Excerpt from "Show Me the Money: A DM/BI Business Value Primer", Bob Lambert and Tri Truong, Information Management Special Reports, March 24, 2009

1. Intangible benefits don’t count.
2. BI has no inherent value.
3. Senior managers often make decisions about future outcomes with insufficient data.

Intangible Benefits Don’t Count: An effective business case communicates tangible future value in a convincing way.  An argument has a chance of convincing a skeptical reader if the reader agrees that the argument’s assumptions are reasonable and that the conclusion follows logically from the assumptions.  Quantifying financial metrics like Return on Investment (ROI) or Net Present Value (NPV) help build the case, but such measures are credible only if readers agree with the underlying assumptions and the logic built upon them.

BI has no inherent value: We in the BI field believe that any organization’s fortunes would improve if it rationalized its data stewardship, integrated its data, and applied analytics creatively in management and operations.  However true, that view must ring hollow to senior business managers.  Without a compelling and motivating story about how a new system contributes to revenue or reduces costs, that system’s business case stops dead in its tracks.  Of course sometimes “someone at a high level” just wants BI, but organizations don’t often embark on BI efforts without first evaluating tangible costs and benefits.

Senior Managers Make Decisions about Future Outcomes with Insufficient Data: Although BI practitioners must make a convincing case for future business value, there’s room for uncertainty.  Executives and senior managers aren’t highly compensated for playing it safe, but rather for understanding current conditions and setting direction based on educated but sometimes courageous predictions of future conditions.  A successful BI business case matches or extends the executive’s knowledge of current conditions and expands his or her view of potential future outcomes of near term actions.

Today: inconsistent data of uncertain quality blurs enterprise view and restricts planning

Today managers, planners, and analysts lack the information required to run the organization as a single enterprise rather than a collection of diverse units.

• Data quality in IT applications varies to the point that, outside financials, it is impossible to gather consistent data supporting an enterprise view of operations.
  • Application development efforts have focused narrowly on departmental interests without accounting for enterprise concerns, making application data incomplete in describing business processes and inconsistent with data in other applications.
  • Focus on departmental concerns and tight development timelines has resulted in incomplete validation of data critical to the enterprise but not critical to the application’s focus.  For example, customer demographics are not critical to the sales process and therefore zip codes and telephone numbers are not consistently collected at point of sale, substantially reducing value of market analysis based on sales data.
• Enterprise planners work with only the highest level summaries of operational data, those summaries suffer large margins of error, and planners cannot definitively answer questions required to make critical business decisions.
• Regulators have questioned the validity and repeatability of reporting because of the organization’s heavy reliance on spreadsheets and manual processes in gathering and compiling data for reports.

Solution: enable sound planning and management by identifying data assets and setting processes to manage them

Empower an Enterprise Information Architect to lead an effort that (1) identifies data that describes the organization, (2) defines how to integrate and improve quality of that data, and (3) improves the ability of information technology to maintain data quality.

(1) Lead definition of an Enterprise Information Architecture identifying information required to manage the organization as a single integrated enterprise, and data quality standards that ensure that data supports enterprise goals.

• Identify and define events and objects critical to the enterprise

• Identify and define relationships among those events and objects and attributes that describe them

• Classify data managed by the organization by type (operational, statistical, financial, decision support, etc.) and define standards for managing and integrating each type.
• Compile the above into a plan that explicitly supports the enterprise strategic plan

(2) Working with senior business managers, put in place a program of data quality improvement that plans and executes specific measures and sustained commitment to improving data quality in business processes and IT applications

• Identify the business group responsible for maintaining quality and integrity of each business object, event, relationship, and attribute

• Identify for each data item of interest to the enterprise its “system of origination” and “system of record”.

• System of origination is the application that provides the entry point of a given data object to the organization.
• System of record is the application that is the source of record for the data object.

• Define and deploy standards and practices for for business process and IT application definition that support data quality and integrity standards

(3) Working with senior IT managers define and put in place standards for application requirements definition, data management, and metadata management to

• Define and deploy application development and interface standards that support data quality objectives.
• Ensure that application development efforts support enterprise data quality
• Continually monitor new developments in data management best practices and make that information available to the enterprise.

Excerpt from "Show Me the Money: A DM/BI Business Value Primer", Bob Lambert and Tri Truong, Information Management Special Reports, March 24, 2009

Before starting the Business Intelligence business case, the BI advocate should do the homework required to ensure its success, including these essential steps:

1. Know the organization’s goals and objectives.
2. Identify a BI champion.
3. Identify and work with BI stakeholders.
4. Identify an application with tangible business value.
5. Define and quantify a quick win prototype project.

Know the organization’s goals and objectives. It is human nature for any of us, including executives, to be receptive to help with our own goals and objectives but less receptive to new ideas that aren’t related to our own goals. Furthermore, senior executives facilitate intensive strategic planning processes to set the right corporate goals and objectives. A proposed BI initiative should clearly and tangibly help achieve strategic objectives already in place.

Identify a BI champion. BI is in a unique position within the application stack. Most organizations can operate without a BI strategy. However, most companies would greatly improve their market position with a comprehensive BI solution. The impetus for deploying such a solution needs to come from a leader within the corporation who champions the value that BI brings to the organization as a whole. Often, this champion is someone at the top level of the business chain of command with a solid grasp of the BI’s potential.

Identify and work with BI stakeholders. BI projects should be driven by BI stakeholders, those who will see direct effects (good or bad) from the BI project. Some stakeholders look to benefit from BI-based solutions to concrete problems. Other stakeholders will have to be convinced about the potential value of BI. Both types of stakeholder must be involved in defining and supporting the goals of a BI project.

Identify an application with tangible business value. Again, in order for the BI application to return value, it must focus on achieving business goals. These goals should be measurable so that the value of the BI application can be determined, and the application should contribute to overall organizational strategy.  Scroll down to “Business Value Examples” here for more.

Define and quantify a quick win prototype project. Businesses must quickly see the value that BI brings in order for it to catch fire in the organization. A prototype project is often the best way to showcase BI’s value proposition. These projects should typically produce tangible results in a matter of weeks and target a well-defined business area. The prototype should have a well-defined goal and ROI metric, and produce data or case studies that show progress toward, if not achievement of, that goal.

- Thanks to co-author Tri Truong for assistance with this post.