Many organizations have evolved key internal business processes built on top of Microsoft Excel. These cross-functional workflows involve several organizational units responsible for collecting business system transactions, modifying this raw data, consolidating, transforming, pivoting and preparing data into a published set of Reports & Graphs – all in MS Excel. Such workflows are a burden to organizations – not repeatable, costly, time-consuming, inflexible and hard to scale, and evolve to become more complex over time. Business critical processes such as financial analysis, operational analysis and revenue analysis are often supported this way. Attempting to replace such systems can be quite daunting and a barrier to replace. The goal of this session is to present an easy to understand methodology and use cases to demonstrate how to move from an operational workflow in Excel to truly automated Business Intelligence.

1. Turning your Excel Business Process Workflows into an
Automated Business Intelligence Application
Kevin O’Rourke
Director, Practice Leader, BI Solutions
TriCore Solutions

2. Turning your Excel Business Process Workflows into an Automated Business Intelligence Application
​Presented by Kevin ORourke, TriCore Solutions
Many organizations have evolved key business internal business processes built on top of Microsoft Excel. These
cross-functional workflows involve several organizational units responsible for collecting business system
transactions, modifying this raw data, consolidating, transforming, pivoting and preparing data into a published
set of Reports & Graphs – all in MS Excel. Such workflows are a burden to organizations – not repeatable, costly,
time-consuming, inflexible and hard to scale, and evolve to become more complex over time. Business critical
processes such as financial analysis, operational analysis and revenue analysis are often supported this way.
Attempting to replace such systems can quite daunting and a barrier to replace.
The purpose of this session is to present an easy to understand methodology and use cases to demonstrate how to
move from an operational workflow in Excel to true Business Intelligence which is automated.

3. The Evolution of Spread marts1
Bringing It All Together5
Delivering the Project – Step by Step4
Methodology Overview3
Use Case Overview2

4. The Evolution of Spread marts

5. “Excel is the most popular decision support tool for business users worldwide. When
spreadsheets containing valuable corporate data are duplicated uncontrollably, and then
modified differently by different users, each file becomes a separate version of the
“truth.”
Each one of these fractured versions of the truth is called a “spreadmart.” Coined by
Wayne Eckerson in 2002, spreadmart is a word meaning both spreadsheet and data
mart. It’s a suitable name for a growing crisis …”
What is a “Spread Mart” ?

6. The politics of Spread marts
 Over 90% of organizations surveyed have over 20-30 spread marts;
 Created by departments (finance, marketing, sales, etc) who want more direct control over reports and analysis;
 Key benefits:
 Faster response to change;
 Ease of use and tools familiarity;
 Lower costs, ease of deployment;
 Minimal reliance on IT Services;
 80% of affected Analysts, 60% of Managers view as good, while 80% IT professionals want to eliminate them;
 Leadership team caught in the middle;
 Mandates by Executives to stop spread marts our hold back IT support only effective 6% of the time;
 Spread marts satisfy real needs, and require replacement strategy;
The average ROI for a spreadmart consolidation project is $3.34 million, with an average payback period of 2.1 years
(source: TDWI, 2014). In addition, one company managed to save 600 man-hours per year by instituting a forecasting
and integrated analysis system based on an Excel/BI hybrid (source: Computer Business Review, 2014).

7. Why spread marts evolve ...
Organizations generally evolve their information platform based on apparent business need (BI maturity). Matured
Excel workflows within an organization is a strong indicator
Drivers that evolve organizations from a direct-to-source only strategy:
 Information requirements are too great for as-is retrieval;
 Business is interested in analyzing data which often requires extensive preparation (cleansing, subject-oriented,
routine aggregation, advanced formulae, etc.
 Data in separate business systems cannot be joined;
 Data residing in multiple systems is inconsistent;
 Data requires extensive manipulation before it can be used as information;

8. Use Case Overview

9. Use Case Scenario
The Company
 Successful and prominent 130-year holding
company;
 Business focus: Utility distribution and
transportation energy products and storage
services;
 Operating centers both domestic and
international;
 Recorded over $5 billion in revenues in 2006;
 128 consecutive years of dividend payments;
 25 consecutive years of increased dividends;
Excel Workflow Process (Spread mart)
 Executive and Cost Center Operations Results Books (2);
 MTD / YTD Versions distributed each Monthly Close;
 25 individual tabs – 1 to 7 data sources per tab /report;
 ~19 legacy and non-legacy business systems;
 Standard analytical hierarchies maintained in separate
sheets;
 Manual process managed thru BSS team and local
contributors;
 ~270 data points (102 distinct);
 ~16 acquisition methods to produce inputs;
 No BI capabilities – trend, comparative, ad hoc,
interactive, automation, etc;
 29 copies to each Cost Center at monthly close;

10. Information Overview Diagram

11. Workflow Diagram

12. Methodology Overview

13. Methodology Detail
 Organizational Requirements Collection and Consolidation.
Discovery and data collection to understand Key
Performance Indicators must be role-based, available,
reliable, analyzable (for root-cause), comparable to known
benchmarks and across clearly defined subject areas;
 Organizational Justification. Understanding quantifiable
and strategic benefit when prioritizing projects is critical to
the process of prioritizing and selecting projects to
determine cost savings or ROI realized from projects;
 Technical Feasibility Assessment. Known requirements
must be evaluated against business systems for availability,
completeness, reliability and quality. This task allows us to
understand in more detail the level of effort and difficulty in
fulfilling known requirements;
 Current State Gap Analysis. Known gaps may exist
between the current state environment and actual
organizational requirements. Once gap analysis is
performed, TriCore will organize into options and
recommendations to the client thru team review;
 Implementation Planning (Roadmap). Implementation and
delivery goals are reassessed by the project team based on
findings. Project planning is further communicated to the
leadership team, business functional area leaders and project
sponsors. Once a general agreement to the plan has been
reached, project mobilization can begin;
 Infrastructure Planning. As critical to information management
definition and flow is to supporting strategic and tactical
organizational objectives, so is the infrastructure to support
usage. Infrastructure planning includes capacity planning,
identifying infrastructure integration 3rd party components,
service level planning and BI component distribution;

14. Facilitated
Session Findings
BI Strategy Roadmap
BI Strategic
Roadmap
Project Planning Views
(Costed)
Data Collection Worksheet
(Technical Feasibility)

15. Technical Feasibility Profile

16. Delivering the Project – Step by Step

17. Delivery of Operations Results Books from Phase I (iteration 1). Goal to standardize the model within this Phase for future phases, with the goal of
delivering the full set of results books;
Iterative Release Strategy. 3 Phases, Oracle direct-to-source, non-Oracle business-as-usual in first phase;
Standardize the data architecture. From the 1st iteration, the data warehouse should be standardized to evolve properly over time. Fundamentally, we
want to establish a plug-and-play architecture. As we onboard new data sources in the 2nd and 3rd iteration, we want to insulate all lineage from Staging
to BI. Most of the work will be done in the Data Acquisition layer to Data Staging Layer;
Architecture for DaaS. The data architecture must be considered a service first, optimized for both planned and unplanned query requests. The overall
design of the architecture must be gracefully modifiable as other requirements from other UGI functional areas are met;
Vary the data source and ETL automation. We are establishing the complete infrastructure in the 1st iteration. Subsequent iterations within the
Operations Analysis project will focus on further integration of non-Oracle data sources from source-to-staging while maintaining stability in downstream
EDW and BI platform;
Requires a Full Time BI Analyst. Our observation is there is definitely a full time role at UGI for a dedicated BI Analyst. The BI Analyst role has strong
business acumen but also has a strong knowledge of Business Intelligence. UGI has a highly diversified and distributed business application landscape. As
a consequence, reporting and analytics is performed thru equally complex manual workflows;
Focus on 3-4 project iterations per year. We need to demonstrate incremental value to build confidence in the value of BI. Using iterative releases that
focus on achieving incremental value promotes better user adoption;
Agile approach on BI Development. The UGI team has provided some great BI navigation scenarios to demonstrate the user experience within the
dashboard version of the Operations Results Books. When developing reports and dashboards, it will be important to get continuous feedback during the
actual development cycle;
Project Approach

18. 3 Phases, 5 Iterations in Phase I

19. Demonstration / Walk-Thru

20. Thank you for the opportunity!
Kevin O’Rourke
Director, Practice Leader, TriCore Solutions