UNIT 4 PREPARE DATA FOR STAKEHOLDERS (1).pptx

FUNDAMENTALS OF DIGITAL
MARKETING AND ANALYTICS
AMCA0214

UNIT 4
PREPARE DATA FOR EXPLORATION
AND STAKEHOLDER

Noida Institute of Engineering and Technology, Greater Noida
Introduction
PRIYA PORWAL
Assistant Professor
MCA
Department
Fundamentals of Digital Marketing
and Analytics
MCA I year
10-05-2024

• To help students understand digital marketing practices, the inclination of
digital consumers, and the role of content marketing, provide an understanding
of the concept of E-commerce and developing marketing strategies in the
virtual world, and impart learning on various digital channels.
• how to acquire and engage consumers online, provide insights on building
organizational competency by way of digital marketing practices and cost
considerations, and develop an understanding of the latest digital practices for
marketing and promotion.
10-05-2024
Noida Institute of Engineering and Technology, Greater Noida

10-05-2024
Text Books/Reference Books

• Stakeholders' expectations are one of the most important.
• Stakeholders are people that have invested
- time,
- interest, and
- resources
- into the projects that you'll be working on as a data analyst.
• The work that you perform will be needed by them
• It's so important to make sure that
- your work lines up with their needs and
- you need to communicate effectively with all of
the stakeholders across your team.
BALANCING NEEDS

• Your stakeholders will want to discuss things like
- the project objective,
- what you need to reach that goal, and
- any challenges or concerns you have.
• These conversations help build
- trust and
- confidence in your work.
• Example
• A company's human resources department.
- The company has experienced an increase in its turnover rate,
- which is the rate at which employees leave a company.
BALANCING NEEDS

• The Vice President of HR at this company is
- interested in identifying any
- shared patterns
- across employees who quit and
- see if there's a connection between employee productivity and
engagement.
• As a data analyst,
- you should focus on
- the HR department's question and
- help find them an answer.
• But the VP might be
- too busy to manage day-to-day tasks or
- might not be your direct contact.
BALANCING NEEDS

• For this task, you'll be updating
- the project manager more regularly.
• Project managers are in charge of
- planning and executing the project
• Part of the project manager's job is
- keeping the project on track and
- overseeing the progress of the entire team.
• In most cases, you'll need to give them regular updates,
• let them know
- what you need to succeed and
- tell them if you have any problems along the way.
BALANCING NEEDS

• You might also be working with other team members.
• For example, HR administrators
- will need to know the data you're using so that
-they can effectively gather employee data.
• You might even be working with other data analysts who are
- covering different aspects of the data.
• It's so important that you know
- who are the stakeholders and
- other team members are
- in the project so that you can
• communicate with them effectively and
• give them what they need to move forward in their own roles on the project.
BALANCING NEEDS

• By analyzing company data, in the example
• You see a
- decrease in employee engagement and
- performance after their first 13 months at the company,
• which could mean that employees
- started feeling demotivated or
- disconnected from their work
- and then often quit a few months later.
• Another analyst who focuses on hiring data also shares that
- the company had a large increase in hiring around 18 months
ago.
BALANCING NEEDS

• You communicate this information with all your
- team members and
- stakeholders and
• They provide feedback on how to
- Share this information with your VP.
• In the end, your VP decides to
- implement an in-depth manager check-in with employees who are
- about to hit their 12-month mark at the firm
- to identify career growth opportunities, which
- reduces the employee turnover
- starting at the 13-month mark.
• This is just one example of how you might
• balance needs and expectations
BALANCING NEEDS

• There are three common stakeholder groups that you
might find yourself working with:
1)the executive team,
2)the customer-facing team, and
3)the data science team
Executive team
• The executive team provides
- strategic and operational leadership to the company.
- They set goals,
- develop strategy, and
- make sure that strategy is executed effectively.
Managing Stakeholders Expectations

The executive team might include
- vice presidents,
- the chief marketing officer, and
- senior-level professionals who help plan and direct
the company’s work.
These stakeholders
- think about decisions at a very high level and
- they are looking for the headline news about your
project first.
- They are less interested in the details

• For example, you might find yourself
- working with the vice president
- to understand the rate of employee absences.
- A marketing director might look to you
- for competitive analyses.
• Your project manager will be
- overseeing the progress of the entire team, and
- you will be giving them more regular updates than someone like the
vice president of HR.
- They are able to give you what you need to move forward on a project,
- including getting approvals from the busy executive team
- can help you pinpoint the needs of the executive stakeholders

• Customer-facing team
• The customer-facing team includes anyone in an organization who
has some level of interaction with customers and potential
customers.
• Typically they
- compile information,
- set expectations, and
- communicate customer feedback to other parts of the internal
organization.
• These stakeholders
- have their own objectives and
- may come to you with specific tasks.
• your analysis and presentation
- focuses on what is actually in the data–
- not on what your stakeholders hope to find.

You could be working with
- other data analysts,
- data scientists, and
- data engineers.
• You might look into the data
- on employee productivity,
while another analyst looks at
- hiring data.
• A big part of your job will be
- collaborating with other data team members
- to find new angles of the data to explore.
Data science team

• Working with stakeholders
- you'll often have to go beyond the data.
- communicate clearly,
- establish trust, and
- deliver your findings across groups.
Discuss goals.
- Ask about the kind of results the stakeholder wants.
Feel empowered to say “no.”
- Maybe you realize their hypothesis isn’t fully formed and
- you have helpful ideas about a better way to approach the
analysis. Or
- maybe you realize it will take more time and effort to perform the
analysis than estimated.

• Plan for the unexpected.
- Before you start a project,
- make a list of potential roadblocks.
- When you discuss
-project expectations and
- timelines with your stakeholders,
give yourself some extra time for problem-solving at each stage of
the process.
• Know your project.
- Keep track of your discussions about the project
-over email or
- reports, and
- be ready to answer questions about how certain aspects are
- Get to know how your project connects to the rest of the company
and

- Your stakeholders will want regular updates on your
projects.
- Share notes about
-project milestones,
-and changes.
- Then use your notes to create a shareable report.
-Another great resource to use is a change log,
- A change log is a file containing a
- ordered list of modifications made to a
Communicate often

• The importance of staying focused on the objective
• This can be tricky when you find
- yourself working with a lot of people
- with competing needs and
- opinions.
Ask yourself a few simple questions at the beginning of each task,
Employee Turnover example
• There, we were dealing with a lot of
- different team members and stakeholders like
- managers,
- administrators, even
- other analysts.
• As a data analyst, you'll find that balancing everyone's needs
Focus on what matters

• There are three things you can focus on
1) who are the primary and secondary stakeholders?
2) who is managing the data?
3) where can you go for help?
• Let's see if we can apply those questions to our example project
• The first question you can ask is
1) Who are those stakeholders?
- The primary stakeholder of this project is probably the Vice
President of HR - who is using the project's findings to
- make new decisions about company policy.
- The secondary stakeholders are
- project manager,
- team members,
- or other data analysts
- who are depending on your work for their own task.

2) Who is managing the data
• In our example,
- there was a data analyst who was focused on managing
the company's hiring data.
- Their insights around a surge of new hires 18 months ago
- turned out to be a key part of your analysis.
• If you hadn't communicated with this person,
- you would have spent time trying to collect or analyze
hiring data yourself
- or you might not have included this in your project

3) Where do you go for help
Project managers support you and your work by
- managing the project timeline,
- providing guidance and resources,
- and setting up efficient workflows.
• They have a big picture view of the project because they know what you and the
rest of the team are doing.
• This makes them a great resource if you run into a problem
• In the employee turnover example,
• you would need to be able to access employee survey data to include in your
analysis.
• If you're having trouble getting approvals for that access,
• you can speak with your project manager to remove those barriers for you
- so that you can move forward with your project.

• The importance of clear communication with your stakeholders and team
members.
• Start thinking about who you want to communicate with and when.
• They will know it and appreciate the time you took to consider them and
their needs.
• Let's say you're working on a big project,
- analyzing annual sales data, and you discover that
-all of the online sales data is missing.
• This could affect your whole team and
- significantly delay the project.
• By thinking through these four questions,
-you can map out the best way to
- communicate across your team about this problem.

1) who your audience is,
2) what they already know,
3) what they need to know and
4) How you can communicate that effectively to them.
• First, you'll need to think about who your audience is.
- Other data analysts working on the project,
- your project manager
- VP of sales,
• who is your stakeholder.
• Second What they already know
• The other data analysts working on this project know
- all the details about data-set,
• and your project manager knows
- the timeline you're working towards.
• Finally, the VP of sales knows
- the high-level goals of the project.

• Third what they need to know to move forward.
• Your fellow data analysts need
- to know the details of where you have tried so far and
- any potential solutions you've come up with.
• Your project manager would need to know
- the different teams that could be affected and
- the implications for the project,
- if this problem changes the timeline.
• Finally, the VP of sales will need to know that
- there is a potential issue that would delay or
- affect the project.

• You can choose the best way to communicate with them
• Instead of a long, worried e-mail which could lead to lots back and forth,
- you decide to quickly book a meeting with your project manager and fellow analysts.
• You let the team know about
- the missing online sales data and
- give them more background info.
- discuss how this impacts other parts of the project.
• As a team, you
- come up with a plan and
- update the project timeline if needed.
• To, the VP of sales
- write an e-mail
-update if there were changes to the timeline
When you communicate thoughtfully and think about your audience first,
• you'll build better relationships and trust with your team members and stakeholders.

• Data is being generated all around
- the world and we're talking tons of data.
• Every minute of every day
- millions of texts and
- hundreds of millions of emails are sent.
- Millions of online searches are made and videos viewed
- those numbers are only growing.
• Let us see
- the ways that data can be generated and
- how industries collect data themselves.
- Every piece of information is data.
• All that data is usually generated as a result of our activity in the world.
• With social media and mobile devices,
- millions and millions of people add huge amounts of data on the internet.
Collecting Data

•
• Every digital photo online is one piece of data.
- Every photo itself holds even more data,
- the number of pixels to
- the colors contained in each of those pixels.
• The data generation and collection comes with a few more things to think about.
- we need to consider ethics so that we
- maintain people's rights and privacy.
• Example,
• The United States Census Bureau uses forms to
- collect data about the country's population.
• This data is used for a number of reasons, like
- funding for schools,
- hospitals, and
- fire departments.
Collecting Data

• The Bureau also collects information about things like
- U.S. businesses,
- creating their own data in the process.
- others can then use the data for their own needs,
- including analysis
• The analytics for the health care industry.
- We run a lot of surveys to learn how patients feel about certain
things related to their health care.
• For example,
- how patients feel
• The data collected helped the companies we work
- improve the care of their patients.
Collecting Data

• There are lots of different ways to collect data.
• Example, a job interview.
- You share information about yourself.
- The hiring manager collects that data and analyzes it to
- help them decide whether to hire you or not.
• But it goes both ways.
- You could also collect your own data about the company to
- help you decide if the company is a good fit for you.
• Scientists also generate data.
- They use a lot of observations in their work.
- they might collect data by studying animal behavior or
- looking at bacteria under a microscope.
Collecting Data

• How do some websites remember your preferences?
- This is done using cookies,
-which are small files stored on computers that
- contain information about users.
• Cookies help advertisers know about
- your personal interests and habits
- based on your online surfing,
- without personally identifying you.
• As a real-world analyst,
-you'll have all kinds of data right at your fingertips and
- lots of it too.
Collecting Data

- you'll need to decide what kind of data to collect and
- use for every project.
• With a nearly endless amount of data out there, you need
to know
- which factors to consider when collecting data.
- will narrow down your choices.
• Let's start with a question like,
- what's causing increased rush hour traffic in your
city?
Collecting Data

• First, you need to know how the data will be collected.
- You might use observations of traffic patterns to
- count the number of cars on city streets
- during particular times.
• You notice that cars are getting backed up on a specific street.
• That brings us to data sources. In our traffic example,
- your observations would be first-party data.
-This is data collected by an individual or group using their
own resources.
Collecting Data

• You might also have second-party data, which is
- data collected by a group directly from its audience and
- then sold.
- you might buy it from an organization that’s
- led traffic pattern studies in your city.
• There can be third-party data or
- data collected from outside sources
- who did not collect it directly.
• No matter where the data came from
- you need to check it for
-accuracy,
-bias, and
- trustworthiness.
Collecting Data

• Just remember that the data you choose should
- apply to your needs, and it
- must be approved for use.
- your job to decide what data to use,
- choosing the data that can
- help you find answers and
- solve problems and
- not getting distracted by other data.
• In our traffic example,
- financial data probably wouldn't be that helpful,
- data about high volume traffic times would be
Collecting Data

• Now let's talk about how much data to collect.
• In data analytics,
- a population refers to all possible data values in a certain data set.
• If you're analyzing data about car traffic in a city,
- your population would be all the cars in that area.
• But collecting data from the entire population can be pretty challenging.
• That's why a sample can be useful.
• A sample is a part of a population
- that is representative of the population.
• You might collect a data sample about
- one spot in the city and analyze the traffic
- or you might pull a random sample from all existing data in the population.
Collecting Data

• As you collect data, you'll also want to make sure
- you select the right data type.
- dates of traffic records stored in a date format.
• The dates could help you figure
- what days of the week there is likely to be a high volume of traffic in the future.
• Finally, you need to determine
- the time frame for data collection.
• In our example,
- if you needed an answer immediately,
- you have to use historical data,
• But let's say you needed to track
- traffic patterns over a long period of time.
- That might affect the other decisions
- you make during data collection.
Collecting Data

• Quantitative and Qualitative data.
• Qualitative data is usually listed as a
- name,
- category, or
- description.
Eg movie titles are qualitative data.
• Quantitative data, which
- can be measured or
- counted and then
- expressed as a number.
• This is data with a certain
- quantity,
- amount, or
- range.
• Eg, movie budget and box office revenue.
Types of Data Formats

• Quantitative data can be broken down into
- discrete or
- continuous data.
• Let's check out discrete data first. This is data that’s
- counted and has
- a limited number of values.
eg, A movie's budget and box office returns
- These are both examples of discrete data that can be
- counted and have a limited number of values.
• For example, the amount of money a movie makes can only be
represented
- with exactly two digits after the decimal to represent.

• Continuous data can be
-measured using a timer, and
- its value can be shown as a decimal with several
places.
• Eg, the movie’s runtime
- 110.0356 minutes.
• There's also
- nominal and
- ordinal data.

• Nominal data is a type of
- qualitative data that's categorized without a set
order.
- this data doesn't have a sequence.
• Eg, You ask people if they've watched a given movie.
Their responses would be in the form of nominal data.
They could respond "Yes," "No," or "Not sure."
• These choices don't have a particular order.

• Ordinal data, on the other hand, is a type of
- qualitative data with a set order or scale.
• If you asked a group of people
- to rank a movie from 1 to 5,
-some might rank it as a 2,
- others a 4, and so on.
• These rankings are in order of
- how much each person liked the movie.
• Now let's talk about internal data,
- which is data that lives within a company's own systems.
• For example,
- if a movie studio usually more reliable and easier to collect,

• Structured data is data
- that's organized in a certain format,
- such as rows and columns.
• Spreadsheets and relational databases are
- two examples
- that can store data in a structured way.
• Structured data is
- Having a framework for the data makes the
- data easily searchable and
- more analysis-ready.
• Examples of unstructured data are
-Audio and
- video files are
-because there's no clear way to identify or organize their content. ,
- but the data doesn't fit in rows and columns like structured data.

• Data models help
- keep data consistent and
- enable people to map out how data is organized.
- A basic understanding makes it
- easier for analysts and other stakeholders
- to make sense of their data and use it in the right
ways.
Data modeling
- is the process of creating diagrams that visually
- represent how data is organized and structured.
-These visual representations are called data models.
Data Modeling

• Data-modeling techniques
• There are a lot of approaches when it comes to developing data
models,
• Two common methods are the
1) Entity Relationship Diagram (ERD) and the
2) Unified Modeling Language (UML) diagram.
ERDs are a visual way to understand the
- relationship between entities in the data model.
UML diagrams are very detailed diagrams that describe the structure of a
by showing the
- system's entities,
- attributes,
- operations, and
-their relationships
• Data modeling helps data analysis to understand how the data is
together
Data Modeling

• A data type is a specific kind of data attribute
- That tells what kind of value the data is.
• Data types can be different
- depending on the query language you're using.
• For example, SQL allows for
-different data types
-depending on which database you're using.
• Data type in a spreadsheet can be one of three things:
- a number,
- a text or string, or
- a Boolean.
Data Types

Boolean Logic
Color is Grey Color is Pink If Grey AND Pink,
then Buy
Boolean Logic
Grey/True Pink/True True/Buy True AND True =
Grey/True Black/False False/Don't buy
True AND False =
False
Red/False Pink/True False/Don't buy
False AND True =
False
False AND False =

Color is Grey Color is Pink
If Grey OR Pink,
then Buy
Boolean Logic
Red/False Black/False False/Don't buy False OR False =
Black/False Pink/True True/Buy False OR True = True
Grey/True Green/False True/Buy True OR False = True
Grey/True Pink/True True/Buy True OR True = True
Color is Grey Color is Pink Boolean Logic for
NOT Pink
If Grey AND (NOT
Pink), then Buy
Boolean Logic
Grey/True Red/False Not False = True True/Buy
True AND True =
True
Grey/True Black/False Not False = True True/Buy
True AND True =
True
Grey/True Green/False Not False = True True/Buy
True AND True =
True
Grey/True Pink/True Not True = False False/Don't buy
True AND False =
False

• A data table, or tabular data, has a very simple structure.
- It's arranged in rows and columns.
- You can call the rows "records" and the columns
"fields."
- a field can also refer to a single piece of data,
- like the value in a cell.
• Eg, song characteristic,
- like the title and the artist, is a field.
- Each separate field has a data type,
- the song titles are a text or string type, while
- the song's length could be a number type if you're using it for
calculations.
- The column for favorites is Boolean
- since it has two possible values: favorite or not favorite.
Data Table Components

• Wide and long data
• Wide data lets you easily identify and quickly compare
different columns
• The data is arranged alphabetically by country,
- so you can compare the annual populations of India and
Bangladesh, Japan
- by just checking out the values in each column.
• The wide data format also makes it easy to find and
- compare the countries' populations at different
periods of time.
Country 2013 2014 2015 2016 2017
India 84000 83000 86000 88006 98700
Bangladesh 45000 45980 46000 46798 48000
Japan 76000 77000 78600 79800 80000

• Long Data
• Here the data is no longer organized into columns by year.
• All the years are now in one column with each country,
- like India, appearing in multiple rows,
- one for each year of data.
Country Name Year POpulation
India 2013 84000
India 2014 83000
India 2015 86000
India 2016 88006
India 2017 98700
Bangladesh 2013 45000

• Data transformation usually involves:
- Adding, copying, or replicating data
- Deleting fields or records
- Standardizing the names of variables
- Renaming, moving, or combining columns in a
database
- Joining one set of data with another
- Saving a file in a different format. For example,
- saving a spreadsheet as a comma separated values
(CSV) file.
Data Transformation

• Goals for data transformation might be:
• Data organization: better organized data is easier to use
• Data compatibility: different applications or systems can
then use the same data
• Data migration: data with matching formats can be moved
from one system to another
• Data merging: data with the same organization can be
merged together
• Data enhancement: data can be displayed with more
detailed fields
• Data comparison: comparisons of the data can then be
made
Data Transformation

• Your data story will be filled with
- characters,
- questions,
- challenges,
- conflict, and hopefully
- a resolution.
• The trick is to
- avoid the conflict,
- overcome the challenges and
- answer the questions.
First
- learn how to analyze data for bias and credibility.
- because even the most sound data can be skewed or misinterpreted
Second
- the importance of being good and bad data
Unbiased and Objective Data

• World of data ethics, privacy and access.
- As more and more data becomes available, and
- the algorithms we create to use this data
- become more complex and sophisticated,
- new issues keep popping up.
• We need to ask questions like,
- who owns all this data?
- How much control do we have over the privacy of data?
- Can we use and reuse data however we want to?
• As a data analyst, it's important to understand
- data ethics and
- privacy because
• in your work, you will have to find out
- correct use and
- application of data
Ethics, Privacy and Access

• Data bias is a type of error that systematically
- skews results in a certain direction.
- Maybe the questions on a survey had
- a particular slant to influence answers, or maybe
- the sample group wasn't truly representative of the population
•
• Example,
- you're going to take the median age of the US patient population with
health
insurance,
• you wouldn't just use
- a sample of Medicare patients who are 65 and older.
• Bias can also happen if a sample group lacks inclusivity.
- people with disabilities tend to be
- under-identified,
- under-represented,
- excluded in mainstream health research.
Biased Data

• The way you collect data can also bias a data set.
For example, if you give people
- only a short time to answer questions,
- their responses will be rushed.
- When we're rushed, we make more mistakes,
- which can affect the quality of our data and create biased outcomes.
• As a data analyst, you have to think about
- bias and
- fairness
- from the moment you start collecting data
- to the time you present your conclusions.
• Another example of heart health
-heart health tend to include a lot more men than women.
- women failing to recognize symptoms and ultimately having
- their heart conditions go undetected and untreated.
• That's just one way bias can have a very real impact.
Biased Data

• The more high quality data we have,
- the more confidence we can have in our decisions.
• A Process I like to call ROCCC, R-O-C-C-C. can be used
R – Reliability
O -- Original.
C – Complete
C -- Current.
C -- Cited
Reliable
• With this data you can trust that you're getting
- accurate,
- complete and
- unbiased information
• that's been vetted and proven fit for use.
Data Credibility – Good Data

• Original
• you'll discover data through a second or third party source.
- To make sure you're dealing with good data, be sure to
- validate it with the original source.
Complete
The best data sources contain
- all critical information needed to
- answer the question or
- find the solution
- cover every aspect first before deciding

Current
• The usefulness of data decreases as time passes.
- If you wanted to invite
- all current clients to a business event,
- you wouldn't use a 10-year-old client list.
- The best data sources are current
- and relevant to the task at hand.

• Cited
• Citing makes the information you're providing
- more credible.
• When you're choosing a data source,
• think about three things.
- Who created the data set?
- Is it part of a credible organization?
- When was the data last refreshed?
• If you have
- original data from
- a reliable organization and
- it's comprehensive,
- current, and
- cited,
That will have good data

• Bad data sources that don’t follow ROCCC.
• R is for not reliable.
- Bad data can't be trusted because it’s
-inaccurate,
-incomplete, or
- biased.
- data that has sample selection bias
- because it doesn't reflect the overall population.
- data visualizations and graphs that are just misleading
• O is for not original.
- you can't locate the original data source and you're just
- relying on second or third party information,
- you may need to be extra careful in
- understanding your data.
Bad Data

• C is for not comprehensive.
- Bad data sources are missing important information
- needed to answer the question or to
- find the solution.
- they may contain human error, too.
• C is for not current.
- Bad data sources are out of date and irrelevant.
- Many respected sources refresh their data regularly,
- to give you the most current info available.
• C is for not cited.
- If your source hasn't been cited
Bad Data

• When we analyze data, we're also faced with
- questions,
- challenges, and
- opportunities,
- but we have to rely on ethics
• Ethics refers to
- well-founded standards of right and wrong that
- benefits to society,
- fairness or
- specific.
• Data ethics refers to
- well- founded standards of right and wrong that
- dictate how data is
- collected,
- shared, and
- used.
Data Ethics

•
• The ability to
-collect,
- share and
- use data in such large quantities is relatively new,
• The importance of data privacy has been recognized by governments worldwide
and
- They help protect people and their data.
• Data ethics tries to get to the root of
- the accountability companies have in
- protecting and
- responsibly using the data they collect.
• The different aspects of data ethics are:
1) ownership,
2) transaction transparency,
3)consent,
4)currency,
5)privacy, and
6)openness.
Data Ethics

• 1) ownership.
- This answers the question
- who owns data?
• It is not the organization that
- invested time and money collecting,
- storing, processing, and analyzing it.
• It is individuals who
- own the raw data they provide, and
- they have primary control over its usage,
- how it's processed and
- how it's shared.
Data Ethics

2) transaction transparency,
- all data processing activities and algorithms should be completely
- explainable and
- understood by the individual who provides their data.
• This is in response to concerns over data bias,
- which systematically skews results in a certain direction.
- Biased outcomes can lead to negative consequences.
- To avoid them, it's helpful to
- provide transparent analysis especially to the
- people who share their data.
- then people can judge whether the
- outcome is fair and
- unbiased and allows them to
- raise potential concerns.
Data Ethics

3)consent.
- This is an individual’s
- right to know
- explicit details about
- how and why their data will be
- used before agreeing to provide it.
• They should know answers to questions
- why is the data being collected?
- How will it be used?
- How long will it be stored?
• The best way to give consent is
- a conversation between
- the person providing the data and the
- person requesting it.
• But with so much activity happening on internet
- consent usually just looks like a terms and conditions
Data Ethics

4)currency.
- Individuals should be aware of
- financial transactions resulting from the
- use of their personal data and the
- scale of these transactions.
• If your data is helping to fund a company's efforts, you should know
- what those efforts are all about and be
- given the opportunity to opt out.
• The last two aspects of data ethics,
• privacy and openness, deserve
• their
Data Ethics

• It's all about
- access,
- use, and
- collection of data.
• It also covers
- a person's legal right to their data.
- protection from unauthorized access to our private data,
- freedom from inappropriate use of our data,
-the right to
- inspect,
- update, or
- correct our data,
- ability to give consent to use our data, and
- legal right to access our data.
• Being able to trust companies with your data is important.
• It's what makes people want to
- use a company's product,
- share their information,
Data Privacy

• Personally identifiable information, or PII, is information that
can - be used by itself or with other data
- to track down a person's identity.
Data anonymization
- is the process of protecting people’s
- private or
- sensitive data by
- eliminating that kind of information.
Typically, data anonymization involves
- blanking,
- hashing, or
- masking personal information, often by
- using fixed-length codes to represent data columns, or
- hiding data with altered values.
Data Anonymization

• Healthcare and
• Financial data
- are two of the most sensitive types of data.
These industries rely a lot on data anonymization
techniques.
After all, the stakes are very high.
Data in these two industries usually goes through
de-identification, which is
- a process used to wipe data clean of
- all personally identifying information.
Data Anonymization

• Here is a list of data that is often anonymized:
- Telephone numbers
-Names
-License plates and license numbers
-Social security numbers(Aadhar number in India)
-IP addresses
-Medical records
-Email addresses
-Photographs
-Account numbers
Data Anonymization

UNIT 4 PREPARE DATA FOR STAKEHOLDERS (1).pptx

UNIT 4 PREPARE DATA FOR STAKEHOLDERS (1).pptx

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