Introducing Total Controlling Concept
Introducing Total Controlling Concept When two figures with different origins are compared it is relation. Assumption based upon relation is relational knowledge (RK). For hidden wiki example when analyst wants to compare two different KPIs, reports or financial statements, analyst wants to gain RK with basis in direct knowledge.
Whenever relation is used to compare two data it can be called relational query. 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 RK is direct knowledge (DK): facts from production systems, relations and formulas in IT systems, reports, performance indicators based upon raw data and formulas (direct relations).
Example of RK 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 DK 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 RK, assuming that figures should be aligned despite the fact that their life cycle was different as shown on
Figure 2. Diversification of data through different processing.
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 DK and RK.
RK is made upon direct knowledge and is presented together with DK.
Many relations form relational knowledge are very well hidden since current IT platforms do not support combined operations with direct and RK 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 RK and upgrade relations.
Figure 4. Data sets with direct and RK
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 derivatives. 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 RK 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.