2. CHAPTER TWO: LITERATURE REVIEW
2.7 Types of Data
Koronios and Baškarada (2013:6) explore the relationships between Data, Information, Knowledge, Wisdom (DIKW) hierarchy. These concepts of data, information and knowledge are fundamental in basic building blocks of the Information Science field.
Raw data is unprocessed facts without any analysis. Information is data that has been interpreted by the user and has meaning. Knowledge is a combination of information, experience and insight that may benefit the organisation. Koronios and Baškarada (2013:7) explain Data, Information, Knowledge, Wisdom (DIKW) hierarchy relationships in Table 2-2.
Table 2-2: Data, information, knowledge and wisdom hierarchy relationship Data, Information, Knowledge and Wisdom hierarchy relationship
Wisdom
Wisdom is accumulated knowledge, which allows one to understand how to apply concepts from one domain to a new situation or problem.
Wisdom is the highest level of abstraction, with vision foresight and the ability to see beyond the horizon.
Wisdom is the ability to act critically or practically in any given situation. It is based on ethical judgement related to an individual’s belief system.
Knowledge
Knowledge is the combination of data and information, to which is added expert opinion, skills, and experience, to result in a valuable asset which can be used to aid decision making.
Knowledge is data and/or information that has been organised and processed to convey understanding, experience, accumulated learning, and expertise as they apply to a current problem or activity.
Knowledge builds on information that is extracted from data. While data is a property of things, knowledge is a property of people that predisposes them to act in a particular way.
Data, Information, Knowledge and Wisdom hierarchy relationship
Inform ation Information is data which adds value to the understanding of a subject.
Information is data that has been shaped into a form that is meaningful and useful to humans.
Information is an aggregation of data that makes decision making easier.
Data
Data has no meaning or value because it is without context and interpretation.
Data is discrete, objective facts or observations, which are unorganised and unprocessed, and do not convey any specific meaning.
Data items are an elementary and recorded description of things, events, activities and transactions.
Source: Koronios and Baškarada (2013:7)
Figure 2-8: The wisdom hierarchy mapping to types of information systems Source: Rowley (2007:n.p.)
Figure 2-8 describes the progression of raw data through to wisdom derived from data. This, in turn, provides information to managers which assists in knowledge and, ultimately, is converted to wisdom by managers through the process of rationalisation.
Within the organisation under study, managers need to rationalise and process data, thereby interpreting the data to achieve wisdom in strategic decision making. At an operational level, unit management utilises SAP ECC6 for operational reporting to address day-to-day operations. For tactical decision making, operations and divisional managers utilise SAP ECC6 to support day-to-day business operations.
For strategic decisions, executive managers should be utilising SAP Business Intelligence for analytics and drill-down reporting. Based on statistics, a low average
Management Information Systems Information
Transaction Processing Systems Data
Decision Support Systems Knowledge Expert Systems
Wisdom
of eight percent of the managers are utilising SAP Business Intelligence for strategic decision making.
2.7.1 Master Data and Transactional Data
Data is fundamental to reporting capabilities and data quality is important to user confidence of the extracted information. “Within SAP there are three basic data types, namely, master data, transactional data and historical data. Master data may include clients, customers, products, employees, inventories, suppliers, stores, assets and contracts. Business operations revolve around master data. The data is shared by multiple users, across the entire organisation” (Stephen & Kleiner, 2011:24).
“The three types of data involved in a SAP system are:
Master Data. Application master data tends to be more static once defined.
Most master data can be driven by the legacy applications. Examples include vendors, customers, charts of accounts, assets, material masters, info records, etc.
Transactional Data. Transactional data is current and outstanding transaction data that needs to be captured from the legacy system and defined to the SAP ECC6 applications for business process completion. Examples include accounting documents, open purchase orders, open sales orders, etc.
Historical Data. Historical data needs to be brought over from the legacy system to the SAP ECC6 System for reference purposes. Examples include closed purchase orders, closed sales orders, summary general ledger information, etc.” (SAP, 2016b).
Within the organisation under study, master data, transactional data and historical data are the drivers of information reporting within SAP Business Intelligence.
Master data is critical to information pertaining to employees, vendors, financial information, project portfolio management, debt management, human resource position, profit centre, cost centre information and capital project information.
Transactional data is the day-to-day operations. This information is in the SAP ECC6 system and uploaded into SAP Business Intelligence by means of the daily batch. It is of critical importance that the batch is successful and that it reconciles with the SAP ECC6 system. Historical data within SAP Business Intelligence is moved to different cubes. This ensures that the data is still available for reporting.
The current financial year data is performance enhanced with business intelligence
been adopted because BI-A is expensive and a limited amount of space is available for enhancing performance. The technical team ensures that aggregates are loaded on the historical cubes to maintain and improve performance. However, historical cubes with large volumes of data are proving to be slow, resulting in low usage of those specific reports.