Regulatory Сompliance Quarter

Business Intelligence Quarter (hereinafter, the Quarter) is the second of four lectures of Organizational Quadrivium (hereinafter, the Quadrivium):

• The Quarter is designed to introduce its learners to enterprise discovery, or, in other words, to concepts related to obtaining data needed to administer the enterprise effort; and
• The Quadrivium examines concepts of administering various types of enterprises known as enterprise administration as a whole.

The Quadrivium is the first of seven modules of Septem Artes Administrativi, which is a course designed to introduce its learners to general concepts in business administration, management, and organizational behavior.

Outline

The predecessor lecture is Market Engagements Quarter.

Concepts

1. Business intelligence. Information that managers can use to make more effective strategic decisions.
   • Anonymization. The severing of links between people in a database and their records to prevent the discovery of the source of the records, and to maintain privacy and confidentiality.
   • Association. A link between two elements or objects in a diagram.
   • Systematic study. Looking at relationships, attempting to attribute causes and effects, and drawing conclusions based on scientific evidence.
   • Behavioral analytics. Using data about people's behavior to understand intent and predict future actions.
2. Data curation. The management of data throughout its lifecycle, from creation and initial storage to the time when it is archived for posterity or becomes obsolete, and is deleted. The main purpose of data curation is to ensure that data is reliably retrievable for future research purposes or reuse.
   • Data validation. The process of ensuring that data have undergone data cleansing to ensure they have data quality, that is, that the data is both correct and useful.
   • Data gap. Indentification of data gaps in available information in reference to a particular procurement.
3. Data model. A model that depicts the logical structure of data, independent of the data design or data storage mechanisms. A data model is the result of a collaborative effort between end business users and IT database analysts. The first step is to define in plain English what data the business needs in order for its various functions to communicate with each other, and how this data must be ordered and structured so it makes the most sense. The second step is for the data analysts and other IT staff to devise a technical data base, a data storage and security plan, and a plan that enables application and analytic report development using this data. Together, these processes result in a data model for the business.
   • Optionality. Defining whether or not a relationship between entities in a data model is mandatory. Optionality is shown on a data model with a special notation.
   • Cardinality. The number of occurrences of one entity in a data model that are linked to a second entity. Cardinality is shown on a data model with a special notation, number (e.g., 1), or letter (e.g., M for many).
   • Data flow diagram (DFD). An analysis model that illustrates processes that occur, along with the flows of data to and from those processes.
   • State diagram. An analysis model showing the life cycle of a data entity or class.
   • Sequence diagram. A type of diagram that shows objects participating in interactions and the messages exchanged between them.
   • Class model. A type of data model that depicts information groups as classes.
   • Snapshot. A view of data at a particular moment in time.
4. Data dictionary. An analysis model describing the data structures and attributes needed by the system.
   • Data entity. A group of related information to be stored by the system. Entities can be people, roles, places, things, organizations, occurrences in time, concepts, or documents.
   • Attribute. A data element with a specified data type that describes information associated with a concept or entity.
   • Glossary. A list and definition of the business terms and concepts relevant to the solution being built or enhanced.
   • Jargon. Specialized terminology or technical language that members of a group use to communicate among themselves.
5. Data manipulation. The action of manipulating data in a skillful manner.
   • Filtering. A sender's deliberate manipulation of information to make it appear more favorable or be seen more favorably by the receiver.
   • Filter. A mechanism that includes or excludes specific data from reports based upon what the user decides to filter (e.g., to tightly tailor a report, you might strictly want records of customers between the ages of 25 and 35 who like skiing, but you want to exclude everyone else).
6. Categorization.
   • Structural rule. Structural rules determine when something is or is not true or when things fall into a certain category. They describe categorizations that may change over time.
   • Knowledge area. A group of related tasks that support a key function of business analysis.
   • Schema. The structure that defines the organization of data in a database.
   • Data mart. A small data repository that is focused on information for a specific subject area of the company, such as Sales, Finance, or Marketing.
   • Data warehouse. A data repository that deals with multiple subject areas (or data marts).
7. Big data. The vast amount of quantifiable information that can be analyzed by highly sophisticated data processing.
   • Contextual data. A structuring of big data that attaches situational contexts to single elements of big data to enrich them with business meaning (e.g., instead of a customer record that tells you the customer's name and address, data appended to this record data also gives the customer's buying preferences, which is gathered from recent web activity data). The result is a more complete understanding of the customer and her lifestyle.
   • Multipolar analytics. A distributed big data model where data is collected, stored, and analyzed in different areas of the company instead of being centrally located and analyzed.
8. Data visualization. A method of putting data in a visual or a pictorial context as a way to assist users in better understanding what the data are telling them (e.g., a map is a way to visualize which areas of the country get the most rainfall).
   • Data point. An individual item on a graph or a chart.
9. Forecasting. A process that uses historical data to predict future outcomes.
   • Forecast. Prediction of outcome.
   • Qualitative forecasting. Forecasting that uses the judgment and opinions of knowledgeable individuals to predict outcomes.
   • Quantitative forecasting. Forecasting that applies a set of mathematical rules to a series of past data to predict outcomes.
   • Scenario. A consistent view of what the future is likely to be.

Roles

1. Data broker. A business that collects personal information about consumers and sells that information to other organizations.

Methods

1. Data-analysis technique.
   • Investigation. The formal or systematic examination of data sources that uses one or more data-gathering techniques and is conducted in order to gather data and/or assess data reliability.
   • Slice and dice. Data manipulation tools in reporting or spreadsheet software that allow users to view data from any angle.

Instruments

1. Data-analysis tool.
   • Database. A collection of data arranged for convenient and quick search and retrieval by applications and analytics software.
   • Management information system (MIS). A system used to provide management with needed information on a regular basis.
   • Digital tool. Technology, systems, or software that allow the user to collect, visualize, understand, or analyze data.
2. Performance management system. Establishes performance standards used to evaluate employee performance.

Practices

The successor lecture is Workgroup Design Quarter.