Enterprise Resource Planning's Role in Intellectual Captial

While teaching an intellectual capital management course I noticed some confusion are having over the role of enterprise resource planning (ERP) systems in intellectual capital. Some students are incorrectly categorizing ERP systems as a type of intellectual capital system; more specifically, a type of business intelligence (BI) system. In this post I will explain the relationship of ERP systems to intellectual capital systems in terms of the organization's processes as intellectual capital and as a type of BI system.

First, the role of ERP systems as an intellectual capital system for managing the organization's processes. One form of an organization's intellectual capital assets is its processes. The knowledge of these processes can be a strategic asset for the organization. ERP systems are used to standardize processes across the organization and this is where some confusion may exist. While ERP systems facilitate process standardization, these systems require adoption of more generic processes rather than unique processes organizations use to gain strategic advantage. The ERP systems are not used to develop innovative processes but rather the adoption of common processes and therefore are not contributing to the strategic process intellectual capital of the organization.

Secondly, the role of ERP systems as a type of BI system for supporting the development of new knowledge within the organization. ERP systems are transaction processing systems (TPS) used to conduct operational business in the organization. These systems yield large quantities of transactional data and this data can be used as an input source to enterprise data warehouses. However, the ERP systems themselves are not classified as a BI system since their primary role is transactional rather than informational. Additional tools can be used to make use of the transactional data but ERP systems are considered transactional systems with the potential of supporting BI practices.

It is clear that ERP systems contribute to developing an organization's intellectual capital but these systems are not intellectual capital systems by themselves. Organizational knowledge is very much associated with the development and implementation of these systems but, once in production, these systems primarily serve as transactional systems.

Differences between Business Intelligence and Knowledge Management

I'm teaching a course where we discuss different forms of managing and applying an organization's intellectual capital. Over the past two weeks I noticed the students experiencing some confusion over the difference between knowledge management (KM) systems and business intelligence (BI) systems. Although I have previously posted my mind mind map depicting an ontology for intellectual capital, it is not sufficiently clear how we differentiate between KM and BI. The two concepts are a part of the intellectual capital ontology and related but are also different.

While both KM and BI are used to support decision making they differ in the type of raw materials used, the processes to develop knowledge, and the type of knowledge developed. I like the models Sabherwal and Becerra-Fernandez presented to illustrate each of these. The diagram below is my adaptation of these models.

Viewing these diagrams we can see that both KM and BI use data to develop knowledge and this knowledge is applied to decision making. However, BI systems primarily use data to generate information which can be applied to generate knowledge and this knowledge is primarily developed as explicit knowledge. KM on the other hand, uses both data and information to develop knowledge and this knowledge is further developed to create new knowledge. The knowledge developed in KM applications consists of both tacit and explicit knowledge.

It can also be argued that BI is a component of KM. Looking at the knowledge management lifecycle of knowledge capture/creation, sharing, and application, we can consider BI applications used primarily in knowledge capture/creation activities. This means that we apply BI applications to capture or create information leading to knowledge but the knowledge sharing and application is outside of the scope of these systems. Whereas a KM system addresses the entire knowledge management lifecycle.

So, while both KM and BI systems are used to capture or create knowledge, KM systems address the entire lifecycle and result in tacit and explicit knowledge while BI systems are primarily used for knowledge capture and result in explicit knowledge.

This is my method of differentiating between the two concepts. Others may disagree or have alternative methods of defining these two concepts. I welcome your insight in the comments.

Too Much Data or the Wrong Type of Data?

Last November I wrote a post to my blog about a new perspective on IT. In this post I discussed my reaction to an article explaining that IT needs to pay more attention to the value of the data rather than only delivering the information system. Today, I came across an article that embodies this problem.

The article discussed the issue of data overload at a call center. Apparently, this call center collected a large amount of historical data and provided a complete portfolio of reports to their staff and the staff was overwhelmed by the volume of data available. The data and reports primary supported analysis of call center efficiency and did not provide information about customer interactions. The call center collects a large amount of data but is not using a majority of this data and does not collect data on the type of information that is currently needed to make decisions.

Now, back to my point about the need to focus on data. In this call center case, the call center data, applications, and reports provided a robust amount of information for the staff but these reports did not support the current need of understanding customer interactions. Rather than building systems and reports based on a large bucket of easily collected data, organizations need to determine where decisions are made and the type of information needed for these decisions. This will help the organizations identify the data needed to support decisions and allow them to build or purchase information systems to deliver this valued information.

By focusing on the end needs of business intelligence applications, these systems can be constructed so that the correct information is available for timely decisions. This is the value the end users and knowledge workers are looking for from the business intelligence applications.

Better Definition of Business Value

In my classes and my project management workshops I try to urge people to look beyond the variables (scope, budget, schedule) of project management. In the project management field we typically assign success to projects that complete the specified deliverables (scope) on time (schedule) within the allocated resources (budget). My argument to this mindset is that this is only the operational perspective of project management and we are missing the bigger picture.

Project managers need to look outside of the project variables to consider value. As project managers we need to associate the success of the project with the value the deliverables offer the organization. In this discussion of value we typically look at the long-term financial benefits provided as a result of the project. However, after reading a recent blog post I see that even the concept of value I had been using is shortsighted.

Certainly value can, and should, be associated with the long-term financial gains resulting from a project. However, value can also be realized by our relationships with our stakeholders through our availability to meet with them, our willingness to listen and respond to their needs, and our ability to understand their function within the organization and how the project can enhance this function.

Project managers must reach way beyond the operational perspective of project management and truly partner with stakeholders to deliver organizational value as well as stakeholder value. Think about your projects and your stakeholders; how are you offering value to them and how are you making sure they are realizing the value you wish to add?