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Initiating A BI 2.0 System Project


Business intelligence uses key performance indicators [KPIs] to assess the present state of business and to prescribe a course of action.

Historically, this type of performance data was manually imputed or configured into weekly or monthly reports.

Today, this data must be available at shorter intervals, with reduced delays. To assist in this, the KPI methodology has been further expanded with other peformance methodology to incorporate KPIs and root cause analysis into a single methodology.

The time requirement for performance data largely depends upon the criticality of time in the provision of a product or service. For instance, the banking industry faces higher operational/credit risk loading, as do credit card companies and "wealth management" services, Weekly data is insufficient. Daily will be more effective. Hourly is mostly unnecessary. The understanding of these varying needs is the first step in designing a BI strategy. Developing beyond requirements is not only costly, it also delays projects, and as such delays competitiveness.

The first step in any BI project, is the intiation phase. During this time, the scope of the project is determined and the business requirements identified. This can be achieved by following the underlying methodology below:

Goal Alignment

Goal alignment helps the company identify where and why a BI system will add value. It determines the short and medium-term purposes of the programme. This must include:

  • Which strategic corporate goals will the system address?
  • What organizational mission/vision does it relate to?

A business case will be developed to outline how this initiative will eventually improve results / performance [i.e. a strategy map].

Establishing Baselines

This is a current analysis stage to determine:

  • Current information-gathering competency
  • Current capability of monitoring important sources of information
  • Data the organization currently collects
  • How current data is stored
  • Statistical parameters of current data - e.g. how much random variation does it contain? Does the organization measure this?

Cost and Risk Analysis

This analysis assesses the financial consequences of a new BI initiative.

  • Assess the cost of the present operations
  • Identify any increase in costs associated with the BI initiative
  • Identify the risk that the initiative will fail

This risk assessment should be converted into a financial metric and included in the planning.

Customer and Stakeholder Benefits Analysis

This analysis determines who pays and who benefits for the BI system. It also includes all stakeholders that are impacted by the system in ways other than direct benefits [indirect benefits] and those that may be resistant to any change in current procedure:

  1. Who will benefit from the initiative
  2. Who will pay
  3. Those with a stake in the current procedure
  4. Customer and stakeholder groups that will benefit directly
  5. Customer and stakeholder groups that will benefit indirectly
  6. The quantitative / qualitative benefits to each group
  7. Alternatives to increase satisfaction for each group
  8. Way in which benefits will be monitored
  9. All others impacted by the intitiative - employees, shareholders, distribution channel members etc.

Metrics

Information requirements identified from above analysis must be operationalized into clearly defined metrics. This phase of the project decides:

  1. What metrics to use for each piece of information being gathered.
  2. Why these metrics are the best to use
  3. Validation for using a particular metric
  4. The number of metrics to be tracked
  5. How they will be tracked - what kind of system can be used to track them.
  6. Standardization of the metrics, so they can be benchmarked against performance in other organizations?
  7. Industry standard metrics available

Measurement Methodology

Although measurement is an extremely important part of not only implementing a BI system, but in it's ongoing use, the gathering and recording of measurement needs to be acceptable both in terms of intrusiveness and cost.

The project plan must therefore establish a methodology or a procedure to determine the most acceptable way of measuring the required metrics.

  1. Methods used
  2. Frequency of data collection
  3. Industry standards for collection method and frequency
  4. Validation as to why the suggested method is best

Results

The BI program will be monitored to ensure that objectives are being met. Like any complex, long term program, adjustments in the programme may be necessary.

The programme should be tested for accuracy, reliability, and validity. Determine:

  1. How to demonstrate that the BI initiative (rather than other factors) contributed to a change in results.
  2. How much of the change was probably random.

 

Business Intelligence Databases

The basic form of data or information used in BI is stored in the databases. When the amount of data is significant (i.e. > 10 GB), it is transformed into a data warehouse.

A Data warehouse houses several databases, typically from several, often disparate engines. For instance, one may be Oracle-specific, another SQL Server-specific, or even half SQL Server and half DB2. Each of these database configurations stores data in a different format. A Data Warehouse stores the combined data and resolves these differences in format and other variances.

Considerations

When a database application is created and attached to data base, certain properties need to be considered.

Access Permissions - within any given application, different users are created and given certain permissions: Admin, Director, Manager, SalesPerson, Temp. Some of them are made administrator while others are made users.

Roles - whilst the application may be 'general' its abilities can be refined to suit certain roles [Director, Manager, Operational Supervisor etc.]

Tasks - the overall application handles different tasks, depending on the role of the current user — certain parts are exposed and enabled while others are not.

 

Related Reading

Developing A Data Warehouse

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