Regulatory Сompliance Quarter

Business Intelligence Quarter (hereinafter, the Quarter) is the first of four lectures of Operations 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 Bookkeeping Quarter.

Concepts

1. Business intelligence. Information that managers can use to make more effective strategic decisions.
   • Information.
   • Enterprise information.
2. Data processing.
   • Controlled processing. A detailed consideration of evidence and information relying on facts, figures, and logic.
   • Automatic processing. A relatively superficial consideration of evidence and information making use of heuristics.
3. Data analysis.
   • Data reliability. The trustworthiness of data; this trustworthiness is a result of analysis of (a) content reliability, (b) source reliability, and (c) data intent.
     1. Content reliability.
     2. Source reliability.
   • Data intent. Intention or purpose with which data was created.
     1. Fact-based data. Data created with the intent to provide its users with facts.
     2. Opinion-based data. Data created with the intent to provide its users with opinions.
     3. Agenda-based data. Data created with the intent to provide its users with information desired to accommodate one's business goals or agendas.
4. Data curation.
5. Content audit. Reviewing and cataloguing an existing repository of content.
   • Analytics. A broad term that encompasses a variety of tools, techniques and processes used for extracting useful information or meaningful patterns from data.
   • 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.
6. Big data. The vast amount of quantifiable information that can be analyzed by highly sophisticated data processing.
7. Productivity. The amount of goods and services produced divided by the inputs needed to generate that output.
   • Productivity. The combination of the effectiveness and efficiency of an organization.
   • Organizational effectiveness. A measure of how appropriate organizational goals are and how well those goals are being met.
   • Graphic rating scale. An evaluation method in which the evaluator rates performance factors on an incremental scale.
   • Breakeven analysis. A technique for identifying the point at which total revenue is just sufficient to cover total costs.
8. 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.

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.

Instruments

1. Data-analysis tool.
   • Database.
   • 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

• Ad hoc query. The ability to create a one-off, "on demand" report from BI or analytics software that answers a specific business question.
• 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.
• Behavioral analytics. Using data about people's behavior to understand intent and predict future actions.
• Balanced scorecard. A performance management tool that holistically captures an organization's performance from several vantage points (e.g., sales results vs. inventory levels) on a single page.
• 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.
• Database. A collection of data arranged for convenient and quick search and retrieval by applications and analytics software.
• Data broker. A business that collects personal information about consumers and sells that information to other organizations.
• 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 model. 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.
• Data point. An individual item on a graph or a chart.
• 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 warehouse. A data repository that deals with multiple subject areas (or data marts).
• 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).
• Gap analysis. A study of whether the data that a company has can meet the business expectations that the company has set for its reporting and BI, and where possible data gaps or missing data might exist.
• Key performance indicator (KPI). A metric a business measures its progress against when performing BI analytics to determine whether it is meeting its goals. The results are reported in the form of a dashboard or a scorecard report that enables executives, managers, and employees to assess performance, and whether a given goal (or metric) is being met, exceeded, or missed.
• Metadata. Data that gives information about what the primary data is about (e.g., if a photo is the primary data, its metadata might consist of what its resolution is, when the photo was taken, etc.).
• Metrics. Measures of performance that observe progress and evaluate trends within an organization.
• 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.
• Schema. The structure that defines the organization of data in a database.
• Slice and dice. Data manipulation tools in reporting or spreadsheet software that allow users to view data from any angle.
• Snapshot. A view of data at a particular moment in time.

The successor lecture is Enterprise Architecture Quarter.