This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data.
Enable better business decision making with big dataChristine K.
Organisations are finding it hard to navigate a deluge of data about customers, employees, suppliers, stakeholders, partners and competitors.
We identified the top 5 areas where better data quality and analysis would have the biggest impact.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data.
Enable better business decision making with big dataChristine K.
Organisations are finding it hard to navigate a deluge of data about customers, employees, suppliers, stakeholders, partners and competitors.
We identified the top 5 areas where better data quality and analysis would have the biggest impact.
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
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/
During the Chief Data Officer Exchange event in London, Denodo discussed the different ways in which data virtualization can help businesses. In this presentation, our guest speaker Simon Gratton (Deloitte UK), provides information about the emerging approaches to achieving agile data delivery and the cultural issues that stand in our way.
FP&A: Innovations in Financial Analytics to Support Organic Growth and Busine...James Myers
Despite recent advances in cloud computing and related technologies, many organizations are still far from where they want and need to be in leveraging data to drive performance improvements. Companies still often apply intuition, rather than hard data, when making strategic and operational decisions. Even when companies define performance metrics, many define vanity as opposed to actionable metrics. Discover new ways to leverage your data to drive shareholder value. In this webinar, you will see how companies are utilizing financial analytics to drive a competitive advantage.
"Making Advanced Analytics Work for You" by Dominic Barton and David CourtRahul Chintu
This is a presentation done during the Data Science and Managerial Relevance internship under the guidance of prof.Sameer Mathur (Ph.d,Carnige Mellon),IIM-LUCKNOW
Today's Chief Data Officer: 3 Types of CDOs, Key Challenges, and Opportunitie...Scott Richardson
In this presentation we examine the 3 types of Chief Data Officers that are common in the industry today. We discuss their responsibilities, key challenges, and present ideas for having an meaningful impact. A great overview of the CDO role and how this executive position can contribute to the success of your business.
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
Data Analytics with Managerial Applications Internship under Prof. Sameer Mathur-Making Advanced Analytics Work for You by Dominic Barton and David Court-presentation
In it Together: why “collaboration” is now an essential skillset for asset ma...StatPro Group
Traditionally, asset management teams have worked in silos. But with asset classes and the data becoming more complex, greater collaboration is now needed. Find out why.
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/
During the Chief Data Officer Exchange event in London, Denodo discussed the different ways in which data virtualization can help businesses. In this presentation, our guest speaker Simon Gratton (Deloitte UK), provides information about the emerging approaches to achieving agile data delivery and the cultural issues that stand in our way.
FP&A: Innovations in Financial Analytics to Support Organic Growth and Busine...James Myers
Despite recent advances in cloud computing and related technologies, many organizations are still far from where they want and need to be in leveraging data to drive performance improvements. Companies still often apply intuition, rather than hard data, when making strategic and operational decisions. Even when companies define performance metrics, many define vanity as opposed to actionable metrics. Discover new ways to leverage your data to drive shareholder value. In this webinar, you will see how companies are utilizing financial analytics to drive a competitive advantage.
"Making Advanced Analytics Work for You" by Dominic Barton and David CourtRahul Chintu
This is a presentation done during the Data Science and Managerial Relevance internship under the guidance of prof.Sameer Mathur (Ph.d,Carnige Mellon),IIM-LUCKNOW
Today's Chief Data Officer: 3 Types of CDOs, Key Challenges, and Opportunitie...Scott Richardson
In this presentation we examine the 3 types of Chief Data Officers that are common in the industry today. We discuss their responsibilities, key challenges, and present ideas for having an meaningful impact. A great overview of the CDO role and how this executive position can contribute to the success of your business.
We conducted a ground-breaking survey of the UK’s data and business professionals to get a snapshot of the state of the world of data, uncover some of the issues facing the industry and get a sense of the changes on the horizon. The results were enlightening, and in some cases, very surprising.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
Data Analytics with Managerial Applications Internship under Prof. Sameer Mathur-Making Advanced Analytics Work for You by Dominic Barton and David Court-presentation
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
Big & Fast Data: The Democratization of InformationCapgemini
Moving from the Enterprise Data Warehouse to the Business Data Lake
Is it possible that ubiquitous analytics represents the next phase of the information age? New business models are emerging, enabled by big data that business leaders are eager to adopt in order to gain advantage and mitigate disruption from start-ups and parallel industries. The winners are likely to be those that master a cultural shift as well as a technology evolution.
Our view is this will be realized through the alignment of a business-centric big data strategy, combined with democratization of the analytical tools, platforms and data lakes that will enable business stakeholders to create, industrialize and integrate insights into their business processes.
Innovative approaches are needed to free up data from silos whilst encouraging both the sharing and the continuous improvement of insights across the business. While it will be evolution for some, revolution for others; the risk of status quo is not just the loss of opportunity but also a widening gap between business and the internal technology functions.
https://www.capgemini.com/thought-leadership/big-fast-data-the-democratization-of-information
This is an evaluation of Making Advanced Analytics Work for You by Dominic Barton and David Court telling us how Big Data can transform a company's business in my DSA Internship.
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.
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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
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/
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.
2. Introduction
Big data and analytics have rocketed to the top of the corporate
agenda.
Executives look with admiration at how Google, Amazon, and
others have eclipsed competitors with powerful new business
models that derive from an ability to exploit data.
4. They also see that big data is attracting
serious investment from technology
leaders such as IBM and Hewlett-Packard
5. The tide of private-equity and
venture-capital investments in
big data continues to swell.
6. The trend is generating plenty of hype, but we believe
that senior leaders are right to pay attention.
Big data could transform the way companies do
business, delivering the kind of performance gains
last seen in the 1990s,when organizations redesigned
their core processes.
8. Companies that inject big data and
analytics into their operations show
productivity rates and profitability
that are 5% to 6% higher than those
of their peers
9. Even so, our experience reveals that
most companies are unsure how to
proceed. Leaders are understandably
leery of making substantial
investments in big data and
advanced analytics.
10. Experts descended on
boardrooms promising
impressive results if new IT
systems were built to collect
massive amounts of
customer data.
To be fair, most companies
eventually managed to get
their CRM programs on track,
but not before some had
suffered sizable losses and
several CRM champions had
lost career momentum
11. Given this history, we empathize with executives who are cautious
about big data. Nevertheless, we believe that the time has come to
define a pragmatic approach to big data and advanced analytics—one
tightly focused on how to use the data to make better decisions.
Two important features underpin those activities: a clear strategy for
how to use data and analytics to compete, and deployment of the right
technology architecture and capabilities.
12. Equally important, the desired
business impact must drive an
integrated approach to data
sourcing, model building, and
organizational transformation.
13. That’s how you avoid the common trap of starting with the
data and simply asking what it can do for you. Leaders
should invest sufficient time and energy in aligning
managers across the organization in support of the mission.
14. 1. Choose the Right Data
• The universe of data and modeling has changed
vastly over the past few years.The sheer volume
of information, particularly from new sources
such as social media and machine sensors, is
growing rapidly.
• The opportunity to expand insights by combining
data is also accelerating, as more-powerful, less
costly software abounds and information can be
accessed from almost anywhere at any time.
15. • Legacy IT structures may hinder new types of data
sourcing, storage, and analysis. Build ModelsThat
Predict and Optimize Business Outcomes
• Data are essential, but performance improvements
and competitive advantage arise from analytics
models that allow managers to predict and optimize
outcomes.
Get the
necessary
IT support.
16. 2. Build ModelsThat Predict and
Optimize Business Outcomes
• Data are essential, but performance
improvements and competitive advantage
arise from analytics models that allow
managers to predict and optimize outcomes.
• More important, the most effective approach
to building a model rarely starts with the data;
instead it originates with identifying the
business opportunity and determining how the
model can improve performance.
17. • The lead concern expressed to us by senior executives
is that their managers don’t understand or trust big
data–based models.
• Develop business-relevant analytics that can be put to
use.
• Like early CRM misadventures, many initial
implementations of big data and analytics fail simply
because they aren’t in sync with the company’s day-
to- day processes and decision-making norms.
Transform
Your
Company’s
Capabilities
18. Develop business-relevant
analytics that can be put to
use.
• Like early CRM misadventures,
many initial implementations of big
data and analytics fail simply
because they aren’t in sync with the
company’s day-to-day processes
and decision-making norms.
19.
20. • Even with simple and usable
models, most organizations will
need to upgrade their analytical
skills and literacy. Managers must
come to view analytics as central
to solving problems and
identifying opportunities—to
make it part of the fabric of daily
operations.
Develop
capabilities
to exploit
big data.