Discover the role of data analytics in transformation projects and business process management. What are the four key areas of data analytics and what are the types of data analytics. Also explains data visuals.
2. 4 Key Areas of Data Analytics
Area Description
Data Process to Data Assets Understanding the life cycle of data and recognizing
or creating data assets from that data or the analysis
of that data
Statistical Analysis The analytics and mathematical operations used for
analysis on sets of data
Data Management and Data Governance The processes for managing and protecting data and
data assets
Data Visualization How to tell the story of your data and data analysis in
order to gain insights for your organization
4. Data Analytics?
From Wikipedia
• Data analysis is a process of inspecting,
cleansing, transforming, and modeling
data with the goal of discovering useful
information, informing conclusions,
and supporting decision-making…. In
today's business, data analysis is
playing a role in making decisions more
scientific and helping the business
achieve effective operation.
5. Data Analytics in
Digital Business
• Combined with Social, Mobile, and Cloud
technologies, analytics is creating a
competitive advantage in digital business
(SMAC)
• Artificial intelligence applications
• Voice recognition
• Ecommerce
• News feeds
• Advertising customization
• Banking applications
• Big data applications
6. Types of Data Analytics
Descriptive: describing what happened
Diagnostic: explaining why something
happened
Predictive: anticipating the future
Prescriptive: attempting to change the future
8. Goals for Data
Management
• Quality
• Accessibility
• Availability
• Reliability
• Performance
• Maintainability
• Timeliness
• Gain insights into business
• Better business decisions
9. Components of Data Management
Security Quality Storage Usage
Software
Engineering
10. Goals for Data
Governance
• Increase compliance
• Improve security
• Maximize use of data as an asset
• Increase consistency between
data assets
• Create structure for use of data
• Create accountability for data
quality
11. Components of Data Governance
Ethics
Compliance
Management
Disaster
Recovery
Security
Asset
Management
12. What is a Visual?
• Any graph, chart, report, table, or text used to allow
analysis on data or view results of analysis
• Uses
• Initial analysis
• Display the results of analysis
• Communicate findings
• Provide insights
• Benefits
• Communicate findings easier
• Create discussions
• Inform all levels of the organization
• Easier to understand
• Realizes patterns
• Allows you to tell the data story
13. Data Analytics Certificate Program: Four Courses
Area Class Description
Data Process to Data Assets Data Analytics 101
Using Data to Provide Insights for
Your Organization
Understanding the life cycle of data and
recognizing or creating data assets from
that data or the analysis of that data
Statistical Analysis Statistics for Data Analytics
Using Statistics to Gain Business
Insights
The analytics and mathematical
operations used for analysis on sets of
data
Data Management and Data
Governance
Data Management and Data
Governance
Managing and Protecting Data
Assets for Your Organization
The processes for managing and
protecting data and data assets
Data Visualization Visualizations for Data Analytics
Choosing the Best Visuals to Create
Data Insights
How to tell the story of your data and
data analysis in order to gain insights
for your organization
Discuss with class: What data fields are collected for each of these types?
Write on flipchart some examples of each.
Many of the statistical models are too complex for this class; we will start out with very basic ones that will allow you to get a foundation in the subject
If time permits, spend some time on these steps; if not, discuss only modeling steps
Define objective: What do you want to predict?
Collect data: Find the right data for your insights needed
Prepare data: What changes need to occur to the data
Determine the model: Decide on the model and apply it
Deploy of model: Put the model into production
Monitor the model: Verifying any changes over time