Businesses are now taking note of Performance Management (PM) and this is reflected in the uptake of the technology and approach by mostly large organisations that are focused on business optimisation and driving the performance of the organisation. However, in order to support this corporate goal, businesses require an architecture that supports the management of information – data and content.
If we consider the components of PM – measuring and monitoring; reporting and analysis; and planning, budgeting and forecasting – it is not difficult to see how the effectiveness and accuracy of a PM solution would be affected by the accessibility and quality of data.
There are four key information requirements that companies need to address in order to unlock the value of information and enable successful PM initiatives. They are:
managing the lifecycle of data;
aggregating data from structured and unstructured sources, ensuring data quality, and leveraging the data for an enterprise wide PM solution.
In order to support PM, it is necessary to ensure that information and/or content is managed over its lifecycle. Cost containment is a major driver of this approach as the cost of managing and storing information must be appropriate to the value of the data, its age and usage. Corporate governance and legislation, including the ECT Act, also create demand for reliable information management. These considerations can assist to create a solid foundation for a content management solution that collects, stores and indexes data, allowing it to be retrieved quickly and easily.
However, businesses also need to understand that not all the information required by users resides in transactional processing systems. Approximately 30% of the information required by the business will be found in structured data sources – for example business forms, applications, documents generated within the organisation – while the other 70% may be from unstructured data sources such as email and information from the World Wide Web.
A typical example is the processing of a credit application at a financial institution. Certain information will be drawn from transaction systems such as the person’s personal details and their credit history. This is then ‘balanced’ with the perspective from an external source such as an external credit bureau. The information will then be combined and delivered to an application that handles credit scoring. In essence, this means data from disparate sources must be delivered into a single business process.
However, this information must be obtained from trusted data sources if it is to be used effectively. This is part of the information management challenge and the third step towards building the right information architecture for PM.
In order to aggregate structured and unstructured data, a data warehouse (or series of them as required in larger organisations) creates a single, summarised version of this information. Smaller businesses might rely on their ERP system as their ‘trusted’ data source which is suitable, provided the information is ‘clean’ and not duplicated. However, larger organisations will have a more complex environment with more applications and data sources.
The collection of data from a variety of data sources is a mammoth task in large organisations and must be approached in the correct manner.
Technologies that assist to create a trusted source of information include master data management and data quality systems such as Extract Transform
and Load (ETL) applications.
With these three challenges addressed, enterprises will have the right architecture in place to implement an enterprise wide PM solution – the fourth and final step that enables the organisation to leverage trusted information to understand how the business is performing and focus on business optimisation.
David McWilliam
Managing Director
Cognos South Africa, an IBM company
