A business intelligence project will be more effective if it is driven from within a knowledge management initiative.
Knowledge management (KM) and business intelligence (BI) are often confused terms, as are data, information and knowledge, and it is here that business people and those responsible for IT make their most fundamental mistake, a mistake that can be the beginning or end of a successful business strategy.
Foundations
"One has to recognise BI and data warehouse projects as digital backbones for the decision-making process."
Data and information are fundamentally different – where data is the factual input for reason and discussion and in itself has little meaning. In fact, data without a point of view can be overwhelming. In much the same way, the BI project will be that much more effective if it is aimed and driven from within the KM initiative. If businesses don't make the distinction between data and information, several phenomena occur.
Firstly, organisations will implement computer and other systems to generate data more quickly. While this happens faster, the conversion to information entails reasoning, interpretation and judgement – all human activities, which generally remain at a comparatively slower pace. The problem is that the technological capability will drive the data collection process and will result in data overload. While there are many definitions of knowledge, there is one that simply sums up the most appropriate approach: "Knowledge is a human capacity to act." This view is adapted from Karl Sveiby's statement that "knowledge is a capacity to act".
Act on it
The reason for a renewed focus on KM, BI, data warehousing and supporting projects is based on the competitive environment where market, customer and operational knowledge have become the cornerstone of competition.
Added to this, there are essentially six issues that have driven business attention back to KM. Often, it is used as a correction from misapplied business process re-engineering projects or as a reaction against fad management theories. KM is also driven through the increasing capability of the Human Sciences as well as the advent of more advanced computing and communications technologies. Increased competition (linked to globalisation) and the creation of the knowledge economy have further highlighted the need for comprehensive KM.
Part of the KM challenge is to deliver an integrated BI solution, which includes data warehousing, for enterprise information management. Added to this is BI, which is about being able to rapidly respond to change in an intelligent fashion. The trick is to balance complete BI solutions with an open BI architecture, and to maximise a positive impact on business processes across the enterprise. One has to recognise BI and data warehouse projects as digital backbones for the decision-making process.
On an esoteric level, a key question arises: where are the values of our information assets, including knowledge and intellect, recorded? One could argue that it is impossible to physically store what the human brain contains – in other words it is not feasible or even possible to record each individual's knowledge.
This is true to a certain degree. However, by taking a forward-thinking approach, we could certainly attempt to collect some measures related to intellectual capital. Most current applications that manage activity, cost and investments were designed with the physical, industrial world in mind. As a consequence, this legacy mindset has not measured intellectual capital, knowledge and the return on these intangibles.
A stitch in time…
So why not extend the value of the data warehouse? It may be the best place to stitch together information from across the company, and provide measures for return on intellectual capital that are often overlooked.
It should be recognised that the data warehouse exists to increase the value of the corporate body of information, while operational systems exist to support the execution of ongoing activity. By using this new source of information, the value of BI should also be further extended.
Take for example intellectual capital: in short these are the intellectual assets, including corporate knowledge, patents, databases and process structures that can be used to increase the value of an organisation. There are typically three types of intellectual capital – human, structural and customer – each of which provides a different set of measures for a company in the emerging knowledge economy.
Assembling information on the intellectual assets of a company is not always an easy task. Operational systems may not have the right information. Gathering information on a company's human capital requires information from human resources, project staffing and the accounting systems. Analysing the impact of structural capital requires that a business analyst assemble information on the cost and return from the asset.
Once a company is alerted to the importance of these assets, it can start to include relevant data in the design of the data warehouse.
The purpose of building a data warehouse is to improve the measurement portfolio of a company, either in terms of timeliness, historical depth, or quality. It enables us to build an analytical system that improves performance and financial measures at operational and strategic levels. What ever the objective of an organisation may be, storing terabytes of data does not necessarily contribute to this.
Currently the demand is for information portals, which combine dynamic, compelling content with services and commerce offerings, personalised to each individual user. The reason for the combination of a wide variety of information and commerce resources should always be to achieve some business objective. Too often applications in isolation, such as customer management relations or balanced score cards, claim to be the ultimate solution for making companies more intelligent. However, a comprehensive KM drive with the data warehouse and BI as the foundations will positively impact the way we do business.
To quote Albert Einstein: "Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world."