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.
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
An Internet Market Research professional, I am trilingual (French, English, German); I am energetic, like to work in team, and in open, multinational and multicultural environments; I like to beat challenges, to discover new areas, to decipher new trends, to be on the sharp edge.
I am especially at ease with Digital Data, providing innovative concepts and accurate data elicitation. A member of the Digital Analytics Association and a Certified Web Analyst, I am working on missions in the Data Management and Analytics field. I am also open to any assignment as a Chief Data Officer, be it temporary or permanent.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
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.
The content of the document, "Implementing Data Mesh: Six Ways That Can Improve the Odds of Your Success," is a whitepaper authored by Ranganath Ramakrishna from LTIMindtree. The whitepaper introduces the concept of Data Mesh, a socio-technical paradigm that aims to help organizations fully leverage the value of their analytical data.
An Internet Market Research professional, I am trilingual (French, English, German); I am energetic, like to work in team, and in open, multinational and multicultural environments; I like to beat challenges, to discover new areas, to decipher new trends, to be on the sharp edge.
I am especially at ease with Digital Data, providing innovative concepts and accurate data elicitation. A member of the Digital Analytics Association and a Certified Web Analyst, I am working on missions in the Data Management and Analytics field. I am also open to any assignment as a Chief Data Officer, be it temporary or permanent.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
Leading enterprise-scale big data business outcomesGuy Pearce
A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.
Data Science: Unlocking Insights and Transforming IndustriesUncodemy
Data science is an interdisciplinary field that encompasses a range of techniques, algorithms, and tools to extract valuable insights and knowledge from data.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data-Analytics-Essentials-Building-a-Foundation-for-Informed-Business-Choices...Attitude Tally Academy
Unlock the power of informed decision-making with our guide, "From Data to Decisions: Building a Solid Foundation for Business Success" Explore the essentials of data analytics, empowering your business to thrive in a data-driven era. Discover strategic insights, navigate through information overload, and transform raw data into actionable intelligence.Whether you're a startup or an established enterprise, this resource is your roadmap to making sound business choices and charting a course toward success.Dive into the world of data-backed strategies and position your business for growth in today's competitive landscape.
Useful Link:- https://www.attitudetallyacademy.com/class/pythonda
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Data strategy - The Business Game ChangerAmit Pishe
This blog highlights the basics of Data Strategy and its application in real-time business scenarios. Components of Data strategy, Data Analytics have been explained crisply. How Insights and Data Stories can be used to create powerful impact on the Business decisions.
The Softer Skills that analysts need (beyond Data Visualisation)Paul Laughlin
A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Presentation to Analytics Network of the OR Society Nov 2020Paul Laughlin
Presentation on 'The Softer Skills that Analysts need' presented by Paul Laughlin at a virtual event run for the Analytics Network group within the UK OR Society. Exploring Paul's 9 Step Model for effective analysis & explaining how Softer Skills are essential throughout that workflow.
Leading enterprise-scale big data business outcomesGuy Pearce
A talk specially prepared for McMaster University. There is more benefit to thinking about big data as a paradigm rather than as a technology, as it helps shape these projects in the context of resolving some of the enterprise's greatest challenges, including its competitive positioning. This approach integrates the operating model, the business model and the strategy in the solution, which improves the ability of the project to actually deliver its intended value. I support this position with a case study that created audited financial value for a major global bank.
Data Science: Unlocking Insights and Transforming IndustriesUncodemy
Data science is an interdisciplinary field that encompasses a range of techniques, algorithms, and tools to extract valuable insights and knowledge from data.
• History of Data Management
• Business Drivers for implementation of data governance • Building Data Strategy & Governance Framework
• Data Management Maturity Models
• Data Quality Management
• Metadata and Governance
• Metadata Management
• Data Governance Stakeholder Communication Strategy
Data-Analytics-Essentials-Building-a-Foundation-for-Informed-Business-Choices...Attitude Tally Academy
Unlock the power of informed decision-making with our guide, "From Data to Decisions: Building a Solid Foundation for Business Success" Explore the essentials of data analytics, empowering your business to thrive in a data-driven era. Discover strategic insights, navigate through information overload, and transform raw data into actionable intelligence.Whether you're a startup or an established enterprise, this resource is your roadmap to making sound business choices and charting a course toward success.Dive into the world of data-backed strategies and position your business for growth in today's competitive landscape.
Useful Link:- https://www.attitudetallyacademy.com/class/pythonda
Data Strategy - Executive MBA Class, IE Business SchoolGam Dias
For today's enterprise Data is now very much a corporate asset, vital to delivering products and services efficiently and cost effectively. There are few organizations that can survive without harnessing data in some way.
Viewed as a strategic asset, data can be a source of new internal efficiencies, improved competitive advantage or a source of entirely new products that can be targeted at your existing or new customers.
This slide deck contains the highlights of a one day course on Data Strategy taught as part of the Executive MBA Program at IE Business School in Madrid.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Data strategy - The Business Game ChangerAmit Pishe
This blog highlights the basics of Data Strategy and its application in real-time business scenarios. Components of Data strategy, Data Analytics have been explained crisply. How Insights and Data Stories can be used to create powerful impact on the Business decisions.
The Softer Skills that analysts need (beyond Data Visualisation)Paul Laughlin
A talk I gave at #DataVizLive online event in Nov 2020. Introducing the Laughlin Consultancy 9-step model for Softer Skills needed by Analysts & previewing some of those steps (beyond data visualisation & storytelling skills).
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
2. Know how to solve business problems by data-analytic thinking
Have an overview about principles of how to model and how to solve
business problems in a non-rigorous manner
Know several tools and ways of how to practically implement solution
methods
2
Meta Introduction:
Overall goals of this class
3. Data Warehousing / Data Engineering
How to store and access huge amounts of data?
Data Mining / Data Science
How to derive knowledge and profitable business action out of large
databases?
Simulation
How to model and analyse complex relationships in order to derive
profitable business action?
3
Main focus areas
4. Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining
and Data- Analytic Thinking. O‘Reilly, CA 95472, 2013.
Steve Williams: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman
Elsevier, 2016
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Guide to
Intelligent Data Analysis, Springer-Verlag London Limited, 2010
Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009
Eibe Frank, Mark A. Hall, and Ian H. Witten : The Weka Workbench, M Morgan Kaufman
Elsevier, 2016.
Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017
Nikhil Ketkar, Deep Learning with Python, Apress, 2017
François Chollet, Deep Learning with Python, Manning Publications Co., 2018.
4
Main literature
5. The 15th annual KDnuggets Software Poll
https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-
software-used.html
Huge attention from analytics and data mining community and vendors, attracting
over 3,000 voters.
Software
5
6. The top 10 tools by share of users were
– RapidMiner, 44.2% share ( 39.2% in 2013)
– R, 38.5% ( 37.4% in 2013)
– Excel, 25.8% ( 28.0% in 2013)
– SQL, 25.3% ( na in 2013)
– Python, 19.5% ( 13.3% in 2013)
– Weka, 17.0% ( 14.3% in 2013)
– KNIME, 15.0% ( 5.9% in 2013)
– Hadoop, 12.7% ( 9.3% in 2013)
– SAS base, 10.9% ( 10.7% in 2013)
– Microsoft SQL Server, 10.5% (7.0% in 2013)
Software
7. Decision Support Systems in the broadest sense can be defined as
Computer technology solutions that can be used to support complex decision
making and problem solving. [Shim et al. 2002]
Broad definition that encompasses many areas
Application systems
Mathematical modeling
Data driven modeling
Subjective modeling
Decision Support Systems
8. Technological development
More powerful computers, networks, algorithms
Collect data throughout the enterprise
Operations, manufacturing, supply-chain management, customer
behavior, marketing campaigns, …
Exploit data for competitive advantage
8
Ubiquity of data
opportunities
9. There is no unique or mathematical definition of Business Intelligence
The Data Warehousing Institute defines Business Intelligence as…
The process, technologies and tools needed
to turn data into information,
information into knowledge and
knowledge into plans that drive profitable business action.
Business intelligence encompasses data warehousing, business analytics
tools, and content/knowledge management.
http://www.tdwi.org/
Business Intelligence:
Definition (1/2)
9
10. Increased profitability
Distinguish between profitable and non-profitable customers
Decreased costs
Lower operational costs, improve logistics management
Improved Customer-Relationship-Management
Analysis of aggregated customer information to provide better customer
service, increase customer loyalty
Decreased risk
Apply Business Intelligence methods to credit data can improve credit risk
estimation
Benefits of Business
Intelligence (1/2)
10
11. Business Intelligence can help improve
businesses in a variety of fields:
• Customer analysis customer profiling
• Behavior analysis fraud detection, shopping trends, web activity, social network analysis
• Human capital productivity analysis
• Business productivity analysis defect analysis, capacity planning and optimization, risk
management
• Sales channel analysis
• Supply chain analysis supply and vendor management, shipping, distribution analysis
11
Benefits of Business
Intelligence (2/2)
12. Marketing
Online advertising
Recommendations for cross-selling
Customer relationship management
Finance
Credit scoring and trading
Fraud detection
Workforce management
Retail
Wal-Mart, Amazon etc.
Some more examples
12
13. Many cellphone companies have major problems
with customer retention
Cellphone market is saturated
Customer churn is expensive for companies
Keep your customers by predicting who should get a retention offer
Example 2: Predicting
customer churn
13
14. Data science: a set of fundamental principles that guide the
extraction of knowledge from data
Data mining: extraction of knowledge from data via tools/
technologies that incorporate the principles
In this class, we do both!
14
Data science vs. data
mining
15. Data Driven Decision-
making (DDD)
DDD: practice of making decisions
based on the analysis of data (rather
than intuition)
Type-1 decision: “discover”
something new in your data
Wal-Mart/Target example
Type-2 decision: repeat decisions at
massive scale (automatic decision
making)
Customer churn example
15
16. CONCLUSION
• Success in today’s data-oriented business environment
requires being able to think about how these
fundamental concepts apply to particular business
problems—to think data analytically.
• An understanding of these fundamental concepts is
important not only for data scientists themselves, but for
any one working with data scientists, employing data
scientists, investing in data-heavy ventures, or directing
the application of analytics in an organization.
• Understanding the process and the stages helps to
structure our data-analytic thinking, and to make it more
systematic and therefore less prone to errors and
omissions.
17. Provost, F.; Fawcett, T.: Data Science for Business; Fundamental Principles of Data Mining
and Data- Analytic Thinking. O‘Reilly, CA 95472, 2013.
Steve Williams: Business Intelligence Strategy and Big Data Analytics, Morgan Kaufman
Elsevier, 2016
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Guide to
Intelligent Data Analysis, Springer-Verlag London Limited, 2010
Carlo Vecellis, Business Intelligence, John Wiley & Sons, 2009
Eibe Frank, Mark A. Hall, and Ian H. Witten : The Weka Workbench, M Morgan Kaufman
Elsevier, 2016.
Jason Brownlee, Machine Learning Mastery With Weka, E-Book, 2017
Nikhil Ketkar, Deep Learning with Python, Apress, 2017
François Chollet, Deep Learning with Python, Manning Publications Co., 2018.
Sharda, R., Delen, D., Turban, E., (2018). Business intelligence, Analytics, and Data
Science: A Managerial Perspective, 4th Edition, Pearson.
17
Literature
