Presentación sobre: Datos: Inteligencia de Negocios.

1. Semester: 8° Group: HP
Teacher: María Guadalupe Pedraza Vázquez
Team # - Integrantes
1._Castañon Escobedo Isaac Fernando #1947979
2._ De León Elizondo Juan Pablo #1931893
3._ De La Cruz Rentería Erick Omar #2079145
4._ García Vázquez Luis Gerardo #1848285
5._Guerrero Treviño Luis Enrique #2079572
6._Vázquez Macías Luis Fernando #1961572
7._Villalobos Hernández Lorena Lizeth #1965677
New Leon Autonomous University
School Of Public Accounting and Administration
Learning unit: Foreign Language and Culture
Topic 6: Data: Business Intelligence
Section 6.1: Data, Information, and Databases
Date: University City, April 22, 2024

2. Chapter 6 - Business Inteligence
INDEX
4 - 5
Data Quality 6 - 8
Data steward 9 - 11
Manipulation of the data 12 - 16
Attributes 17 - 21
Relational database 22 - 24
Data latency 25 - 29
Dynamic / Nearpod 32
End 33
Introduction 3
Summary 30 - 31

3. LEARNING
OUTCOMES
SECTION
6.1
INTRODUCTION
Data Quality
Storing Data Using
Relational Database
Management System
Using a Relational
Database for Business
Advantages
6.1 Explain the four
primary traits that
determine the value of
data.
6.2 Describe a database, a
database management
system, and the relational
database model.
6.3 Identify the business
advantages of a relational
database.
This chapter introduces the concepts of
information and data and their relative
importance to business professionals and firms.

4. CHAPTER 6 - BUSINESS INTELLIGENCE
SECTION 6.1- DATA, INFORMATION AND DATABASES
• Data granularity: Refers to the extent of detail within the data (fine and detailed or coarse and abstract).
Data is everywhere in an organization.
Managers in sales, marketing, human
resources, and management need data to
run their departments and make daily
decisions.

5. Analytical data encompasses all organizational data, and its primary purpose is to support the performance of
managerial analysis tasks.
The Four Primary Traits of the Value of Data
~ Data Type
~ Data Timeliness
~ Data Quality
~Data Governance
Data Type: Transactional and Analytical
Transactional data encompasses all of the data contained within a single business process or unit of work, and its
primary purpose is to support daily operational tasks.
Data Timeliness
Real-time data: Means immediate, up-to-date
data.
Real-time system: Provides real-time data in
response to requests.

6. Data Quality
Business decision are only as good as the quality of the data used to make them.
Data inconsistency: Occurs when the same data element has different values. Take, for example, the amount of work that needs to
occur to update a customer who had changed her last name due to marriage.
Data integrity issues: Occur when a system produces incorrect, inconsistent, or duplicate data. Data integrity issues can
use managers to consider the system reports invalid and make decisions based on other sources.
Five Common Characteristics of High-Quality Data

7. Completeness.
1.
2. Another issue with completness
3. Consistency
4. Accuracy
5. Another issue with accuracy
6. Another issue with completeness
Example of low-quality data

8. Business Driven MIS
Determining data quality issues
Real people magazine is geared toward working individuals and provides articles and advice on everything from car maintenance to
family planning. Create a report detailing all the issues with the data, potencial causes of the data issues, and solutions the company
can follow to correct the situation.
Knowing how low-quality data
issues typically occur can help a
company correct them. Addressing
these errors will significantly
improve the quality of company
data and value to be extracted from
it.
The four primary reasons for low-quality data are:
Online customers intentionally enter inaccurate data to protect their privacy
1.
Different system have different data entry standards and formats.
2.
Data-entry personnel enter abbreviated data to save time or erroneous data by accident.
3.
Third-party and external data contains inconsistencies, inaccuracies, and errors.
4.

9. Data steward: Responsible for ensuring the policies and procedures are
implemented across the organization and acts as a liaison between the MIS
department and the business.
Data stewardship: The management and oversight of an organization’s
data assets to help provide business users with high-quality data that is easily
accesible in a consistent manner.
Understanding the Benefits of
Using High-Quality Data

10. Data governance: Refers to the overall management of the availability,
usability, integrity, and security of company data.
Master data management (MDM): The practice of gathering data and
ensuring it is uniform, accurate, consistent, and complete, including such
entities as customers, suppliers, products, sales, employees, and other critical
entities that are commonly integrated across organizational systems, MDM
is commonly included in data governance.
Data Governance

11. Database: Maintains data about various types of objects (inventory), events
(transactions), people (empleyees), and places (warehouses).
Database management systems (DBMS): Creates, reads, updates, and
deletes data in a database while controlling access and security.
Storing Data Using a Relational
Database Management Systems

12. A
Manipulation of the data in the database
Structured query
language (SQL): Asks
users to write lines of
code to answer
questions against a
database.
Query-by-example
(QBE) tool: Helps users
graphically design the
answer to a question.
against a database.

13. Relationship of Database, DBMS, and
User

14. A
Data Cleansing Debate
Human resources: You have sent out a quick electronic survey to all 500 of your
employees to determine job satisfaction. Only 30 percent of the employees completed
the survey with 100 percent completion: 20 percent completed the survey with 50
percent completion; and 50 percent chose not to complete the survey.
Salary comparisons: One of your employees has collected
data from four external sources on average salaries for each
job posting in your company. The employee recently quit
and did not document where the data was collected from.
Marketing: You have sent out a quick electronic survey to potential
and current customers about an exciting new product. The survey
is optional and does not track who is completing the survey.

16. Storing Data Elements in Entities
and Attributes
Relational database model: Stores data in the form of logically related
two- dimensional tables.
Relational database management system: Allows users to create, read,
update, and delete data in a relational database. The relationships in the
relational database model help managers extract this data. Illustrates the
primary concepts of the relational database model: entities, attributes, keys,
and relationships.
Entity (also referred to as a table): Stores data about a person, place, thing,
transaction, or event. The entities, or tables, of interest. Notice that each
entity is stored in a different two-dimensional table (with rows and
columns).

17. Attributes: The data elements
associated with an entity. In the
attributes for the entity TRACKS are
TrackNumber, TrackTitle,
TrackLength, and RecordingID.
Attributes for the entity
MUSICIANS are MusicianID,
MusicianName, Musician Photo, and
MusicianiVotes.
Storing Data Elements in
Entities and Attributes.

18. Record: A collection of related
data elements (in the
MUSICIANS table, these include
"3, Lady Gaga, Gagatiff, Do not
bring young k..
-ve shows"). Each record in
an entity occupies one row in its
respective table.
To manage and organize various entities within
the relational database model, you use primary
keys and foreign keys to create logical
relationships. Let's jump into an analysis of a
primary key.
* Primary key: A field (or group of fields) that
uniquely identifies a given record in a table. In
the table RECORDINGS, the primary key is the
field RecordingID that uniquely identifies each
record in the table.
Record
Creating Relatlonships
through Keys

19. BUSINESS DRIVEN ANALY
LIES, LIES, AND MORE LIES: HOW TO LIE WITH
STATISTICS.
Analyze the following common big data analysis errors and rank them depending on the
error that would cause the most damage for problems with inaccurate analysis to least
damage to a data analysis.
• Analysis Paralysis:
Impossible to make a
decision with the
overwhelming amount
of data collection.
• Lack of a Data Steward:
Without a data steward, the rules
to how data is collected are
lacking and you find duplicates,
columns being used incorrectly,
and inaccurate input.
• Data Silos: Data is the new oil. As a
result, countless companies are collecting
and storing as much of it as they can and
letting it sit idle because they do not have
a need or direction for analysis. Don't just
let your data sit in a silo. It has the power
to improve operations. inform your
product road map. and solve longstanding
obstacles-but only if you actually use it.
• Lack of Analytics:
Businesses are finding
they do not have the
talent or expertise to
properly analyze the
massive amounts of data
they are collecting.

20. Coca-Cola Relational Database Example
4 Figure 6.9 illustrates the primary concepts of the relational
database model for a sample order of soda from Coca-Cola.
Figure 6.9 offers an excellent example of how data is stored
in a database. For example, the order number is stored in the
ORDER table, and each line item is stored in the ORDER
LINE table. Entities include CUSTOMER, ORDER,
ORDER LINE, PRODUCT, and DISTRIBUTOR.
Attributes for CUSTOMER include Customer ID, Customer
Name, Contact Name, and Phone.
Attributes for PRODUCT include Product ID. Product
Description, and Price. The columns in the table contain the
attributes.
Foreign key: A primary key of
one table that appears as an
attribute in another table and
acts to provide a logical
relationship between the two
tables.
• Thinking You Control Your
Data: No business or individual has
full control over their data. The
way things work now, data is
copied every time a new person or
application wants to work with it.

21. Consider Hawkins Shipping, one of the distributors
appearing in the DISTRIBUTOR table. Its primary key,
Distributor ID, is DEN8001. Distributor ID also appears as
an attribute in the ORDER table. This establishes that
Hawkins Shipping (Distributor ID DEN8001) was
responsible for delivering orders 34561 and 34562 to the
appropriate customers). Therefore, Distributor ID in the
ORDER table creates a logical relationship (who shipped
what order) between ORDER and DISTRIBUTOR.

22. Using a relational database
for business advantages
Many businesses managers are familiar
with excel and other spreadsheets
programs they can use to store business
data. Although spreadsheets are excellent
for supporting some data analysis, they
offer limited functionality in terms of
security, accessibility, and flexibility and
can rarely scale to support businesses
growth. From a business perspective,
relational databases offer many
advantages over using a text document or
a spreadsheet, as displayed in:

23. Business driven ethics and security.
Unethical Data mining
Mining large amounts od data can create a number of
benefits for business, society, and government, but it can
also create a number of ethical questions surrounding an
invasion of privacy or misuse od data.
Facebook recently came under fire for its data
mining practices because it followed 700,000
accounts to determine whether posts with highly
emotional content are more contagious.

24. Databases tend to mirror business structure, and a database needs to handle changes
quickly and easily, just as any business needs to be able to do. Equally important, databases
need to provide flexibility in allowing each user to access the data in whatever was best
suits his or her needs. The distinction between logical and physical views is important in
understanding flexible database views.
Physical view of data: Deals with the physical storage o data on a storage device.
Logical view of data: Focuses on how induvial users logically access data to meet their own
particular business needs.
Increased Flexibility

25. Denotes the advancement in a system's or application's capability to adapt and
handle growing demands or usage without compromising its efficiency or speed.
Scalability refers to the system's ability to accommodate an expanding workload or
user base by efficiently allocating resources and distributing tasks across multiple
components or servers.
Data latency: The time it takes of data to be stored or retrieved.
Increased Scalability and Performance

26. Refers to the practice of minimizing the duplication of data within a
system or dataset. This involves identifying and eliminating
unnecessary or duplicated information to streamline storage,
improve data management efficiency, and reduce the risk of
inconsistencies or errors.
Data redundancy: The duplication of data, or the storage of the
same data in multiple places.
Reduced Data Redundancy

27. Increased Data Integrity (Quality)
Business rule: Defines how a company
performs certain aspects of its business, and
typically results in either a yes/no or tue/false
answer. Stating that merchandise returns are
allowed within 10 datys of purchase is a
example of business rule.
Data integrity: A measure of
the quality of data.
Integrity constraints: Rules
that help ensure the quality of
data.

28. The implementation of measures and protocols aimed at protecting
sensitive information from unauthorized access, disclosure, alteration,
or destruction. This involves deploying various security technologies,
such as encryption, access controls, firewalls, and intrusion detection
systems, to safeguard data throughout its lifecycle.
Identity management: A broad administrative area that
deals with identifying individuals in a system (such as a
country, a network, or an enterprise) and controlling their
access to resources within that system by associating user
rights and restrictions with established identity.
Increased Data Security

29. Bussiness Driven Globalization

30. Summary
Learning Outcome 6.1: Explain the four primary tralts that determine the value of data.
Information is data converted into a meaningful and useful context. Information can tell an organization
how its current operations are performing and help it estimate and strategize about how future
operations might perform. It is important to understand the different levels, formats, and granularities
of data, along with the four primary traits that help determine the value of data, which include (1) data
type: transactional and analytical; (2) data timeliness; (3) data quality; and (4) data governance.
Learning Outcome 6.2: Describe a database, a database management system, and the relatlonal database
model.
A database maintains data about various types of objects (inventory), events (transactions), people
(employees), and places (warehouses). A database management system (DBMS) creates, reads, updates,
and deletes data in a database while controlling access and security. ADBMS provides methodologies for
creating, updating, storing, and retrieving data in a database. In addition, a DBMS provides facilities for
controlling data access and security, allowing data sharing, and enforcing data integrity. The relational
database model allows users to create, read, update, and delete data in a relational database.

31. Learning Outcome 6.3: Identify the business advantages of a relational
database.
Many business managers are familiar with Excel and other spreadsheet
programs they can use to store business data. Although spreadsheets are
excellent for supporting some data analysis, they offer limited functionality in
terms of security, accessibility, and flexibility and can rarely scale to support
business growth. From a business perspective, relational databases offer many
advantages over using a text document or a spreadsheet, including increased
flexibility, increased scalability and performance, reduced data redundancy,
increased data integrity (quality), and increased data security.
Summary

32. Activity Link
Nearpod
https://app.nearpod.com
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33. Thank you
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