BA 304 Ch 6 Data Business Intelligence

the 4 Primary Traits of the Value of Data

Type, Timeliness, Quality and Governance 

Transactional Data

Encompasses all of the data contained within a single business process or unit of work, and its primary purpose is to support the performing of daily operational tasks

Analytical Data

Encompasses all organizational data, and its primary purpose is to support the performing of managerial analysis tasks

Data Timeliness

is an aspect of data that depends on the situation.

Real-time data and real-time systems

Data Quality

Accurate, Complete, Consistent, Timely and Unique.

Data inconsistency and data integrity issues and data steward.

The 4 Primary Sources of Low-Quality Include:

1. Customers intentionally enter inaccurate data to protect their privacy

2. Different entry standards and formats

3. Operators enter abbreviated or erroneous data by accident or to save time

4. Third party and external data contains inconsistencies, inaccuracies, and errors

Potential business effects resulting from low quality data include

• Inability to accurately track customers

• Difficulty identifying valuable customers

• Inability to identify selling opportunities

• Marketing to nonexistent customers

• Difficulty tracking revenue

• Inability to build strong customer relationships

Understanding the Benefits of Good Data

• High quality data can significantly improve the chances of making a good decision

• Good decisions can directly impact an organization's bottom line

• A data steward is responsible for ensuring data policies and procedures are implemented across an organization

Data Governance

Refers to the overall management of the availability, usability, integrity, and security of company data.

Master Data Management (MDM) and data validation

Database

maintains data about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)

Entities

A person, place, thing, transaction, or event about which information is stored

Data Element

smallest or basic unit of data

Data Model

Logical data structures that detail the relationships among data elements using graphics or pictures

Metadata

details about data

Data Dictionary

Compiles all of the metadata about the data elements in the data model

Primary Key

A field (or group of fields) that uniquely identifies a given entity in a table

Foreign Key

A primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables

Database Management System (DBMS)

Creates, reads, updates, and deletes data in a database while controlling access and security

Structured Query Language (SQL)

Asks users to write lines of code to answer questions against a database

Data Analysis Cycle

Collect (identify data sources), Analyze (ask the right questions), Visualize (craft your data story) and Communicate (influence and persuade).

Business Intelligence Dashboards

Track corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis.

Consolidation

The aggregation of data from simple roll-ups to complex groupings of interrelated information.

Drill down

Enables users to view details, and details of details, of information.

Slice-and-dice

The ability to look at information from different perspectives.

Pivot

Rotates data to display alternative presentations of the data.

Estimation Analysis

Determines values for an unknown continuous variable behavior or estimated future value.

Affinity Grouping Analysis

Reveals the relationship between variables, along with the nature and frequency of the relationships.

Cluster Analysis

A technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible.

Classification Analysis

The process of organizing data into categories or groups for its most effective and efficient use; for example, groups of political affiliation and charity donors.

Collaborative Filtering

A technique used in recommendation systems to provide personalized recommendations to users based on their similarity to other users. It is based on the idea that people who have similar preferences or behaviors in the past are likely to have similar preferences in the future.

Recommendation Engine

A data mining algorithm that analyzes a customer’s purchases and actions on a website and then uses the data to recommend complementary products.

Market Basket Analysis

Evaluates such items as websites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services.

Data warehouse

A logical collection of data—gathered from many different operational databases—that supports business analysis activities and decision-making tasks

Data Aggregation

The collection of data from various sources for the purpose of data processing

Extraction, Transformation and Loading (ETL)

A process that extracts data from internal and external databases, transforms it using a common set of enterprise definitions, and loads it into a data warehouse

Data Mart

Contains a subset of data warehouse data

Information Cube

The common term for the representation of multidimensional data

Data Lake

A storage repository that holds a vast amount of raw data in its original format until the business needs it

Data Scrubbing

A process that weeds out and fixes or discards inconsistent, incorrect, or incomplete data

Poor Data Quality

The data, if available, were often incorrect or incomplete. Therefore, users could not rely on the data to make decisions.

Inconsistent Data Definitions

Every department had its own method for recording data so when trying to share information, data did not match and users did not get the data they really needed

Ineffective Direct Data Access

Most data stored in operational databases did not allow users direct access; users had to wait to have their queries or questions answered by MIS professionals who could code SQL.

Lack of Data Standards

Managers needed to perform cross-functional analysis using data from all departments, which differed in granularities, formats, and levels.

Inadequate Data Usefulness

Users could not get the data they needed; what was collected was not always useful for intended purposes.

Distributed Computing

Processes and manages algorithms across many machines in a computing environment

Ledger

Records classified and summarized transactional data

Blockchain

A type of distributed ledger, consisting of blocks of data that maintain a permanent and tamper-proof record of transactional data

Proof-of-work

A requirement to define an expensive computer calculation, also called mining, that needs to be performed in order to create a new group of trustless transactions (blocks) on the distributed ledger or blockchain

Hash

A function that converts an input of letters and numbers into an encrypted output of a fixed length

Blocks

Data structures containing a hash, previous hash, and data