This document discusses how advanced analytics and big data have become top priorities for companies. It argues that big data has the potential to transform businesses and deliver major performance gains. However, companies need to carefully define a pragmatic strategy for using data and analytics, focusing on how to make better decisions. The key is having a clear strategy for how to use data to compete and deploying the right IT architecture and analytical capabilities. Past failures with CRM show that analytics initiatives need to align with companies' processes and decision-making.
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.
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.
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.
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.
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.
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.
Right First Time: the importance of "working the portfolio" onceStatPro Group
Automating process and using data just once - but for many purposes - is increasingly seen as the best way for asset managers to respond to the demand for 24/7 reporting capability.
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.
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.
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.
Right First Time: the importance of "working the portfolio" onceStatPro Group
Automating process and using data just once - but for many purposes - is increasingly seen as the best way for asset managers to respond to the demand for 24/7 reporting capability.
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
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
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 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
As firms move from siloed, transaction-oriented systems to more integrated,
socially aware ones, they will face challenges related to customer data. “Big data”
is characterized by increases in data volume, velocity, variety, and variability. To
improve customer engagement, companies must invest in solutions to effectively
manage big data.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
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.
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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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. 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.
3. They also see that big data is attracting serious
investment from technology leaders such as
IBM and Hewlett-Packard.
Meanwhile, the tide of private-equity and
venture-capital investments in big data
continues to swell.
4.
5. 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.
As data-driven strategies take hold, they will
become an increasingly important point of
competitive differentiation.
6.
7. Leaders are understandably leery of making
substantial investments in big data and
advanced analytics.
They’re convinced that their organizations
simply aren’t ready.
After all, companies may not fully understand
the data they already have, don’t yield insights
that can be put to use
8.
9. 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
10. 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.
11. 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.
12. Get the necessary IT support.
Legacy IT structures may hinder new types of data
sourcing, storage, and analysis.
Build Models That 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.
13. Transform Your Company’s Capabilities
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.
14.
15. Managers also need to get creative about the
potential of external and new sources of data.
Social media are generating terabytes of
nontraditional, unstructured data in the form of
conversations, photos, and video. Add to that
the streams of data flowing in from sensors,
monitoring processes, and external sources
that range from local demographics to
weather forecasts
16. Leaders at all levels must also be attuned to
novel approaches to gathering and
husbanding information. As business
practices in the internet era continue to
evolve, inspiration can often arise from a scan
of the external environment.