Building a business intelligence (BI) dashboard for corporate governance is the primary support mechanism for a company's entire portfolio of enterprise and IT governance initiatives. Governance dashboard solutions are giving organizations improved visibility into areas of their business (IT, compliance, risk, etc.) that have been historically opaque and cloudy. As a byproduct of these dashboards, business strategy becomes better aligned with IT spending through the use of symbiotic metrics, balanced scorecards and complementary governance methodologies such as the CPR (conformance, performance and relating responsibility) framework.

The metrics used in dashboards are most commonly called key performance indicators (KPIs) and measure the enterprise's performance against consensual and well-defined goals and targets of excellence such as Six Sigma and Balanced Scorecard methodologies. KPIs will help drive the business in the desired strategic direction and serve as guideposts for quality and value innovation. They will aid in identification of excellent performance (strategic and tactical) and the correction of poor execution. Associated result thresholds built around KPIs can present management with "red alerts" and raise exception processing, better positioning management to be catalysts for organizational change. An executive will be able to easily tell if the company is underperforming or overperforming in critical areas and give immediate attention to the rectification of problems or capitalize on potential opportunities. The most common classifications of KPIs are leading and lagging indicators. Leading indicators yardstick business processes and activities that will have an impact on future corporate performance; they can, more often that not, be thought of as qualitative benchmarks. Lagging indicators measure the end result of past activity such as financial actualization and other quantitative-oriented constructs.

Many companies encounter trouble in defining a robust set of KPIs that properly aid in the monitoring and measuring of the business's performance, let alone drive continuous future improvements in operational quality and revenue. KPIs must steadfastly depend on business strategy as well as the methodology chosen for governance, but their definition is most impacted by the type of analysis that will be performed. Most often, the problem is that the key drivers of what is being measured have different ontologies - the who, what, where, how and why of underlying data (and thus the behaviors that KPIs will measure) will be divergent depending on the stakeholder and business component being measured. For example, a business continuity (BC) team will need to work with data that has a specific BC logistics focus - details about technical infrastructure and associated personnel, hardware vulnerabilities and incident command structures. These dashboard users will need to focus on measures that convey the degree to which business operations can be carried out in the event of disruption or what mission-critical systems have priority in a disaster recovery scenario. Other dashboard audiences will need to measure very different areas, such as regulatory compliance or risk management - therefore using vastly different KPIs that are more often associated with traditional corporate governance topics. Added complexity will quickly arise. For instance, some audiences will want to focus on classic output metrics (financial); for others, process or procedural metrics such as time to market, inventory supply and customer hold time will be more vital. The point is that you must know what kinds of questions will be asked of corporate data before you can properly define KPIs. While some data dimensions, such as geographic regions, application names and various market and reference data, may be shared among stakeholders, many won't. The same applies for measures of performance. Thus, the decision to build separate dashboards with siloed data marts versus trying to combine larger data sets in a cross-functional dashboard - attempting to solve every question of risk, compliance and BC in one holistic environment - will be tantamount.

In addition to KPI creation, the skillful consolidation of complex assemblages of data - so that they look and act like individual units of information from which management can perform intelligent drilling and pivoting - is the most critical success factor for dashboard success. For enterprise dashboards that attempt to provide intelligence on numerous categories of governance questions, supporting infrastructure must be carefully architected; data integration expertise, especially, will need to be in full abundance. Untangling the webs of business rules and transactional, market, reference and third-party data that are distributed throughout the enterprise will require some of the most advanced technical solutions available today. Options may include using enterprise integration software for true federated real-time integration or cutting-edge extract, transform and load routines (with embedded business rules and logic) invoked as Web services in order to seed the dashboard with information from a seemingly infinite number of disparate data sources.

Governance dashboards must support and drive corporate strategy and associated planning. Only by properly defining potent KPIs will it be possible to always know if the business is traveling in the right direction and instill a culture of continuous improvement and accountability. The more accurate the KPI, the better it will aid in achieving the operational excellence of all concerned business units. If KPIs are flawed, the usefulness of dependent governance decision support systems will be greatly minimized. What is not measured properly cannot be improved effectively. 

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