Data Science Analytics And Information Part 7DataMites
Data analytics is more specific and concentrated than data science. Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling.
VISIT: https://datamites.com/data-science-course-training-hyderabad/
I presented this at ICT Spring Europe 2015 in Luxembourg. The presentation highlights the way in which big data investments are not always delivering on their promise and why brands should consider taking a 'human-centred' approach to big data analytics.
This presentation is a qualitative analysis of a very intelligent article written by Patrick Spenner and Anna Bird about the importance of big data in Marketing specifically.
Algorithms and the technology of personalisation finalColin Strong
This presentation was created for a Google working group meeting which explores the nature of the relationship between the consumer and the Internet. Presented at Google Offices, Berlin June 5th 2015.
Data Science Analytics And Information Part 7DataMites
Data analytics is more specific and concentrated than data science. Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling.
VISIT: https://datamites.com/data-science-course-training-hyderabad/
I presented this at ICT Spring Europe 2015 in Luxembourg. The presentation highlights the way in which big data investments are not always delivering on their promise and why brands should consider taking a 'human-centred' approach to big data analytics.
This presentation is a qualitative analysis of a very intelligent article written by Patrick Spenner and Anna Bird about the importance of big data in Marketing specifically.
Algorithms and the technology of personalisation finalColin Strong
This presentation was created for a Google working group meeting which explores the nature of the relationship between the consumer and the Internet. Presented at Google Offices, Berlin June 5th 2015.
THREE QUESTIONS YOU SHOULD ASK YOURSELF BEFORE YOU IMPLEMENT ANY BIG DATA STR...Tyrone Systems
Whether you run a small business with just a few employees or you are in charge of a multinational corporation,
you can benefit from an effective big
data strategy.
Improve customer experience with a customer intelligence platformDavid Corrigan
A short presentation on the real problem with customer experience - the status quo. The issue is the link between your data and analytics strategy. What's required is context and intelligence to make data analytics-ready. Customer intelligence platforms are designed to do that - to produce an intelligent customer 360 for analytic and operational use cases. Better customer data is the foundation for improving the customer experience.
Welcome to the Chief Analytics Officer Forum Europe
On 7th – 9th March 2016, over 80 Chief Analytics Officers and senior analytics leaders met in London for intimate, top-level discussions; dissecting the role of the CAO, exploring innovative case studies and addressing mutual cross-industry challenges. To learn more, visit http://www.caoforumeurope.com/
This event is organised by http://coriniumintelligence.com/
Welcome to the Chief Analytics Officer Forum Europe
On 7th – 9th March 2016, over 80 Chief Analytics Officers and senior analytics leaders met in London for intimate, top-level discussions; dissecting the role of the CAO, exploring innovative case studies and addressing mutual cross-industry challenges. To learn more, visit http://www.caoforumeurope.com/
This event is organised by http://coriniumintelligence.com/
How to Leverage the Power of Data Analytics in Sales?Shaily Shah
Data is the DNA behind the robust analytics and insights supporting modern organizations to recognize new products, determine how to serve customers better, and enhance operational efficiencies.
THREE QUESTIONS YOU SHOULD ASK YOURSELF BEFORE YOU IMPLEMENT ANY BIG DATA STR...Tyrone Systems
Whether you run a small business with just a few employees or you are in charge of a multinational corporation,
you can benefit from an effective big
data strategy.
Improve customer experience with a customer intelligence platformDavid Corrigan
A short presentation on the real problem with customer experience - the status quo. The issue is the link between your data and analytics strategy. What's required is context and intelligence to make data analytics-ready. Customer intelligence platforms are designed to do that - to produce an intelligent customer 360 for analytic and operational use cases. Better customer data is the foundation for improving the customer experience.
Welcome to the Chief Analytics Officer Forum Europe
On 7th – 9th March 2016, over 80 Chief Analytics Officers and senior analytics leaders met in London for intimate, top-level discussions; dissecting the role of the CAO, exploring innovative case studies and addressing mutual cross-industry challenges. To learn more, visit http://www.caoforumeurope.com/
This event is organised by http://coriniumintelligence.com/
Welcome to the Chief Analytics Officer Forum Europe
On 7th – 9th March 2016, over 80 Chief Analytics Officers and senior analytics leaders met in London for intimate, top-level discussions; dissecting the role of the CAO, exploring innovative case studies and addressing mutual cross-industry challenges. To learn more, visit http://www.caoforumeurope.com/
This event is organised by http://coriniumintelligence.com/
How to Leverage the Power of Data Analytics in Sales?Shaily Shah
Data is the DNA behind the robust analytics and insights supporting modern organizations to recognize new products, determine how to serve customers better, and enhance operational efficiencies.
Marketing & SalesBig Data, Analytics, and the Future of .docxalfredacavx97
Marketing & Sales
Big Data, Analytics,
and the Future of
Marketing & Sales
March 2015
3McKinseyonMarketingandSales.com @McK_MktgSales
Table of contents
Business
Opportunities
Insight and
action
How to get
organized and
get started
8 Getting big impact from big
data
16 Big Data & advanced
analytics: Success stories
from the front lines
20 Use Big Data to find
new micromarkets
24 Smart analytics: How
marketing drives short-term
and long-term growth
30 Putting Big Data and
advanced analytics to work
34 Know your customers
wherever they are
38 Using marketing analytics to
drive superior growth
48 How leading retailers turn
insights into profits
56 Five steps to squeeze more
ROI from your marketing
60 Using Big Data to make
better pricing decisions
60 Marketing’s age of relevance 72 Gilt Groupe: Using Big Data,
mobile, and social media to
reinvent shopping
76 Under the retail microscope:
Seeing your customers for
the first time
80 Name your price: The power
of Big Data and analytics
84 Getting beyond the buzz: Is
your social media working?
90 How to get the most from big
data
94 Five Roles You Need on Your
Big Data Team
98 Want big data sales programs
to work? Get emotional
102 Get started with Big Data:
Tie strategy to performance
106 What you need to make Big
Data work: The pencil
110 Need for speed: Algorithmic
marketing and customer
data overload
114 Simplify Big Data – or it’ll be
useless for sales
54 McKinseyonMarketingandSales.com @McK_MktgSales
Introduction
Big Data is the biggest hame-changing opportunity for marketing and sales
since the Internet went mainstream almost 20 years ago. The data big bang
has unleashed torrents of terabytes about everything from customer behaviors
to weather patterns to demographic consumer shifts in emerging markets.
The companies who are successful in turning data into above-market growth
will excel at three things:
ƒ Using analytics to identify valuable business opportunities from the data to
drive decisions and improve marketing return on investment (MROI)
ƒ Turning those insights into well-designed products and offers that delight
customers
ƒ Delivering those products and offers effectively to the marketplace.
This goldmine of data represents a pivot-point moment for marketing and
sales leaders. Companies that inject big data and analytics into their operation
show productivity rates and profitability that are 5 percent to 6 percent hight
than those of their peers. That’s an advantage no company can afford to
gnome.
This compendium explores the business opportunities, company examples,
and organizational implications of Big Data and advanced analytics. We hope
it provokes good and useful conversations.
Please contact us with your reactions and thoughts.
David Court
Director
David headed McKinsey’s
functional practices, and
currently leads the firm’s digital
in.
Data analytics for the mid-market: myth vs. realityDeloitte Canada
Every mid-market company has data. Data that offers insight to help solve the business issues that matter most.
So why have so few mid-market companies taken the first step? Lack of comfort? Unclear outcomes? Not sure where to start? Analytics helps mid-market companies make smarter business decisions leading to increased productivity, profitability and competitiveness.
Dispel the myths. Recognize the possibilities. Squeeze more out of your data.
These slides were presented by Pauline Chow, Lead Instructor in Data Science & Analytics, General Assembly for her talk at Data Science Pop Up LA in September 14, 2016.
The enterprise marketer's playbook: Building an integrated data strategy.
An integrated data strategy can help any business see customer journeys more clearly ― and then give customers more relevant ads and experiences that get results. So why doesn't everyone have such a strategy? We look at what sets the marketing leaders apart.
Let marketing data be your guide
If you've ever felt too swamped by data to find the customer insights you need, you're not alone. But there's a new and better approach to gaining deeper audience insights: building an integrated data strategy.
Read this report to learn how:
86% of senior executives agree that eliminating organizational silos is critical to expanding the use of data and analytics in decision-making.
75% of marketers agree that lack of education and training on data and analytics is the biggest barrier to more business decisions being made based on data insights.
Leading marketers are 59% more likely to use digital analytics to optimize the user experience in real time.
Humanizing Big Data: The Key to Actionable Customer Journey AnalyticsRocketSource
The ability to gather and act on Big Data has changed our world. For companies, this influx of information is an opportunity to understand consumers on an unprecedented level. But there's a big difference between collecting disparate data points and connecting with consumers through journey analytics.
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/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
7. Unfortunately, there’s no
shortage of individuals with
just enough statistical and
software knowledge to be
data-dangerous. For many
organizations, a mediocre
data scientist may be worse
than none at all.
9. The goal is to make all of the
organization — not just the geeks
and quants — more conversant
in how to align probability,
statistics, technology and
business value creation
11. Small teams not addressing big
problems or grand challenges
but an imperative to generate
insights that could get the
organization doing something
interestingly valuable really fast.
12. One team, did something as simple as comparing
a certain class of tweets from their best customers
with their competitor’s customer’s tweets. The
overlaps and differences immediately suggested
ways to better target and take-away rivals’
customers beyond social media.
13. An industrial products company
started monitoring blogs, boards,
and other social media platforms
around maintenance and service
complaints and then mapping
that data to internal client
maintenance data. The resulting
insights completely changed the
internal dialogues between sales,
customer support, maintenance
engineering.
14. The typical team was collectively less
skilled and competent than a typical data
scientist. But that collective team learned
from each other
18. Marketing
Professor, IIM
Lucknow
Created by Dibya Maheswari,
Accenture Solutions Private
Limited during an internship under
Professor Sameer Mathur, IIM
Lucknow
Dibya Maheswari,
Accenture Soln. Pvt Ltd.
Sameer Mathur
