Model

Business Intelligence Pervasiveness
Predictive
Analytics
Organizational Readiness
Analytic
Applications
BICC

Dashboarding
Executive
Data Readiness
Sponsorship
Ad Hoc Disparate Data Data Mart Data Warehouse
Analysis
Infrastructure
Query and Skills Data Quality
Reporting

The BI Maturity Model – Data Readiness

 The first layer for the foundation for a successful Business
Intelligence deployment is the data layer or data readiness.
 Without a strong data structure in place that is designed to answer
business questions, it will be much more of a significant challenge to
have a widely successful BI deployment.
 Disparate data sources that are transformed, combined and
manually manipulated will prove a challenge to secure information
that is trusted throughout the organization
 As organizations progress in their need for information, there may
be a tendency to build subject specific data marts
 Better than disparate data sources
 Still challenges with combining data across subject areas or subject marts
 Less efficient data marts as BI needs increase and data marts are modified


The BI Maturity Model – Data Readiness

 Ultimately successful organizations, for a wide variety of technical
and business reasons deploy a data warehouse.
 The key is that the data warehouse must not solely be designed
from a technical perspective of size, performance and structure but
must first be designed from a business perspective.
 It must be understood what business questions must the data
warehouse support now and in the future and not just from a
siloed, sales, finance, HR etc. perspective but designed to allow
these metrics to link across operations and support corporate
objectives.


Execute Data Quality

 Data is the backbone that allows for a sound BI deployment.
 A sound Data Quality strategy and execution is a significant part of
that backbone and overall BI strategy
 Data Quality is important because:
 Trusted Data. The ability to trust the data and know that facts and figures are
correct is extremely important, especially in financial reporting and today’s SOX
requirements.
 Accurate Information. In order to get accurate reporting, you must have clean
data.
 Example of sales reporting based upon customer ids.
 If there is not a master data management system in place or stringent rules on customer master
entry, it is highly likely that there are duplicate customer entries.
 A data quality tool can identify duplicates, cleanse and consolidate them resulting in more accurate
information.
 A data quality tool can cleanse the data interactively as the information is entered resulting in
ongoing clean data from the point of entry.


Execute Data Quality

 Data Quality is important because:
 Compliance. SOX, HIPPA are just a couple of examples of required compliance.
 Individual person example
 Suppose you have individual data in your system for Michael Andrew Jones. This individual may
have entries as Mike Jones, Mike A. Jones, Michael Jones, Michael A. Jones, Michael Andrew Jones,
Mike Andrew Jones etc.
 It becomes a severe challenge to data privacy if this data is not cleansed and accurate. Are these
versions of the same individual or different people?
 Security. The security of data from keeping secret company strategic
differentiators to ensuring employees only see certain types of data is critical to
most organizations.
 A sound data quality program can help ensure that data is secure by consolidating data, moving
data appropriately and make sure employee information is clean and accurate as employees are
allowed access to only certain types of data.

 Data quality should be an enterprise wide program, not just from an
IT perspective but most importantly from a business perspective.
 When developing your BI strategy, be sure to include a data quality
strategy


The BI Maturity Model – Organizational Readiness

 Organizational readiness is really about having the right skills in
place and the right organizational structure to support those skills.
 Business intelligence skills may require
 Specific BI tools knowledge,
 Business knowledge ,
 Skills on dashboard best practices and visual effectiveness
 Very specialized data mining and predictive analysis skills.
 What in house and external skill sets an organization has access to
will drive what BI they are ready to deploy.
 Executive exposure and sponsorship
 What skills are required now and in the future? What skills can we
utilize now? What skills can we cross train? Hire?


The BI Maturity Model – Organizational Readiness

 Understand the readiness and capabilities of the various
organizations across the enterprise
 Understand
 Audience
 Deliver mechanisms
 Frequency
 Self service or developed reports
 Training requirements


The BI Maturity Model – BI Pervasiveness

 Tool complexity is not just from a technical perspective but is
measured by the number of users, the training required, the
business expertise and technical expertise required to deploy these
tools.
 An organization can generally deploy developer reporting tools to a
small population of users in a relatively easy manner.
 As more ad hoc query and analysis is deployed, a meta data
infrastructure may be required along with more significant training
for the end users.
 Once Meta Data is in place and users understand how to secure
data and understand what metrics can be measured and how, then
dashboards can be deployed.
 Finally advanced analytics and predictive analytics can be deployed.
 Walk before you run



Activity Based Business Intelligence


 Execute requirements gathering by understanding the daily work
requirements at the audience level
 Purchasing Buyer for Raw materials for Manufacturing process
 Report for overdue purchase orders with materials overdue by 10 days
 Peel back the onion
 Buyers uses the report and then queries deeper to review the materials on each PO
 Based upon materials, buyer reviews the production schedule and determines if
production may be impacted by late deliveries
 If production is negatively impacted, buyer works with production scheduler to re-
schedule or expedite materials from vendor
 Buyer’s real value is a dashboard that shows production line impact due
to overdue materials
 Dashboard should drill back to overdue po’s detailed reports
 Embeds BI into the business work flow
 Identifies the impact of BI to other business areas



 Flow of dashboard to drill down reports



Align Metrics from Top to Bottom

Align Metrics From Top to Bottom

 In order to truly provide value to the organization from top to
bottom, the metrics must align from top to bottom across
information and eliminate silos.
 Aligning metrics can prove to be a powerful tool to help both the
top and bottom lines of an organization.
 Line leaders to middle managers to executives must have metrics
and dashboards that are in alignment and support one another.
 Business Scenario
 Construction Company
 Corporate Objective to increase margin by 1%
 Operations contributes by utilizing a machine 25% more efficient
 Specialized skills required
 HR aligns their metrics with Operations to retain, cross train and hire these skills


Align Metrics From Top to Bottom

 Understanding the “links” between functional areas can help define
conformed dimensions and ensure the ability to measure across
business units/areas
 If the data structure is present and objectives are in alignment, all
aspects of an organization can be strategic and contribute to the
overall success of the company.



Business Intelligence Competency Center

Plan For A Business Intelligence Competency Center

 A Business Intelligence Competency Center is a permanent
organization within a company with a formal structure designed to
most effectively promote business intelligence within an
organization by putting together the right skills and knowledge
which results in increased user adoption.
 A BICC does not have to start out with every role and every skill that
is defined in the end solution.
 A BICC should and can evolve as your organization evolves adding
the necessary skills as your BI deployment grows and more business
units and users adopt the chosen BI tools.
 Initially it may not require much of a significant BICC if you have a
single data source and are deploying developer level reports to a
single business unit. As you deploy end user reporting solutions and
dashboards to additional business units your BICC must grow and
adapt in order to keep user adoption of your BI solution high.



 It is important to understand the types of resources and skills
required for a BICC so it can be determined what specific skills your
organization needs as BI becomes more pervasive and evolves.
 The chart below depicts some resources/skills required for a BICC.
Not every role is absolutely required and some roles can be
combined for each organization is different.



Build or Buy

Contents

• Pro’s and Con’s – Build from Scratch
• Pro’s and Cons’ – BI Apps
• Timeline and Activities
• Simple Calculations and Comparisons
• General ROI statements
• Soft ROI
• Hard ROI
• Choosing which BI App to deploy


Build or Buy components of the solution

• Based upon all of the factors discussed to this point, should you build or
buy a solution
• Packaged solutions that are a hybrid
– Oracle BI Applications
– Rapid Marts
– Significant cost and time savings

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ROI – Build from Scratch

• Build from scratch disadvantages

– Thousands of tables, fields, views and all must be analyzed and determined which are

appropriate

– Status driven transactions, status must be analyzed and understood for appropriate

metric use

– Appropriate denormalized data structure must be designed to support reporting and

metrics

– ETL scripts must be designed and developed to move data from PeopleSoft to data

warehouse (if a data warehouse is utilized)

– If no data warehouse is utilized, then potential performance hit on PeopleSoft

transaction system



• Build from scratch disadvantages (continued)

– Extensive requirements gathering

– No industry standard KPI measurements (unless industry standards are understood)

– More extensive skills required to build a complete solution
• Data modeler

• Data quality

• Data architect

• Data warehouse administrator

– Higher costs

– Longer time frame
• Higher failure percentage*
*(According to a 2003 Gartner report, more than 50 percent of data warehouse projects failed,
and the ones that survived were delivered very late with extremely high costs.) Embarcadero Technologies



• Build from scratch advantages

– Build it specific to the organization requirements

– If utilize only BI against the transaction table, report development can be more swift as

a data warehouse is not required (Note: some issues result from reporting against the

transaction system)

– No additional cost for the BI application


ROI – Buy BI apps

• Buy (BI Apps) Advantages

– Time to value – faster implementation (typically 3-4 months per application)

– Industry standard KPI’s and measurements

– Higher percentage of success

– Pre-built data warehouse structure (using a data warehouse reduces performance

concerns against the PeopleSoft transaction system)

– Pre-built dashboards

– Pre-built reports

– BI application is embedded in the PeopleSoft transaction processes/menu. Do not have

to leave the PeopleSoft application to generate BI


ROI – Buy BI Apps

• Buy (BI Apps) Advantage

– Upgrades and maintenance. The BI apps are upgraded and maintained with the

PeopleSoft transaction system

– All appropriate tables, fields, views, status’ are already analyzed and determined

– Data warehouse foundation is already in place to add additional data content from

outside of PeopleSoft


ROI – Timeline/Activity Comparison

Months to years - Build
Design and
Requirements Analyze Design a Data Design and
Create ETL develop reports, Test & Train
Gathering PeopleSoft Model deploy security
dashboards, kpi’s

Much of this work, delivered as part of BI Apps

Weeks to Months – BI Applications
Refine
Install and Execute ETL
Test dashboards, Train
configure processes
reports, kpi’s


Simple calculations and comparisons

• Perform some simple calculations based upon some assumptions

• The Procurement and Spend Analytics contain:

– 2 dashboards, 14 dashboard pages, 103 reports, 161 metrics

• Assume that each report is of simple complexity. Simple is defined as simple

formatting and groupings, standard calculations such as totals, sums, averages etc.

Standard graphs and charts. 8 hours to design, develop and unit test. (Note: Most likely

there will be a mix of low, medium and high complex reports)

• If each report is 8 hours then 103 X 8 = 824 hours of work or 20.6 weeks of effort.

This does not include dashboards and dashboard pages.

• This also does not include any data structure or ETL work or metrics analysis


Simple calculations and comparisons

• Perform some simple calculations based upon some assumptions

• The Supply Chain and Order Management Analytics contain:

– 2 dashboards, 18 dashboard pages, 157 reports, 388 metrics

• Assume that each report is of simple complexity. Simple is defined as simple

formatting and groupings, standard calculations such as totals, sums, averages etc.

Standard graphs and charts. 8 hours to design, develop and unit test. (Note: Most likely

there will be a mix of low, medium, high complex reports)

• If each report is 8 hours then 157 X 8 = 1256 hours of work or 31.4 weeks of effort.

This does not include dashboards and dashboard pages

• This also does not include any data structure or ETL work or metrics analysis


General ROI statements

• According to TDWI Research, the average data warehousing project costs

$1.1 million and takes 10 months to deliver, while a data mart project costs

$544,000 and takes six months to deliver. *

• One leading BI implementer has estimated that the cost of deploying a

custom solution is typically 2.5 to 3.5 times greater than the cost estimate

of deploying prebuilt applications.**

• When you add everything up, we estimate about a 7X effort increment to

deliver a similar piece of functionality as the BI Apps.***
*From In Search of a Single Version of Truth: Strategies for Consolidating Analytic Silos by Wayne Eckerson, TDWI Best Practices Report,
2004 (www.tdwi.org/research/reportseries). Technically, the numbers are for consolidating data warehouses, but the common approach for
consolidation was starting from scratch
** The Business Intelligence Consulting Group, “The Great Debate: Buy Versus Build,” 2007
*** Jeff McQuigg – Independent consultant. IT Toolbox Blog 3/17/2008


Soft ROI

• More timely access to information

• Improved decision making

• Improved performance (no reports against the transaction system)

• BI embedded into the business process. With BI apps, users appear to stay

within the PeopleSoft application

• Total Cost of Ownership (BI apps are supported)

• Baseline data warehouse in place, providing a foundation to add additional

data sources

• Industry standard measurements

Soft ROI

• Oracle Development Takes care of the following activities:

– Requirements Gathering

– Naming Standards & Industry KPI’s

– Source System Analysis

– Change Data Capture Design

– ETL design and coding and execution

– Metadata

– Alerts and triggers

– Reports and dashboards

– Supportability and upgrades

– Enabling BI within PeopleSoft (Big ROI)


Hard ROI

• In order to determine the Hard ROI, you must have measurable value

• Start with these two questions:

– What will you be able to do with the BI apps that you could not do prior to implementing

them?

– What problems will be resolved through the use of the BI apps that were unsolvable

before the project was started?

• Example: Based upon BI information, it is estimated inventory values can be

reduced by 10% or $1,000,000 resulting in a better control of cash.

• Example: Based upon BI information, it is estimated to reduce purchasing costs by

5% or $200,000.

• See attached document on ROI


Executing on the Vision

Long term and short term strategy

• Understand what can be deployed in the short term to meet key goals
• Design the short term solution to support the long term vision
• Examples
– Deploy developer based BI tools first
– Work towards a comprehensive Data Warehouse
– Create interim data marts (design for the integration to the long term data
warehouse)
– Deploy a rapid mart or BI application

– Deploy BI self service


Executing on the Vision

• All of the elements described within this presentation must be
understood in order to develop a successful BI strategy
• Categorize and classify the findings
• Prioritize the elements based upon feasibility and benefit
• Based upon the findings create a short term and long term business
intelligence strategy


Business Benefit and Feasibility

• Map the benefit and feasibility to help prioritize strategy elements

High

Purchase BI
Build data apps
warehouse

Busi BI Self
ness service
Valu
e

Deploy
developer
based tools

Low Feasibility or Ability to Execute High


BI Strategy Requirements Workshop

• Understand High Level Key measurements and objectives of top
management
• Understand key measurements and objectives of upper and middle
management along with operations
• Ensure BI strategy aligns top to bottom
• Gather report information consisting of:
– Metrics – what gets measured and what is the measurement criteria
– Medium – What is the preferred medium for the presentation of the report/metrics


BI Strategy Requirements Workshop

• Gather report information consisting of:
– Frequency – How often is the metric measured and presented? Self service,
scheduled?
– Audience – Who is the intended audience for the report?
– Delivery mechanism

• Gather BI Maturity information consisting of:
– What are the data sources
• What type of data is stored in each source?
• What is the data structure type?

– What are the current BI skill levels?
– What is the organizational alignment for BI?


Decision Elements

• Capture information related to each report/metric
• Specifics for each report will drive the solution for each report

Data Strategic
Report Description Frequency Audience Mechanism KPI's Sources Data Type Ad-Hoc Scheduled Benefit Feasibility /Tactical


Building A Bi Strategy

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