Introducing Total Controlling Concept
When two figures with different origins are compared it is relation. Assumption based upon relation is relational knowledge. For example when analyst wants to compare two different KPIs, reports or financial statements, analyst wants to gain relational knowledge with basis in direct knowledge.
Whenever relation is used to compare two data it can be called relational querry. If assumption is already set upon expected results it is relational assumption. If results of analysis fit into expectations it is relational knowledge.
Opposed to relational knowledge is direct knowledge: facts from production systems, relations and formulas in IT systems, reports, performance indicators based upon raw data and formulas (direct relations).
Example of relational knowledge from telecom industry, assumption is that total number of disconnected subscribers should be equal to summary of difference of EOP total number of subscribers and total number of gross adds in one month. Since data processed and diversified for total disconnections, gross adds and EOP (end of period total number of subscribers) through different processes and systems, data set will not be aligned as on the point before entering into production systems in POS and in ideal environment with perfect historical data.
Relational assumption is made upon known facts, based on facts relation adds additional “knowledge” or expectations. From the expectations new relational querry is made.
Figure 1. Introducing relational knowledge
What happens is that upon direct knowledge people use relational expectations. In IT industry it means upon actual data from different systems and processes with very different procedures of processing business users apply relational knowledge, assuming that figures should be aligned despite the fact that their life cycle was different as shown on
That is area of using Power points, Excel and etc. to customize data from reporting systems or production systems to fine tune and add additional knowledge or assumptions. Area of mixing direct knowledge and relational knowledge.
Relational knowledge is made upon direct knowledge and is presented together with direct knowledge.
Many relations form relational knowledge are very well hidden since current IT platforms do not support combined operations with direct and relational knowledge together, especially not on level of financial statements. What is still hidden are many small relations stored in Intellectual capital of experts knowing impacts and influences of direct knowledge data sets on other, but only for local impacts and influences. Not on general, company level.
Figure 3. Data set with relations from direct knowledge
Usually users are aware of obvious relations form company relational knowledge. But are not aware of other relational strings. Because users do not have platform to build relational knowledge and upgrade relations.
Figure 4. Data sets with direct and relative knowledge
What figure shows is complete picture, it does not only consist of Data set 1 to 5 with data as direct knowledge and from some obvious relations that are not supported from IT systems with actual data and aggregations and formulas implemented on data derivates. It also consists of many hidden relations that are not obvious.
This is the area of competitive advantage. This is the area where intellectual capital of company can come to true efficiency for company benefit. This is the area where knowledge management has to give support. This is the area where IT systems have to support company. This is complete picture for pilots form introduction story of chapter 7 with missing links between instruments. It is a complete picture of direct and relational knowledge efficiently connected.
Current IT systems are not structured to support Intellectual Capital data collection within knowledge management processes.
Figure 5. Diversification of data through different processing
Management, analysts, business users of data in form of reports based upon relational knowledge expect same from IT systems and their products – reports. If this premises of relational knowledge are not installed as validation rules through company Information System then inner relations of data set are diversified due to many processings and data set gives “distorted” expected picture based upon relational knowledge.
Finance uses set of validation rules as part of relational knowledge to secure financial data quality.
Relational knowledge without direct knowledge is worthless. This are just assumptions in clouds but assumptions based on actual and planning data are something completely different.
Relational knowledge is based upon relations between data sets and upon mutual influences.
Expected picture is something like this:
Figure 6. Data synthesis
Process on can be called non financial data consolidation, integration or synthesis and similar in everyday IT life, IT related projects for BI and DWH. But it is very tough task…
Companies need information layer to apply relations between data. To apply relational knowledge for quick win. Organizing and distributing relational knowledge is additional very important task for Knowledge management and Intellectual Capital.
Knowledge management (KM), set of procedures, processes, methods and actions in company with goal to organize, select, store, publish and distribute of one part of intellectual capital, knowledge. Individual knowledge and/or group knowledge incorporated in organizational processes or practice.
Intellectual Capital (IC) represent non material assets in form of knowledge, expertise, know-how, ideas, experience, relations that contribute to non measurable value of company and measurable value like patents, trademarks, brands and similar, „materialized“ intellectual property.
There are three types of intellectual capital: relational, human and organizational.
Trying to apply relational knowledge on Direct knowledge is current situation in many companies, disappointment with slow implementation, failure of many IT systems (DWH, CRM and etc.)
Relational knowledge axioms
- It is derivate from facts and direct relations (direct knowledge);
- Combines data that do not have actual relation. Not having actual relation means no relation within direct knowledge dana set.
- When relational knowledge gets confirmation from direct knowledge results it becomes direct knowledge and stops being relational knowledge.
- Relational questions can only be answered with relational knowledge.
What is not known should be followed with assumptions, and assumptions should be placed into relations based on direct knowledge. This „invisible“ knowledge is very important because direct knowledge does not provide answers to all questions.
Example of relations: assets are equal to obligations and capital; products A, B and C contribute best to our EBITDA and etc.
 Source: http://en.wikipedia.org/wiki/Intellectual_capital