This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
Session about types of analytics. Descriptive, diagnostic, predictive and prescriptive analytics.
Conference DATA ANALYSIS DEVELOPMENT 2016 by RZECZPOSPOLITA.
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
This presentation introduces big data and explains how to generate actionable insights using analytics techniques. The deck explains general steps involved in a typical analytics project and provides a brief overview of the most commonly used predictive analytics methods and their business applications.
Vijay Adamapure is a Data Science Enthusiast with extensive experience in the field of data mining, predictive modeling and machine learning. He has worked on numerous analytics projects ranging from healthcare, business analytics, renewable energy to IoT.
Vijay presented these slides during the Internet of Everything Meetup event 'Predictive Analytics - An Overview' that took place on Jan. 9, 2015 in Mumbai. To join the Meetup group, register here: http://bit.ly/1A7T0A1
Slides used for a presentation to introduce the field of business analytics. Covers what BA is, how it is a part of business intelligence, and what areas make up BA.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Discussed what is Prescriptive Analytics, comparison between Descriptive and Prescriptive Analytics, process, methods and tools. A report presentation conducted at University of East - Manila, Philippines dated July 6, 2017.
Session about types of analytics. Descriptive, diagnostic, predictive and prescriptive analytics.
Conference DATA ANALYSIS DEVELOPMENT 2016 by RZECZPOSPOLITA.
DI&A Slides: Descriptive, Prescriptive, and Predictive AnalyticsDATAVERSITY
Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions that may be taken based on the analysis. Predictive analytics focuses on the application of statistical models to help forecast the behavior of people and markets.
This webinar will compare and contrast these different data analysis activities and cover:
- Statistical Analysis – forming a hypothesis, identifying appropriate sources and proving / disproving the hypothesis
- Descriptive Data Analytics – finding patterns
- Predictive Analytics – creating models of behavior
- Prescriptive Analytics – acting on insight
- How the analytic environment differs for each
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
What Is Prescriptive Analytics? Your 5-Minute OverviewShannon Kearns
This slide deck walks you through the basis of understanding prescriptive analytics. Understand the different kinds of prescriptive analytics, how it works, its value, where to find use cases and more!
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
What is business intelligence and where it is applicable is described in this presentation. The subject is offered as elective to BE IT students of Pune University.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
What Is Prescriptive Analytics? Your 5-Minute OverviewShannon Kearns
This slide deck walks you through the basis of understanding prescriptive analytics. Understand the different kinds of prescriptive analytics, how it works, its value, where to find use cases and more!
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
What is business intelligence and where it is applicable is described in this presentation. The subject is offered as elective to BE IT students of Pune University.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
The Softer Skills Analysts need to make an impactPaul Laughlin
25 min presentation given at London Business School, to the OR Society's Analytics Network. Summarising Laughlin Consultancy's 9 step model of Softer Skills for Analysts.
Driving Business Performance with Microsoft Performance ManagementNic Smith
Learn how Microsoft technology supports your initative for performance management. PerformancePoint Server completes an end-to-end vision for Microsoft BI and enables organizations to monitor, analyze, and plan to drive results.
Business intelligence and data analytic for value realization iyke ezeugo
This presentation centres on how Businesses can take advantage of this era of information overload for enhancing their Business Intelligence and Data Analytic exploits to assure greater values with the available technology solutions.
It is focused on demystifying the BIG DATA phenomenon of the information age, and also on motivating traditional business drivers to begin to take advantage of business decision support systems (DSS) for their business intelligence and data analytics needs. The objective is to help organizations discover what and what they can do with these ICT solutions in their business for greater value realization. These values are expressed in building agile business that are able to thrive, make profit, grow and remain sustainable in the midst of stiff competition, globalization, innovation and regulatory pressures, even with elastic customers’ demands.
The future growth of a career as a business analyst its role and responsibili...Learningrow
Business Analyst plays a vital role in all organization. Business Analyst is a person who understanding business change needs by capturing, analyzing, researching and documenting requirements and supporting the communication of a specific business to improve the productivity of a business and provide the solutions to the client and to different companies.
Difference between Business Intelligence and Business Analytics_Mujeeb Riaz.pdfMujeeb Riaz
Business Intelligence is used to analyze historical data to predict future trends. It's typically used for things like sales forecasts and product recommendations. On the other hand, business analytics uses predictive modeling to predict how different variables affect each other.
The Business Analyst: The Pivotal Role Of The FutureTom Humbarger
This presentation was originally made at the Silicon Valley IIBA Chapter meeting in June 2008 by Kathleen (Kitty) Hass from Management Concepts (www.managementconcepts.com). Kitty is also a new board member at-large for the IIBA.
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The role of a business analyst is an important part of any project team. Acting as the key interface between the users and the project manager they gather information, document processes, and confirm the final documents with users.
Business Analyst Responsibilities:
Evaluating business processes, anticipating requirements, uncovering areas for improvement, and developing and implementing solutions.
Leading ongoing reviews of business processes and developing optimization strategies.
Staying up-to-date on the latest process and IT advancements to automate and modernize systems.
Conducting meetings and presentations to share ideas and findings.
Performing requirements analysis.
Documenting and communicating the results of your efforts.
Effectively communicating your insights and plans to cross-functional team members and management.
Gathering critical information from meetings with various stakeholders and producing useful reports.
Working closely with clients, technicians, and managerial staff.
Providing leadership, training, coaching, and guidance to junior staff.
Allocating resources and maintaining cost efficiency.
Ensuring solutions meet business needs and requirements.
Performing user acceptance testing.
Managing projects, developing project plans, and monitoring performance.
Updating, implementing, and maintaining procedures.
Prioritizing initiatives based on business needs and requirements.
Serving as a liaison between stakeholders and users.
Managing competing resources and priorities.
Monitoring deliverables and ensuring timely completion of projects.
Business Analyst Requirements:
A bachelor’s degree in business or related field or an MBA.
A minimum of 5 years of experience in business analysis or a related field.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
2. Table of Contents …
Definition of Analytics and Predictive Analytics
How Analytics and Predictive Analytics Compare
Defining Business Intelligence “BI” and its Relationship to Predictive Analytics
Business Intelligence’s Evolution & its Organizational Impact
The Importance of Communication Skills & Predictive Analytics
The Business Case for Predictive Analytics
Conclusion and Key Takeaways
4. What is Analytics?
Using analytics is like driving your car but watching traffic through the rear-
view mirror, not seeing what’s ahead and thereby in danger of crashing
“… the application of computer technology,
operations research and statistics to solve
problems in business and industry. Analytics is
carried out within an information system.”
“… the application of computer technology,
operations research and statistics to solve
problems in business and industry. Analytics is
carried out within an information system.”
Tom Davenport
noted author
5. What is Predictive Analytics?
Using predictive analytics is like driving your car and watching traffic through
the front windshield, anticipating traffic, making course corrections to avoid
traffic jams and getting there faster and safer
“predictive models exploit patterns found in historical
and transactional data to identify risks and
opportunities. Models capture relationships among
many factors to allow assessment of risk or potential
associated with a particular set of conditions, guiding
decision making for candidate transactions.”
“Any solution that supports the identification of
meaningful patterns and correlations among
variables in complex, structured and unstructured,
historical, and potential future data sets for the
purposes of predicting future events and assessing
the attractiveness of various courses of action.”
7. How Analytics and Predictive Analytics Compare
Predictive Analytics are more sophisticated analytics that
“forward thinking” in nature
Analytics is the understanding of existing (retrospective)
data with the goal of understanding trends via comparison
Developing analytics is the first step towards deriving
predictive analytics
They used for gaining insights from mathematical and/or
financial modeling by enhancing understanding, interpretation
and judgment for the purpose of good decision making
8. How Analytics and Predictive Analytics Compare
Attribute Analytics Predictive Analytics
Purpose:
Understand the Past
Observe Trends
Catalyst for Discussion
Gain Insights
Make Decisions
Take Action
View: Historical and Current Future Oriented
Metrics Type: Lagging Indicators Leading Indicators
Data Used: Raw & Compiled Information
Data Type: Structured Structured and Unstructured
Users: Middle & Senior Mgt
Analysts, End Users
C-Level & Senior Mgt
Strategists, Analysts, Mgrs
Benefits: Gaining an understanding of data
Productivity Improvements
Gaining Information & Insights
Process Improvements
9. Benefits of Analytics and Predictive Analytics
Benefits of analytics: productivity gains through improved
data-gathering processes results in less time required for
producing reports and metrics
Takeaway: Both types of gains are beneficial but
improvements in analytics are NOT as scalable as to
the benefits in predictive analytics which are
repeatable, virtuous and scalable
Benefits of predictive analytics: process improvement gains
through improve revenue generation & cost structures leading
to enhanced decision making
11. Defining Business Intelligence & its Relationship to Predictive Analytics
Unfortunately, the human & business strategy elements are
often overlooked and forgotten but are key ingredients to the
success of BI
“… computer-based techniques used in
identifying, extracting and analyzing business
data … aims to support better business decision-
making … BI technologies provide historical,
current and predictive views of business
operations.”
BI is typically thought of in terms of technology inclusive of data management
practices, data warehouses, ETL processes, etc.
Predictive Analytics are a sub-set of Analytics and a branch of BI which is
the least understood and underestimated
12. Defining Business Intelligence & its Relationship to Predictive Analytics
Analytics serves as the “glue” in aligning the key elements of business
Analytics provide the feedback to business people signaling success or
failure of their strategy and business model
Business Intelligence = Business + Intelligence
Business = The Strategy + Business Model + Infrastructure + Technology
+ + +
13. Defining Business Intelligence & its Relationship to Predictive Analytics
People create information for the organization in order to gain understanding of its
customers, competitors and ecosystem
Business Intelligence is a process of generating insights and or knowledge
(predictive analytics) through people and technologies in order to execute their
strategy
This process needs to be leveraged into a core competency, a unique and virtuous
process to differentiate the business in a world of “me-too” organizations & strategies
Intelligence = People + Processes + Analytics
+ +=
15. BI’s Evolution and its Organizational Impact
The most important part of BI is the
human element and achieving
people’s business and personal goals
Most businesses organize their BI activities and professionals under the IT function
under the Enterprise 1.0 model
With advances in technology and social media, the Enterprise 1.0 model, is not the
most efficient, scalable, and collaborative way to execute your business strategy
especially from a human resourcing perspective
With globalization, advances in internet technologies and social media, we have
advanced to the era of Enterprise 2.5
As a result of Enterprise 2.5, changes in business require evolution in BI
16. BI’s Evolution & its Organizational (Design) Impacts
In the era of Enterprise 2.5, BI is readily
becoming a distinctive capability & asset
for organizations
If BI is deemed strategic, this function
should be realigned to fall under the
direction of the CEO or Office of Strategy
Management (OSM)
Implementing a new organizational
structure will encounter language and
communication challenges between
business and BI professionals
CEO
CIO
Business Intelligence Group
CEO
COO
CIO
Office of Strategy Management &
Business Intelligence Group
Old Model – “Enterprise 1.0”
New Model – “Enterprise 2.5”
18. The Importance of Communication Skills & Predictive Analytics
The purpose of predictive analytics is to help organizations see relationships
between business elements so senior management may craft targeted business
strategies and exploit opportunities on a timely basis with a focus on the future
In order to benefit from predictive analytics, people across the organization must
communicate and understand with one another but language often becomes a barrier
BI professionals often think language is SQL (Structured Query Language) and
business people often think language is reports, metrics and meetings
IT & BI professionals need to understand the language of strategy, business
models and performance while solving business not technology problems
SQL vs
19. The Importance of Communication Skills & Predictive Analytics
Need market
segmentation report,
now!
OK, what are the
parameters and
how do you want
it rendered?
CEO/Business People BI People
Conversations @ Work
20. The Importance of Communication Skills & Predictive Analytics
Huh? What is he
asking me?
Just need my report!
CEO/Business People
Huh? What is he
asking me?
Market
Segmentation?
BI People
Conversations @ Work
21. The Importance of Communication Skills & Predictive Analytics
Takeaway: Business professionals need to appreciate the role of technology as an
enabler and they need to lead and determine where & how IT/BI infrastructure
should be deployed to improve decision making
Takeaway: It is not enough to have state of the art in
BI technologies, without having a common
understanding and a common language between the
business people and BI professionals, otherwise BI
efforts will fall short of desired results
Takeaway: IT & BI professionals need to understand the language of
strategy, business models and performance while solving business NOT
technology problems
23. The Business Case for Predictive Analytics – Macro level
On a macro level, organizations need predictive analytics for:
Strategic Planning
Financial Planning
Focusing on Priorities
Competitive Analyses
Achieving Profit and Revenue Targets
Developing Competitive Advantages and Differentiation
Predictive analytics can provide timely feedback to executives on their strategic
initiatives – without feedback course corrections may be too late
Predictive analytics provide leading indicators and insight to assist in planning for
answering the big question: What should we do next? – next quarter, next year etc.
24. The Business Case for Predictive Analytics – Micro level
On a micro level, organizations need predictive analytics for:
Improving business processes
Doing more with less budget (working smarter not harder!)
Allocating resources appropriately
Understanding correlations and sensitivities with customer segments
To ensure long term financial resources are available to run the business
Developing Competitive Advantages and Differentiation
Q: Why do most organizations struggle with Analytics and especially Predictive
Analytics?
A: Organizations fail to recognize and misunderstand the necessary and intangible
elements of people, skills, and corporate culture and tying these elements back to
their analytics, business model and strategies – Caution: this is a long-term fix
26. Conclusion & Key Takeaways
Takeaway: Predictive Analytics is the analytical ability to
see relationships between business drivers and performance
and the ability to model these relationships performed by
people to improve organizational visibility
Conclusion: Business Intelligence begins with your
organization’s strategy and business model and only then
should performance metrics and analytics be appropriately
conceived and deployed
Takeaway: It is not enough to have state of the art in
BI technologies, without having a common
understanding and a common language between the
business people and BI professionals, otherwise BI
efforts will fall short of desired results
27. Conclusion & Key Takeaways
Takeaway: IT & BI professionals need to understand the
language of strategy, business models and performance
while solving business not technology problems
Takeaway: IT & BI professionals need to understand the
language of strategy, business models and performance while
solving business not technology problems
Takeaway: Business professionals need to appreciate
the role of technology as an enabler and they need to
lead and determine where & how IT/BI infrastructure
should be deployed to improve decision making
28. Sources, References, and Trade Marks
www.wikipedia.org
Competing on Analytics, 2007, Thomas H. Davenport
www.forrester.com
The Lego Minifigure is a trade mark of The Lego Group
Clipart provided by OCAL and www.clker.com
29. Introduction to Predictive Analytics – Part I
Jay Roy, Chief Strategy Officer
www.predictivedashboards.com
jay.roy@predictivedashboards.com
T:214-621-7612