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Mission

Why a company exists

Vision

What a company wants to be

Values

What the company believes in and how they will behave

Competitive Advantage

are traits or ways an organization has that keep them ahead of their competitors; these are traits that their rivals desire.

Differentiation Advantage

 Customer’s perceived value is greater than its competitors

Low Cost Advantage

Company’s profit margin is higher than its competitors

Differentiation and Low Cost Advantage

The

state where both differentiation and low cost advantage are achieved by a company

Value Chain

A series of activities in an organization’s operations that add value to its final product or service.

Inbound Logistics, Operations, Outbound Logistics, Marketing and Sales, Service

Value Chain Primary Activities (ILOOL MSS)

Inbound Logistics

Receiving and storing of raw materials

Operations

Conversion of raw materials into finished goods

Outbound Logistics

Storing and distributing finished goods to customers

Marketing and Sales

Making the customers aware of the product or service and provides them with an opportunity to buy

Service

Activities after the point of sale (training, installation, and support)

Firm Infrastructure, Human Resource Management, IT, Procurement

Four Value Chain Supporting Activities (FHIP)

Firm Infrastructure

Organizes a firm for it to function

Human Resource Management

Supports all concerns related to staffing

IT

Supports the organization’s IT infrastructure

Procurement

Supports purchasing of materials for production

Corporate Strategy

An organization’s gameplan. Consists of a Goal, Scope, and Means

Objective, Score, and Advantage

The basic elements of a strategy statement are….

Balanced Scorecard

How we will implement and monitor that plan

Measures

A unit-specific term used to describe business objects or entities

(e.g. 1M USD in revenue, 1000 monthly sales)

Metrics

A quantifiable measurement used to track and assess the performance

of a business process (e.g. 10% increase in month on month sales, 5% increase

in subscriber growth)

KPI

A subset of metrics used to measure how well a business is achieving its

goals (e.g. After migrating from On-premise servers to Cloud servers, there is a 30% cost reduction in IT operations)

Data Strategy

A subset of the

corporate strategy

that is specifically

focused on data

Alignment Across Organizations, Value Creation from Data, Improved Decision-Making, Operational Efficiency

A Data Strategy should always have these 4 things. If not, then most likely, it will not be viable (AVIO)

Alignment Across Organizations, Value Creation from Data, Improved Decision-Making, Operational Efficiency

A Data Strategy should always have these 4 things. If not, then most likely, it will not be viable (AVIO)

Data Governance and Stewardship

The exercise of authority,

control, and shared decision-making (planning, monitoring, and enforcement)

over the management of data assets

Data Architecture

 Identifying the needs of the enterprise (regardless of

the structure) and designing and maintaining the master blueprints to meet

those needs. Using master blueprints to guide data integration, control data

assets, and align data investments with business strategy

Data Modeling and Design

The process of

discovering, analyzing, and scoping data requirements, and then representing

and communicating these data requirements in a precise form called the data

model. This process is iterative and involves conceptual, logical, and physical

models

Data Storage and Operations

The design, implementation, and

support of stored data to maximize its value

Data Security

The planning, development, and execution of security

policies and procedures to provide proper authentication, authorization, access,

and auditing of data and information assets within cultural and regulatory

considerations

Data Integration and Interoperability

________ the movement and consolidation of data within and between data stores,

applications, and organizations. _________ is the ability for multiple

systems to communicate.

Document and Content Management

Controlling the capture,

storage, access, and use of data and information stored predominantly outside

relational databases.

Reference and Master Data

Managing reconciled and integrated data

through stewardship and semantic consistency in support of enterprise-wide

needs to share its data assets

Data Warehousing and Business Intelligence

Planning,

implementation, and managing an integrated data system to support

knowledge workers engaged in reporting, query, and analysis

Metadata Management

Planning, implementation, and control activities

that contributes to the ability to process, maintain, integrate, secure, audit and

govern other data

Data Quality Management

The planning, implementation, and control

of activities that apply techniques for collecting and handling data ensuring it

addresses the needs of the enterprise and local consumer is fit for use.

Data Lifecycle

outlines the stages

that a particular set of data goes

through in analytics projects.

Generation, Collection, Processing, Storage, Management, Analysis, Visualization, Interpretation

Stages of Data Lifecycle

Data Pipelines

support the Collection,

Processing, and Storage stages of the Data

Lifecycle. They are micro services that

perform different tasks to prepare the data for

analytics.

Batch, Streaming, Cloud-Native

Types of Data Pipelines

Batch

Data pipelines that are run on a

specific schedule, and on specific

intervals.

Streaming

Data pipelines that are

continuously running and ingesting

data from one place to another.

Cloud-Native

Data pipelines that use cloud

managed services in performing

their tasks. This can be batch or

streaming

Data Mining

Sometimes referred to as

exploratory data analysis due

to analysis of large quantities

of data using statistical,

technical, and business

knowledge.

Cross Industry Standard Process for

Data Mining (CRISP-DM)

provides a

framework to structure our thinking

about data analytics problems.

Data Warehouse

A physically separate store of data transformed from the

operational environment

Dimensional Modeling

is used to

model data in Data Warehouses

Facts

Entities that contain measures

that the business is tracking.

Dimensions

Describe the objects

being measured in the fact

tables. They are sometimes

called Lookup tables because of

how they are used in Analytical

Queries.

Star Schema, Snowflake Schema, Fact Constellation

Common Forms of Dimensional Model

Star Schema

Fact tablesare

surrounded by

dimension tables

forming a star shape.

Each row in the fact

table represents a

measure while each row

in the dimension table

represents an attribute

of the dimension.

Snowflake Schema

An extension of the Star

Schema where

dimensions have other

dimensions linked to

them to minimize

redundancy. (There is

some form of

normalization)

Fact Constellation

Multiple fact tables

sharing common or

conformed dimension

tables.

Cube

a truly multidimensional

data structure for capturing and

analyzing data. Mathematically, a

hypercube, it can support multiple

dimensions and hierarchies.

Hierarchies

dimensions that

are organized together in a

parent-child structure to define

granularity.

Roll up

summarize data by climbing up hierarchy or by dimension

reduction

Drill Down

reverse of roll-up from higher level summary to lower

level summary or detailed data, or introducing new dimensions

Slice and Dice

Project and select

Pivot

reorient the cube, visualization, 3D to series of 2D planes