We should count, analyse and obtain results from these metrics, which are really significant for our business. The rest… hmmm, so we should leave them alone. Because all other figures are only good for blurring our eyes.
Keep it Simple and Make it Fun: Change Management Success Stories from Unityw...Scout RFP
Sourcing and Procurement leaders must make fundamental changes in order to address today’s new, more challenging market environment and prepare for the future. How do you drive adoption of a new process and then ensure improvements stick across the business? In this session, learn from Namejs Kin, Branch Manager - Procurement at Unitywater and Michael Leiken, Head of Spend Management at LendingTree as they walk you through key factors to driving change management from a stakeholder perspective. Learn how to map out your timeline -- from build-out to go live -- and involve key stakeholders along the way. Above all, when it comes to change, these experts will share actionable examples to help keep it simple, make it fun, and ensure it sticks.
We should count, analyse and obtain results from these metrics, which are really significant for our business. The rest… hmmm, so we should leave them alone. Because all other figures are only good for blurring our eyes.
Keep it Simple and Make it Fun: Change Management Success Stories from Unityw...Scout RFP
Sourcing and Procurement leaders must make fundamental changes in order to address today’s new, more challenging market environment and prepare for the future. How do you drive adoption of a new process and then ensure improvements stick across the business? In this session, learn from Namejs Kin, Branch Manager - Procurement at Unitywater and Michael Leiken, Head of Spend Management at LendingTree as they walk you through key factors to driving change management from a stakeholder perspective. Learn how to map out your timeline -- from build-out to go live -- and involve key stakeholders along the way. Above all, when it comes to change, these experts will share actionable examples to help keep it simple, make it fun, and ensure it sticks.
Presentation on "YOU MAY NOT NEED BIG DATA AFTER ALL" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
Acquire Grow & Retain customers - The business imperative for Big DataIBM Software India
The emergence of Big Data and Analytics has changed the way marketing decisions are made. Marketing has moved away from traditional ‘generalisation’ practices such as customer segmentation, geographical targeting etc. and is focussing more on the individual – the ‘Chief Executive Customer’.
Every organization generates large quantities of data — regarding customers, employees, partners, products, services, and operations. Business leaders must be able to access this information quickly and in a context that gives them immediate insight for better, faster business decisions.
Unlocking the Value of Usage Data March 20, 2014
Dan McGaw, Director of Marketing KISSmetrics @danielmcgaw
Puja Ramani, Director of Product Management & Analytics Gainsight @pramani #customersuccess #KISSwebinar
1 The Case for User Analytics, 2 Making User Analytics Actionable, 3 Realizing ROI
We Have Entered The Age Of The Customer
Customer data is everywhere
Welcome to our world of Customer Analytics.
How it works (it’s simple and powerful)
Your customer is at the heart of KISSmetrics
How effective is my signup process?
“You can’t maximize your revenue and profit unless you are tracking the lifetime value of each of your customers. And that’s what KISSmetrics does better than anyone else.” !! — Thomas (Zappos).
Which of my marketing channels has the highest ROI?
What do my customers do before they sign up?
Are customers coming back on a regular basis?
Making User Analytics Actionable
We all know a data driven world is inevitability
We track everything from our health to our homes to our children
We have more data about our customers than ever before
So what’s stopping us?
38% of companies are not able to communicate and interpret customer analytics results.
54% can’t integrate and manage all their data sources.
The four pillar approach is your roadmap to ROI
People Objective Strategy Technology
So what can you do?
Data Science Alert Rules and Playbooks Confirm Intuition
Blend with other data sources to discover insights
Score customer health using usage data
Have one view of all your customers
Fire off tasks or outreach based on usage
Take action on early warnings and manage each event
Consistently collaborate to keep customer relationships healthy
Who’s getting ROI from usage data?
Reduce Churn
THANK YOU
Dan McGaw, Director of Marketing KISSmetrics @danielmcgaw
Puja Ramani, Director of Product Management & Analytics Gainsight @pramani
Analytics Isn’t Enough To Create A Data–Driven CultureaNumak & Company
The earned values are perhaps compatible with older technologies. As we believe big data and AI are extensions of analytical capabilities, the most common and most likely to succeed are those related to "advanced analytics and better decisions."
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
Harvesting the value from Advanced AnalyticsJaap Vink
In general, Analytics help you leverage investments that you have done already in your IT investments, on ERP, on CRM systems, on sales
force automation systems, and on all
the data collection that you put in
place.
Unfortunately, reality isn’t that
straightforward. It’s still a struggle
for most companies to drive valuable
insight into the data they have.
Analysis of ted talk, "Lies, Damned Lies and Statistics" by Sebastian WernickeVaibhav Srivastav
This presentation gives brief analysis of the TED talk, "Lies, Damned Lies and Statistics" by Sebastian Wernicke.
This gives relevant insights from the talk about certain key points that are needed to be kept in mind while forming a certain speech.
Presentation on "YOU MAY NOT NEED BIG DATA AFTER ALL" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
Acquire Grow & Retain customers - The business imperative for Big DataIBM Software India
The emergence of Big Data and Analytics has changed the way marketing decisions are made. Marketing has moved away from traditional ‘generalisation’ practices such as customer segmentation, geographical targeting etc. and is focussing more on the individual – the ‘Chief Executive Customer’.
Every organization generates large quantities of data — regarding customers, employees, partners, products, services, and operations. Business leaders must be able to access this information quickly and in a context that gives them immediate insight for better, faster business decisions.
Unlocking the Value of Usage Data March 20, 2014
Dan McGaw, Director of Marketing KISSmetrics @danielmcgaw
Puja Ramani, Director of Product Management & Analytics Gainsight @pramani #customersuccess #KISSwebinar
1 The Case for User Analytics, 2 Making User Analytics Actionable, 3 Realizing ROI
We Have Entered The Age Of The Customer
Customer data is everywhere
Welcome to our world of Customer Analytics.
How it works (it’s simple and powerful)
Your customer is at the heart of KISSmetrics
How effective is my signup process?
“You can’t maximize your revenue and profit unless you are tracking the lifetime value of each of your customers. And that’s what KISSmetrics does better than anyone else.” !! — Thomas (Zappos).
Which of my marketing channels has the highest ROI?
What do my customers do before they sign up?
Are customers coming back on a regular basis?
Making User Analytics Actionable
We all know a data driven world is inevitability
We track everything from our health to our homes to our children
We have more data about our customers than ever before
So what’s stopping us?
38% of companies are not able to communicate and interpret customer analytics results.
54% can’t integrate and manage all their data sources.
The four pillar approach is your roadmap to ROI
People Objective Strategy Technology
So what can you do?
Data Science Alert Rules and Playbooks Confirm Intuition
Blend with other data sources to discover insights
Score customer health using usage data
Have one view of all your customers
Fire off tasks or outreach based on usage
Take action on early warnings and manage each event
Consistently collaborate to keep customer relationships healthy
Who’s getting ROI from usage data?
Reduce Churn
THANK YOU
Dan McGaw, Director of Marketing KISSmetrics @danielmcgaw
Puja Ramani, Director of Product Management & Analytics Gainsight @pramani
Analytics Isn’t Enough To Create A Data–Driven CultureaNumak & Company
The earned values are perhaps compatible with older technologies. As we believe big data and AI are extensions of analytical capabilities, the most common and most likely to succeed are those related to "advanced analytics and better decisions."
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
Harvesting the value from Advanced AnalyticsJaap Vink
In general, Analytics help you leverage investments that you have done already in your IT investments, on ERP, on CRM systems, on sales
force automation systems, and on all
the data collection that you put in
place.
Unfortunately, reality isn’t that
straightforward. It’s still a struggle
for most companies to drive valuable
insight into the data they have.
Analysis of ted talk, "Lies, Damned Lies and Statistics" by Sebastian WernickeVaibhav Srivastav
This presentation gives brief analysis of the TED talk, "Lies, Damned Lies and Statistics" by Sebastian Wernicke.
This gives relevant insights from the talk about certain key points that are needed to be kept in mind while forming a certain speech.
Analysis of ted talk, "3 ways to spot a bad statistics" by Mona ChabaliVaibhav Srivastav
This presentation does analysis of the ted talk, "3 ways to spot a bad statistics" by Mona Chabali.
It goes through key insights mentioned by her during her talk.
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Slide 1: Beauty of Data Visualization Intro
Slide 2: The Billion Dollar O-gram
Slide 3: World's Fear Landscape overtime in media
Slide 4: Explanation of landscape.
Slide 5: Break-up times, as per Facebook status updates.
Slide 6: Attack probability data of countries
Slide 7: "Let dataset, change your mindset." -Hans Rosling
Slide 8: Language of the eye (Pattern) and language of the brain (learn) creates beautiful visualizations.
Slide 9: How could managers use data visualization?
Slide 10: Data Visualization can compile a huge number of databases into few pages.
Slide 11: It will help giving a visual aspect to your data.
Slide 12: It will help in discovering new facts.
Slide 13: Beautiful, Lovely data.
Slide 14: Thank You.
Analysis of the ted talk by Jer Thorp on 'Make Data more Human.Vaibhav Srivastav
The presentation is based on analysis of the ted talk by Jer Thorp on 'Make Data more Human.'
Slide 1: Introduction to the topic
Slide 2: Jer Thorp makes one of the most beautiful data visualizations in the world.
Slide 3: He designed the naming algorithm for the 09/11 Memorial, Ney York city, wherein all are connected near to the person they were connected with.
Slide 4: He has insisted on giving data human traits, and treat them like one.Following methods:
Slide 5: Data is bigger, doesn't mean its better. It may have lots of errors, redundancy, irrelevancy.
Slide 6: We need to give data a human-like context.
Slide 7: Data can be related to the most human of the attribute, Emotion.
Slide 8: Visualization of data solves the majority of the problems.
Slide 9: Jer Thorp co-founded icascade, a start-up meant to construct a detailed picture of how information propagated through the social media space.
Slide 10: Visualization helps plot huge amounts of complex data, clarifying all the factors.
Slide 11: Benefits of Data treatment
Slide 12: Absorb info. in new and more constructive ways.
Visualize relationships and patterns between operational and business activities.
Identify and act on emerging trends.
Manipulate and interact directly with data.
Foster a new business language.
Slide 13: Have a storytelling data.
Slide 14: openpaths.cc a site to donate your data for research purposes.
Slide 15: Thank You
Analysis of the article by Thoman C Redman on 'How to start thinking like a D...Vaibhav Srivastav
Slide 1: Welcome slide on analysis of the article by Thoman C Redman on 'How to start thinking like a Data Scientist?'
Slide 2: Why, Why do we need to think like Data Scientists?
Slide 3: Because Data are forcing their way into all the industries. Data is the new currency.
Slide 4: Procedure to think like a Data Scientist
Slide 5: Step 1- Define the problem statement to be solved.
Slide 6: Step 2- Think about all the data that can solve your problem.
Slide 7: Step 3- Collect your data using necessary functions and protocols.
Slide 8: Clean your data for missing and irregular files.
Slide 9: Have confidence in the efficiency of your data.
Slide 10: Be wise to your data, Don't get too hard on it.
Slide 11: Visualize your data, Plot the graphs.
Slide 12: Do data analysis.
Slide 13: Check for variations in the data.
Slide 14: Formation of hypothesis based on observation from data.
Slide 15: Test your hypothesis on real-valued function.
Slide 16: Communicate the results of the evaluation.
Slide 17: Don't be data illiterate.
Slide 18: Thank You!
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
7. Most companies don’t do a good job
with the information they already
have.
They don’t know how
to manage it, analyse it
in ways that enhance
their understanding,
and then make changes
in response to new
insights.
9. z
Need to learn how to use the data already
embedded in their core operating systems
INSiGHT
10. z
Digital economy is all about capturing,
analysing, and using information to serve
customers.
Companies that have what we call a culture
of evidence-based decision making, have
all seen improvements in their business
performance
INSiGHT
11. z
why don’t more companies make better
use of data and analysis?
Adopting evidence-based
decision making is a difficult
cultural shift: Work processes
must be redefined, data must
be scrubbed, and business
rules must be established to
guide people in their work.
12. z
Four must practices for
‘Decision makers’
Establish one undisputed source of
performance data
They give decision makers at all levels
near-real-time feedback
They consciously articulate their business
rules and regularly update them in response
to facts
They provide high-quality coaching to
employees who make decisions on a
regular basis.
13. z
Toshifumi Suzuki
CEO and president of
7-Eleven
Toshifumi Suzuki placed
responsibility for ordering—
the single most important
decision in the business—in
the hands of the stores’
200,000 mostly part-time
salesclerks. Those employees,
Suzuki believed, understood
their customers and, with
good information, could make
the best decisions about what
would sell quickly.
14. z
Relevant insights for a manager
Use Scorecard
The best way to teach people how to use data to create business benefits is to
provide them with data about their own performance. Regular scorecards clarify
individual accountability and provide consistent feedback so that individuals
know how they are doing.
15. z
Explicitly Manage Your Business
Rules
The best way to teach people how to use data to create business benefits is to
provide them with data about their own performance. Regular scorecards clarify
individual accountability and provide consistent feedback so that individuals
know how they are doing.
Relevant insights for a manager