Moss L.T., Atre S. Business intelligence roadmap: The complete project lifecycle for decision-support applications
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[ Team LiB ]
Business Drivers
Without strong business drivers and without an alignment with the strategic business
goals of the organization, the BI decision-support initiative may falter. For example,
let us assume that the organization wants to increase revenue by decreasing time to
market. This translates into building BI applications as fast as possible, no matter
what other effects this might have (for example, as speed goes up, quality goes
down). Further, let us assume that the BI application objective is to decrease
operating costs by increasing productivity. This leads to building BI applications that
deliver business process improvements no matter what it takes (for example, as
quality goes up, speed goes down). In this example, the organization's strategic goal
and the BI application objective are both worthy business drivers for building a BI
solution. However, because the strategic goal and the BI application objective are
not compatible in terms of speed and quality issues, it will be difficult to get
management's support for this BI application.
This example illustrates the importance of understanding the organization's strategic
business goals as well as the IT strategic plan and ensuring that the BI application
objectives support both. This may be more difficult to do than it appears. Even some
of the most sophisticated organizations often do not have easily accessible or well-
articulated strategic business goals statements. Become a "detective" and review the
organization's annual report, public statements, newspaper coverage, syndicated
articles, and internal memos for valuable information.
Substantiate your business justification. Do not invent a business case where one
does not exist just to get the BI project approved. Interview senior managers to
confirm the organization's strategic goals, and interview business managers and
business analysts to validate the BI application objectives.
Let us discuss an example of a valid business justification. An automobile
manufacturer was rated near the bottom of a study on customer satisfaction and
product quality. This hurt the manufacturer in two ways.
The warranty costs were much higher than those of an average automobile
manufacturer. These measurable costs were directly impacting the
organization's bottom line.
1.
Unsatisfied customers spread the word about the manufacturer: "I'll never buy
another car from that company—and I'll tell all my friends." The costs of
damaged customer confidence and lost sales were immense but much more
difficult to measure than the costs of warranty.
2.
In this example, the strategic business goals were to retain the customers and to
reduce the expenses on warranty costs. In order to achieve these two goals the
automobile manufacturer had to be able to communicate the information about
malfunctioning parts to the parts makers on a timely basis. If a parts maker did not
improve the quality of a part, the automobile manufacturer would have to buy that
part from a different parts maker. The automobile manufacturer also needed
information about the customers who were returning the malfunctioning cars in order
to contact them for "damage control."
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This automobile manufacturer justified building a BI application to measure
manufacturing quality and to relate the quality measures to loss of sales, customer
complaints, and customer defection. Quality measures were to be captured at the
time of assembly as well as from the warranty data. Since a major portion of overall
product quality is based on the quality of the parts that go into the automobile, the
quality measures were to be provided to the parts makers through secure Web
access. By giving the parts makers this information, the automobile manufacturer
believed the parts makers would be able to improve the quality of their parts, which,
in turn, would improve the overall quality of the assembled automobile. In this case,
the BI application objectives directly supported the strategic business goals.
Business justification is an iterative process. As difficult as it might be to justify the
business case, realize that business managers are aware of the buzz about BI and
would like to take advantage of any competitive benefit they can get. Reiterating the
benefits will help crystallize the business justification and make everyone feel
comfortable about funding the BI decision-support project.
Once the strategic business goals and BI application objectives are verified and
matched, you can define the business analysis requirements for the BI application
that will allow the organization to meet its strategic business goals.
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[ Team LiB ]
Business Analysis Issues
In most organizations, business analysis issues usually revolve around unmet
information needs from current heterogeneous data sources and poor quality of the
source data.
Information Needs
With the help of business analysts, formulate the business issues that need to be
resolved by each BI application objective. Determine what results you want to obtain
from the business analysis, for example, answers to such questions as, "Why are we
losing 50 percent market share to ABC Company in New England?" Then define the
information requirements for the business issues at hand. Determine the subject
areas, timing, level of detail, granularity of data, and even what external data you
need to answer the business questions. Identify the associated business roles (e.g.,
senior business management, business analyst, and so on) that would be active in
the various decision-support functions.
Identify possible data sources where the required information could reside. Data
sources can be internal as well as external, and business insights often lie buried in
the relationships among the multiple data sources.
Types of Data Sources
One of the challenges in building a BI decision-support environment is to merge data
from different types of data sources. There are three major types of data sources:
operational, private, and external (Figure 1.2).
Figure 1.2. Three Major Data Sources
Operational Data
Online transaction processing (OLTP) and batch systems provide internal operational
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data about subject areas, such as the following:
Financial
Logistics
Sales
Order entry
Personnel
Billing
Research and engineering
Private Data
This internal departmental data usually comes from the desktops and workstations of
business analysts, knowledge workers, statisticians, and managers. Examples
include the following:
Product analysis spreadsheets
Regional product usage spreadsheets
Prospective customer databases
External Data
Organizations often purchase external data from vendors that specialize in collecting
industry-specific information available in the public domain, such as the following:
Health care statistics
Customer profile information
Customer catalog-ordering habits
Customer credit reports
External data is usually clustered around the following categories:
Sales and marketing data: lists of prospective customers
Credit data: individual credit ratings, business viability assessments
Competitive data: products, services, prices, sales promotions, mergers,
takeovers
Industry data: technology trends, marketing trends, management science,
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trade information
Economic data: currency fluctuations, political indicators, interest rate
movements, stock and bond prices
Econometric data: income groups, consumer behavior
Demographic data: age profiles, population density
Commodity data: raw material prices
Psychometric data: consumer profiling
Meteorological data: weather conditions, rainfall, temperature (especially for
agricultural and travel industries)
Source Data Quality
Merging and standardizing data is usually a requirement of every BI application but
one that is not so easy to accomplish. One of the difficulties in merging and
standardizing data from different types of data sources is that the data is stored in
different file structures on different platforms. What makes the process even more
difficult is that the keys for the same objects on different data sources usually do not
match, the definitions for the same apparent data are often inconsistent, and the
values are often missing or conflicting. In addition, different people in the
organization have authority to determine business rules and policies for data from
different types of data sources, and resolving data conflicts among them or getting
clarification is often all but impossible.
Standardizing data from internal operational data sources is difficult enough, but
standardizing data from private and external data sources is a major challenge and
could be costly. This cost should be calculated and included in the cost-benefit
analysis.
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[ Team LiB ]
Cost-Benefit Analysis
A common complaint is that BI projects are hard to cost-justify. That can be true if
there is no obvious business problem to solve. One of the most difficult aspects in
building a business case for a BI application is to show an adequate ROI. Despite the
difficulty, you must demonstrate how, by analyzing and mining the information in
the BI decision-support environment, the organization can more effectively
maneuver and adapt to an increasingly changing marketplace.
Benefits are usually harder to quantify than costs, and it will take many high-valued
benefits to offset the costs. A very effective method for justifying the expenditure of a
BI application is to tie it directly to a business problem of measurable proportion. For
example, let us assume an organization is losing $5 million each year because it
cannot curb insurance fraud due to insufficient and unreliable data about its
underwriting practices. If the proposed BI application can resolve that specific
business problem, it will be relatively easy to justify. Therefore, be as detailed as
possible when identifying the benefits, even if it is difficult to quantify a precise ROI.
This way you can gain the confidence of business executives and win approval for the
BI project.
Note that not all business problems need a BI solution. For example, the types of
problems that do not require a BI application because they can be solved in more
economical and less complicated ways are:
Provide easier online access to a flat file
Archive operational data
Merge two operational files for operational processing
Separate the operational reporting function from the operational update
function
Sometimes all you need to do to solve an operational problem is to buy a better
reporting tool or move the data into a relational database; neither should be
interpreted as a need for a BI solution. However, if the business problem hinges on
an inability to analyze integrated cross-functional data or to extract from the
operational systems hidden intelligence needed to make strategic business decisions,
then a BI decision-support initiative is probably the right solution.
The results of the cost-benefit analysis should succinctly state how the BI application
would solve a business problem or enable a business opportunity. It should also
state what type of information will be available, how that information can be used to
make better business decisions, and when and how the information will be presented
to the business community (e.g., monthly reports, ad hoc access through online
analytical processing [OLAP] tools). Once you have clearly stated the business need
and outlined the benefits, the next step is to estimate and compare the detailed costs
and benefits so you can produce the projected ROI, which provides the justification
for the BI project.
All BI decision-support initiatives should fulfill at least one of the five benefit
1.
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categories listed below (Figure 1.3).
Revenue increase, possibly in the form of:
Identification of new markets and niches
More effective suggestive selling
Faster opportunity recognition
Faster time to market
1.
Profit increase, including possibilities for:
Better targeted promotional mailings
Early warning of declining markets
Identification of under-performing product lines or products
Identification of internal inefficiencies
More efficient merchandise management
2.
Customer satisfaction improvement through:
Improved understanding of customer preferences
Improved customer-to-product matching
Up-selling to customers
Increased repeat business
Faster resolution of customer complaints
3.
Savings increase through:
Reduction in wasted or out-of-date merchandise
Reduction in requests for customized reporting
4.
Market share gain through:
Increased numbers of customers who defect from the competition
Much higher customer retention rate as compared with previous years and
with the competition
5.
Figure 1.3. Benefit Categories
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In addition to determining ROI, a business case assessment must include an
appraisal of risk. Any project is bound to involve some risks and, given the high costs
of BI projects, performing a risk assessment is a high priority.
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[ Team LiB ]
Risk Assessment
Risks are factors or conditions that may jeopardize a project. Risks should be
assessed for the following six major variables:
The technology used for implementing the project1.
The complexity of the capabilities and processes to be implemented2.
The integration of various components and of data3.
The organization and its financial and moral support4.
The project team staff's skills, attitudes, and commitment levels5.
The financial investment in terms of ROI6.
Table 1.1 depicts a basic risk assessment matrix for these six variables, using the
colors of a traffic light to indicate the severity of the risk:
Green = low risk—go ahead with the project
Yellow = medium risk—caution, proceed slowly
Red = high risk—stop, reevaluate before proceeding
Each organization should develop its own appropriate variables and risk conditions
for analyzing the risks most likely to impact its BI project. In developing that
detailed risk assessment matrix for your organization, expand on the questions listed
below.
Technology risk
- How mature are the selected technologies within the marketplace?
- How mature are the selected technologies within the organization?
- How many different technologies will co-exist?
- Do we have incompatible operating systems?
- Do we have incompatible database management systems (DBMSs)?
Table 1.1. Basic Risk Assessment Matrix
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Level of Risk
Variable
Green (Low)
Yellow (Medium)
Red (High)
Technology
Experienced with
mature technology
Minimal experience
with technology
New technology, little
experience
Complexity
Simple, minimal
workflow impact
Moderate, some
workflow impact
Mission critical, will
require extensive
reengineering
Integration
Stand-alone, no
integration
Limited integration
required
Extensive integration
required
Organization
Solid internal
support
Supportive to a
large extent
Little internal support
Project team
Business experience,
business-driven,
talented, great
attitude
Some business
experience,
business-driven,
talented, fair
attitude
No business experience,
only technology-driven,
limited talent, bad
attitude
Financial
Investment
Possible ROI within
a very short time
Possible ROI within
a moderate time
frame
Possible ROI after a few
years
Complexity risk
- How complex is the overall IT environment?
- How complex is the BI application itself?
- How extensively will workflow have to change? Will it have to be
completely reengineered?
- How many sites will be supported?
- What is the degree of distribution of data, processes, and controls?
Integration risk
- How many interfaces will the BI application have?
- Are there external interfaces?
- How much source data redundancy exists?
- Can the primary keys from various data sources be matched?
- Do we have incompatible standards? No standards?
- Do we have "orphan" records as a result of referential integrity
problems?
Organization risk
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