1) There is a growing gap in capabilities and performance between companies that invest heavily in data and analytics compared to those that invest less. The capability gap is exacerbated by a shortage of analytical talent.
2) The amount of data being created is growing exponentially, estimated at 2.5 quintillion bytes per day globally. However, most organizations are not effectively using the data they already have.
3) Investing in analytics can provide significant financial benefits across industries. For example, leveraging big data in healthcare could capture $300 billion annually and increase retailers' operating margins by 60%.
How Do You Improve Data Skills and Data Literacy in your Business?Bernard Marr
Data literacy should be a priority for every organization. Investing in data skills and data literacy is critical for all companies today. Most companies have a deficit in data skills that should be addressed as quickly as possible. There are several ways to improve data skills and data literacy in your business.
Proper forecasting is a key factor that decides a company's survival in the market. Companies that are good at forecasting can win over the market quickly because they are ready for every uncertain situation. Companies now have started relying on digital methods of forecasting for better and genuine results.
The case study on Pricing Strategy of Cath KidstonPantho Sarker
Cath Kidston is a well-known company in UK in the field of homeware and fashion products. We were assigned to perform different analysis using the data of Cath Kidston. So, In this report, we presented different analysis based on Cath Kidston’s data.
Through this report the following thing are going to be to be covered:
Chapter 01 discusses on the introductory part of the study.
To give an overview of “Cath Kidston” through Chapter 02).
In Chapter 03, we conducted the analysis of the economy. In this chapter we included economic recession and economic boom and then we apply economic analysis in respect of Cath Kidston.
In Chapter 04, we run industry analysis by applying Porter's Five Forces Model and PESTLE analysis. In this chapter first we discuss about the theoretical framework of the mentioned two tools and then we apply these tools to analyse the industry related to Cath Kidston.
In Chapter 05, the analysis of the company was performed using SWOT analysis and business cycle as well as using variability analysis.
In Chapter 06, the problem of the case was introduced.
In Chapter 07, a solution was given based on the problem statement presented in the chapter six. In addition, we solved the questions which were provided in the case.
In chapter 08, the required recommendations are provided with due context.
At last to overview the whole study conclusion will work to that purpose.
This report aims at introducing Cath Kidston assessing its different sides. It is hoped that readers will get a better overview on Cath Kidston through this presentation.
COVID-19 has increased the need for intelligent decisioning through AI, but ROI is not guaranteed. Here's how to accelerate AI outcomes, according to our recent study.
How Do You Improve Data Skills and Data Literacy in your Business?Bernard Marr
Data literacy should be a priority for every organization. Investing in data skills and data literacy is critical for all companies today. Most companies have a deficit in data skills that should be addressed as quickly as possible. There are several ways to improve data skills and data literacy in your business.
Proper forecasting is a key factor that decides a company's survival in the market. Companies that are good at forecasting can win over the market quickly because they are ready for every uncertain situation. Companies now have started relying on digital methods of forecasting for better and genuine results.
The case study on Pricing Strategy of Cath KidstonPantho Sarker
Cath Kidston is a well-known company in UK in the field of homeware and fashion products. We were assigned to perform different analysis using the data of Cath Kidston. So, In this report, we presented different analysis based on Cath Kidston’s data.
Through this report the following thing are going to be to be covered:
Chapter 01 discusses on the introductory part of the study.
To give an overview of “Cath Kidston” through Chapter 02).
In Chapter 03, we conducted the analysis of the economy. In this chapter we included economic recession and economic boom and then we apply economic analysis in respect of Cath Kidston.
In Chapter 04, we run industry analysis by applying Porter's Five Forces Model and PESTLE analysis. In this chapter first we discuss about the theoretical framework of the mentioned two tools and then we apply these tools to analyse the industry related to Cath Kidston.
In Chapter 05, the analysis of the company was performed using SWOT analysis and business cycle as well as using variability analysis.
In Chapter 06, the problem of the case was introduced.
In Chapter 07, a solution was given based on the problem statement presented in the chapter six. In addition, we solved the questions which were provided in the case.
In chapter 08, the required recommendations are provided with due context.
At last to overview the whole study conclusion will work to that purpose.
This report aims at introducing Cath Kidston assessing its different sides. It is hoped that readers will get a better overview on Cath Kidston through this presentation.
COVID-19 has increased the need for intelligent decisioning through AI, but ROI is not guaranteed. Here's how to accelerate AI outcomes, according to our recent study.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalCapgemini
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
The Future of SharePoint (FOSP) - SharePoint Saturday Redmond - Sept 22 2012Jeff Shuey
The Future of SharePoint (FOSP) is going to be all about data and access to data. Data is being created everywhere today – from traditional corporate processes to social media and mobile computing efforts. Capturing, Managing and Governing this data will be critical to every business. SharePoint is at the early stages of reaching its full potential and to being THE PLACE where data is surfaced from disparate repositories.
Objective Benchmarking for Improved Analytics Health and EffectivenessPersonifyMarketing
Achieving a high state of analytics excellence can be a daunting task. It involves mastering progressive stages of data health, technological capability, and staff readiness, all while putting out countless fires and responding to last-minute requests for analysis. Strategic progress can be slow, and charting that progress for the executive team, cumbersome and uncertain.
Join us as Denny Lengkong from Personify Implementation Partner, IntelliData, and Personify's Solution Director, Bill Connell, present a rational framework for understanding analytics health and effectiveness. This webinar will help you learn how to make targeted investments in analytics over time that everyone in your organization will understand.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
Shopin's Retail Intelligence Data Engine (R.I.D.E.) analysis of CoachEran Eyal
Shopin announced the solution to the lack of access to purchase data from all retailers ranging from Amazon, eBay to Macy’s, Nordstrom, Coach, Michael Kors and more, in the form of the Retail Intelligence Data Engine.
R.I.D.E. (Retail Intelligence Data Engine) is a patent-protected innovation from Shopin’s team that has extracted and reverse-engineered the purchase data of online retailers from their websites and it measures the strength of influence of each retailer, brand or product upon each other, no matter where that product is sold.
Shopin’s CEO Eran Eyal describes R.I.D.E. as a “Purchase Data Omniscience Engine”.
Eran Eyal delves deeper: “In the past, only companies like Amazon have had sufficient purchase data to know which products to promote, recommend with each other, or which products to create. It’s important to note that 35% of Amazon’s revenue comes from their purchase data powered recommendations. That’s 17% of the United State’s total eCommerce revenue! We found a way to democratize and decentralize this data as well as over 80% of U.S. fashion e-commerce in scale. We don’t just deliver the data, we give you the predictive actionable recommendation.”
Shopin released Q2 2019 figures that exceeded projections:
4BN+ Purchase data Transactions (value of over $400BN)
200MM+ product/ SKU cco-occurrence
300MM+ SKUs identified and tracked
150,000 Brands tracked
Shopin brings a global perspective to retailers through an industry-wide transaction data fabric and unique analytics platform which leverages proprietary Visual AI and NLP technology. The interaction of their proprietary Visual Artificial Intelligence and Natural Language Processing models enables unique capabilities for the depth and breadth of their analysis and knowledge.
I wrote this business plan as part of the "It's Your Time" Entrepreneur Training Program offered by the Inland Empire Women's Business Center and sponsored by Citibank.
Out of 70 participants and 21 submitted business plans, this one was chosen by a panel of judges as the first place winner, and I received a prize package of over $2500 in consulting services.
I eventually determined that the idea behind Homeschool Catalyst no longer resonated for me, and I moved on to a different start-up idea.
Writing this was a great learning process, and I put it up here to share with other aspiring entrepreneurs who need inspiration and examples of an award winning business plan.
OpenText Presents: Mastering the Digital Economy through Big Data and Custome...OpenText
IDC’s Helena Schwenk joins OpenText to discuss how big data can help overcome the barriers faced by Executives aiming to redefine their businesses to compete in the Digital Economy. The era of self-service analysis has exposed data to more people within a business, but this in itself creates challenges for IT, who retain responsibility for the health and hygiene of data, as well as security. View the webinar here: http://ow.ly/bImR307Ptue
Analytics Trends 2015: A below-the-surface lookDeloitte Canada
Big Data is a big deal for everyone these days and only growing in importance, especially when it comes to analytics generating actionable insights. Deloitte has identified eight significant analytics trends to watch in 2015 – including one supertrend that will impact everything else.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
Shared Service Centers: Risks & Rewards in the Time of CoronavirusCognizant
Our recent research reveals that organizations are reassessing the pros and cons of captive services. Companies are twice as likely to reduce than increase their use of shared service centers.
Cracking the Data Conundrum: How Successful Companies Make #BigData OperationalCapgemini
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
So, how can organizations make Big Data operational? There are many factors that go into the making of a successful Big Data implementation. However, the single biggest factor that we observed in our research was that organizations that have a strong operating model stood apart. This operating model has multiple distinct elements, which include, among others, a well-defined organizational structure, systematic implementation plan, and strong leadership support. For instance, success rates for organizations with an analytics business unit are nearly 2.5 times those that have ad-hoc, isolated teams. The report highlights the key factors for successful Big Data implementations.
The Future of SharePoint (FOSP) - SharePoint Saturday Redmond - Sept 22 2012Jeff Shuey
The Future of SharePoint (FOSP) is going to be all about data and access to data. Data is being created everywhere today – from traditional corporate processes to social media and mobile computing efforts. Capturing, Managing and Governing this data will be critical to every business. SharePoint is at the early stages of reaching its full potential and to being THE PLACE where data is surfaced from disparate repositories.
Objective Benchmarking for Improved Analytics Health and EffectivenessPersonifyMarketing
Achieving a high state of analytics excellence can be a daunting task. It involves mastering progressive stages of data health, technological capability, and staff readiness, all while putting out countless fires and responding to last-minute requests for analysis. Strategic progress can be slow, and charting that progress for the executive team, cumbersome and uncertain.
Join us as Denny Lengkong from Personify Implementation Partner, IntelliData, and Personify's Solution Director, Bill Connell, present a rational framework for understanding analytics health and effectiveness. This webinar will help you learn how to make targeted investments in analytics over time that everyone in your organization will understand.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
Manufacturers were hard hit by COVID-19, but our research reveals the next best steps to take, based on the investments digital leaders in the industry have made and plan to make.
Shopin's Retail Intelligence Data Engine (R.I.D.E.) analysis of CoachEran Eyal
Shopin announced the solution to the lack of access to purchase data from all retailers ranging from Amazon, eBay to Macy’s, Nordstrom, Coach, Michael Kors and more, in the form of the Retail Intelligence Data Engine.
R.I.D.E. (Retail Intelligence Data Engine) is a patent-protected innovation from Shopin’s team that has extracted and reverse-engineered the purchase data of online retailers from their websites and it measures the strength of influence of each retailer, brand or product upon each other, no matter where that product is sold.
Shopin’s CEO Eran Eyal describes R.I.D.E. as a “Purchase Data Omniscience Engine”.
Eran Eyal delves deeper: “In the past, only companies like Amazon have had sufficient purchase data to know which products to promote, recommend with each other, or which products to create. It’s important to note that 35% of Amazon’s revenue comes from their purchase data powered recommendations. That’s 17% of the United State’s total eCommerce revenue! We found a way to democratize and decentralize this data as well as over 80% of U.S. fashion e-commerce in scale. We don’t just deliver the data, we give you the predictive actionable recommendation.”
Shopin released Q2 2019 figures that exceeded projections:
4BN+ Purchase data Transactions (value of over $400BN)
200MM+ product/ SKU cco-occurrence
300MM+ SKUs identified and tracked
150,000 Brands tracked
Shopin brings a global perspective to retailers through an industry-wide transaction data fabric and unique analytics platform which leverages proprietary Visual AI and NLP technology. The interaction of their proprietary Visual Artificial Intelligence and Natural Language Processing models enables unique capabilities for the depth and breadth of their analysis and knowledge.
I wrote this business plan as part of the "It's Your Time" Entrepreneur Training Program offered by the Inland Empire Women's Business Center and sponsored by Citibank.
Out of 70 participants and 21 submitted business plans, this one was chosen by a panel of judges as the first place winner, and I received a prize package of over $2500 in consulting services.
I eventually determined that the idea behind Homeschool Catalyst no longer resonated for me, and I moved on to a different start-up idea.
Writing this was a great learning process, and I put it up here to share with other aspiring entrepreneurs who need inspiration and examples of an award winning business plan.
OpenText Presents: Mastering the Digital Economy through Big Data and Custome...OpenText
IDC’s Helena Schwenk joins OpenText to discuss how big data can help overcome the barriers faced by Executives aiming to redefine their businesses to compete in the Digital Economy. The era of self-service analysis has exposed data to more people within a business, but this in itself creates challenges for IT, who retain responsibility for the health and hygiene of data, as well as security. View the webinar here: http://ow.ly/bImR307Ptue
Analytics Trends 2015: A below-the-surface lookDeloitte Canada
Big Data is a big deal for everyone these days and only growing in importance, especially when it comes to analytics generating actionable insights. Deloitte has identified eight significant analytics trends to watch in 2015 – including one supertrend that will impact everything else.
Big Data: Real-life examples of Business Value Generation with ClouderaCapgemini
Capgemini has helped multiple organizations to put Big Data to work and create value for their business and their clients.
This prsentation looks at real-world cases of how organizations are using, or planning to use, big data technology. It will look at the different ways in which the technology is being used in a business context.
Examples are drawn from Retail, Telco, Financial Services, Public Sector and Consumer goods.
It will look at a range of business scenarios from simple cost reduction through to new business models looking at how the business case has been built and what value has been realized.
It will also look at some of the practical challenges and approaches taken and specifically the application of Enterprise Data Hubs in collaboration with its prime partner Cloudera.
Written by Richard Brown, Global Programme Leader, Big Data & Analytics, Capgemini
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
Shared Service Centers: Risks & Rewards in the Time of CoronavirusCognizant
Our recent research reveals that organizations are reassessing the pros and cons of captive services. Companies are twice as likely to reduce than increase their use of shared service centers.
Big Data, Big Thinking: Untapped OpportunitiesSAP Technology
In this webinar factsheet, SAP’s Rohit Nagarajan and Suni Verma from Ernst & Young explore Big Data in India, adoption patterns across the globe, and how you can embark on your own Big Data journey.
Leverage Sage Business Intelligence for Your OrganizationRKLeSolutions
Learn how Sage Business Intelligence provides the insight you need to make better decisions faster! This informative presentation explores Sage Intelligence and Sage Enterprise Intelligence solutions for Sage 100, Sage 500 and Sage X3.
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.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
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.
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
Data as a Profit Driver – Emerging Techniques to Monetize Data as a Strategic...DATAVERSITY
The Digital Economy is changing the way organizations do business across the globe, and is set to transform the economy on an unprecedented scale. Business optimization, and entirely new business models are emerging as data-driven technology provides unprecedented opportunity for innovation and change. In many organizations, data not only supports business profitability, but data itself has become the critical business asset.
What does it mean to leverage data as a business asset? And how can today’s data-centric technologies support the data-driven revolution? Join our expert panelists as they discuss the latest innovations in the data landscape.
Entry Points – How to Get Rolling with Big Data AnalyticsInside Analysis
The Briefing Room with Robin Bloor and IBM
Live Webcast Sept. 24, 2013
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?AT=pb&SP=EC&rID=7501927&rKey=664935ceb7de1aec
Where to begin? That question remains prominent for many organizations who are trying to leverage the value of big data analytics. Most sources of big data are quite different than traditional enterprise data systems. This requires new skill sets, both for the granular integration work, as well as the strategic business perspective required to design useful solutions.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the pain points associated with modern data volumes and types. He will be briefed by Rick Clements of IBM, who will tout IBM's big data platform, specifically InfoSphere BigInsights, InfoSphere Streams and InfoSphere Data Explorer. He will also present specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.
Visit InsideAnalysis.com for more information
The demand for BI continues to grow, and while there's no question that analytics brings value, there is often uncertainty about how BI initiatives will deliver bottom-line benefits. Your business case for BI should prove ROI, but this is not always a straightforward process.
How To Pick The Best Analytics Tools: Product Analytics Landscape
Here, we’ll talk about assessment criteria, key features, and greater for deciding on systems and gear that match your enterprise app development desires.
Choosing the right solution for your data
Because massive facts apply to the sort of huge spectrum of use app development instances, packages, and industries, it’s difficult to nail down a definitive listing of choice criteria.
Types of data analytics tools & key features
What is the gear used for massive facts analytics? Data analytics tools gear constitute a huge category, though they have a tendency to fall into some key groups.
Customer data platforms
Customer data platforms like customer relationship management platforms (CRM) seize purchaser facts that may be used to enhance strategies or promote products. However, CDPs take matters to the following level.
Core capabilities:
• 360-diploma view of the purchaser.
• Connect more than one fact source.
• Unifies purchaser facts throughout all linked structures.
• Improve concentrated on for advertising campaigns.
Business intelligence (BI) tools
Today’s business intelligence (BI) assists companies to see iOS app development and apprehend facts. According to gartner, BI gear span 3 major categories. Online analytical processing, or OLAP, permits fact discovery, ad-hoc reporting, simulation fashions, overall performance control, and different complicated evaluation abilities. There’s additionally statistics transport–which serves up insights within the shape of visualizations, reports, and dashboards. And finally, BI integration–which offers metadata control and imparting app developers surroundings to assist your method.
Core capabilities:
• Data visualization.
• Predictive modeling.
• Data mining.
• Forecasting.
Customer analytics tools
Customer analytics is designed to control the overall analytics technique from guidance to perception generation. In maximum instances, purchaser analytics systems include web development pre-built facts fashions for forecasting, propensity to buy, and numerous statistical evaluation strategies to apprehend purchaser conduct and optimize products, offerings, and reports.
Core capabilities:
• Granular segmentation.
• Customer satisfaction Insights.
• Statistical modeling.
• Acquisition, retention, & churn metrics.
Digital experience platforms
Digital experience platforms is a new kind of enterprise-grade software development designed to optimize the purchaser revel in at each touchpoint. While DXPs overlap with purchasers revel in control systems, DXPs cognizance greater on streamlining strategies, coordinating and personalizing content material to customers throughout an extensive variety of channels which include the Internet of Things (IoT), virtual assistants, VR reports, and greater.
Core capabilities:
• API-first structure.
• Multi-touchpoint control.
• Dynamic templates for automating personalization.
• Content control and transport.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
My read and summarization of the booklet on devops by mike loukides from O Reilly, great read for starters.. a good reference on automation, inreastructure as code
Tom Davenports Classic on hwo to Build Organizations of Knowledge workers, around talent Management, Information and Managerial Hygiene.. great reference for managers
Read in 2011, a very foundational book on physics, narrated in a very easy lay-man terms.. This book talks about constants, in nature and how we need to interpret and listen to these constants..
These are my book notes, great book one can buy this book on Amazon... worth a read for science buffs
In 2011 i read this wonderful book from the found of IDEO Tom Kelley, on how to manage and inculcate innovation.. this book was a precursor for the book ten faces of Innovation
A personal collection of HR concepts through training sessions attended.. highlights.. Areas like Presentations, Leadersdhip, Influencing, Interviews .. etc...
Life Biography and the philosophy of Sri Sankara, A book that i picked up at vadyar and sons Palakkad, well written introduction into the greatest Advaita philosopher Sri Sankara .. deck to be updated.. with more information later.s
UCF framework presented to a large IT service company in Mumbai in 2008.. showing my thinking then on how an organization could approach organization capability recording and building.. related to PCMMI.
Morey stettner wrote a very practical guide for managers, do surely read it.. this is my prime reference for managing my teams at work.. the presentation is a precis of that book and the key principles resident there..
Anticipatory Failure Determination <afd> is a method similar to FMEA in design, to extract and discover failures in design ad how to cope and manage these risks.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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.
1. Analytics growing as a business mandate.
Data is Growing Performance Gap Widens Capability Gap Exists..
4.4x
2.7x
2.4x
2.4x
2x
Investment in Data and
Analytics
Top Performer Bottom Performer
Sources: IBM Breakaway Now with Business
Analytics and Optimization
17%
42%
28%
10%
USE OF DATA BY BUSINESS*
75% or more 50-74%
25-49% 0-24%
++ There is a skill gap
60% executives say they “have more
information than we can effectively
use”** [IBM Report] .
McKinsey Report on Big Data estimates
50-60% gap in the supply of deep
analytical talent; equaling 140,000 to
190,000 unfilled positions.
40% growth in global data
annually
Globally 2.5 quintillion bytes of
data per day
90 % of the data in the world
today has been created in the last
two years alone.
Customer Transactions
Customer records through device
ubiquity and better data mgmt..
1
Customer Interactions
Social Unstructure, semantics..
20B events / Day – Facebook
2
Machine Interactions
Logs sensors intelligence on all
equipment
3
IBM Report Global Business Analytics
market size is pegged around $105 billion
and growing at CAGR 8%.
Shifting Priorities for
Management in Analytics..
2. Potential for applying Analytics to Business
Based on areas explored with verticals.. During BPVM
ThemesFinance &
Accounting
GRC
CRM
Service&
Warranty
Vertical
Solutions
Worldwide
financial services
OpRisk and GRC
technology market
will grow to $2
billion by 2013 at a
compound annual
growth rate of
6.5%.
The global
financial data
analytics market
size has been
potentially
estimated at $5
billion
The global
warranty
management
technology market
will represent
more than $1.1bn
in 2012, compared
to $715m in 2007
Worldwide CRM Applications Market
Forecast to Reach $18.2 Billion in
2011, Up 11% from 2010
In 10 years,
leveraging big data
in the health
industry could
capture $300
billion annually.
Potential increase
in retailers’ OM
from big data
could be 60%
High
%-age of spend directed
towards Analytics
Sources:
1 - Prithvijit Roy: New financial analytics hub;
2 - Chartis Research;
3 – IDC; 4 – Datamonitor;
5 - McKinsey BigData report,
1
2 3 4
5
Low
4. Value Chain of Analytics in Business.
CRITICAL
BUSINESS
KPIs
DATA
MANAGEMEN
T
PROCESS
CHANGES
Strategic
Themes
Volume,
Variety,
Velocity
Actions from
Insights /
Foresights
Business Analytics
VISUALIZATIO
N
Real time / In
Process
ANALYTICS
APPLICATION
S
Insights &
Foresights
5. Analytics Value to Business
Business
outcome
Operations
Transformation
Insights Data
•Customer Insight
•Digital Marketing
•Pricing / Risk
•Product Design
•Service / Operations
•BI / Dashboards
•Manual Operations
•Embedded Analytics
•CEP / Rules Engines
•RT Integration
•Analysis / Methods
•Prediction / Data Mining
•Machine Learning
•Sample vs Large Data
•Parameterized and NON
•Data Sources { External,
Unstructured }
•Data Integration {ETL}
•Data Lineage {Metadata}
•Data Preparation {Index,
Search}
•Customer Segmentation,
Behavior based models in
all industry
•Price Sensitivity analysis
•NPD / Molecule research
in Pharma
•Risk in BFSI
•Driving Digital Initiatives
like Mobile
•Triaging / Routing in
Contact centers
•Running a Analytics KPO
that provides insights for
Operations
•Methods like
Segmentation, Regression
based scoring,
• Sensitivity Scenarios ,
What-if
•Text and media mining
capabilities [ PCA ]
•Semantic Search
•70% of the effort is spelt
out in Data
•External sources, public
and paid..
•Text, media processing /
Index
6.
7.
8. Analytics Services Maturity Model
ALIGNED INTEGRATED OPTIMIZINGFRAGMENTED
DATAANALYTICSVISUALIZATIONPROCESS[ACTIONS]
SCALE / STRUCTURE
SOURCE / RETRIEVE
CONFIG - CONTROL
INTERACTION
ALGORITHM
MODELING
DESIGN
EXECUTE
MANAGE
PRESENTATION
STRUCTURE
Simple 2-Dimensional Graphs and
reports including Types of Visuals
supported?
Static simple play out
Simple structure, numeric [ cardinal]
and non-numeric- [ Ordinal]
Internal Local Files, federated
Ad-hoc Customer opportunity
Operational Changes >
Basic Functions and statistics
User Configuration, Data Security
Structured Data with metadata
support,
Integrated data sets through DB-
DWH, SQL based retrieve
Single Iteration playout
Computational Flows
Process Maps, Kpi- Metrics
Breakdowns,
Manual Process Change / Actions
Tactical Changes – re-structure to
Business operations, processes..
Linear Functions, Regression,
Statistics,
Strategy Changes - New services
models, synthesis of business value
Integrated Partner Actions,
Automation into systems,
scenario analysis, what -if analysis,
Complex Statistics [econometrics] ,
Numerical Method, Clustering
Analysis,
System Generation-Automation ,
visual re-formation,
Compliance and traceability effort in
adding new data sources
external connectors – API,
Composite Visuals, infographics
Unstructured text, Data Scale – Size
and time
Value Chain Analysis , Benchmark
Data
New Revenue Models
Sense and response mechanisms,
Simulation, optimization,
Text & Analytics, Neural Networks,
fractals,
Actions integration - external
systems.
Storyboards, Virtual Reality
late binding – auto discovery of
structure
Access to non standard data, late
structure binding
Real time search
Data as Media like Voice, Image and
Video Bigdata Management
pivot based interaction – User self
service
Maps, Multi-dimensional Graphs,
9. How are Businesses acquiring Analytics
Inhouse /
Captive
Solution
Utilities
Services /
Resources
Platforms /
Tools
1. A Typical Bank would have a 1Bn USD budget
2. 80% spend inhouse and in Captive
3. 1200 Person = 600Mn $ Value / 100 Mn $ Cost
4. Slow, lethargy, internal Constraints, IPR
1. Small Boutique companies getting seeded
2. Focusing on either large platforms [ splunk ] or a
very specific Business use Case [ Mydrive ]
3. Scale issues, pricing,
1. Large resource houses, with 80% $ from staff Aug
2. Fragmented delivery, water fall, change is a
challenge , Utilisation is key , security & leakage
3. Can Scale, some can partner,
1. Best complement to Inhouse / Captive
2. Developing the foundations for the next gen,
3. Focused more on tech rather than business
4. Partner to all above entities,
10. Value Proposition for the Data Science Organization
Building &
Maintaining a Core
Data Platform for
Analytics: that
includes setting up of
specialized data
marts (for pricing,
reserving, etc.),
identifying internal
and external data
sources, building
connectors,
integrating with
internal core
insurance systems
and the like.
Assisting in Effort Intensive, Repetitive Non-Core
Analytical Activities that allow the client’s core
analytical team to concentrate on modeling thus
increasing core analytical bandwidth. Some
activities that vendor could take over include:
Data Cleaning
Data Aggregation and Transformation
Creating Transformed Variables
Assisting in creating transformed variables
Model Validation
Checking model accuracy
Recalibrating models and reporting results
Integration of Analytics with
Business:
Reporting Services
Integration of Results into
Core Systems
Business Process Integration
Building “Analytics as a
Service” Platform
Flexibility and Cost
Optimization with “Lab
0n Hire” Service Model
Trained Data Scientists
Onsite-Offshore model
for cost optimization
Licensing and Tool Costs
spread across multiple
projects
Multiple pricing options
including utility-based
models
1
2
3 4
11. Delivering Analytics Value to Business
Business
outcome
Operations
Transformation
Insights Data
SolutionsservicesToolsPlatforms
300 400 7000
wipro
Other players CTS, TCS, Big 4, musigma
TeraData
Pivotal
Opera
Cloudera
Tableau
Clikview
RevoR
Mydrive InfoChimp
70 1200 500Bank captive
12. Typical Analytics Practice
Strategic Eco-system Alliances
1051
Analytics [ 140 – 60 USD ]
BI [ 100 - 40 USD ]
Data / Integration [ 100 – 30 USD]
1. 80% of the business is still Staff
Augmentation
2. 80% of the business in BI / MI and
low end data services..
3. Large players like Wipro / TCS /
MuSigma in the range of 5000-
10000 resources
4. Lot of SME consulting Smaller
players
5. Clients are slower than the vendor..
1. Staff Augmentation in various Skill Areas
2. Partnering and COE development for clients
3. Project based Delivery – Agile Waterfall
4. Embedded Analytics in Operations and other initiatives
like Digital, mobile etc..
5. Service Transformational Analytics – CTS
6. Very weak in industry / Business domain
13. Industry Trend Past and Future
• Rapid directionless ops growth –
has helped ISV [+30% CAGR ]
• Bringing structured data together
• Now looking for Show and Tell + 0
consulting + More Action
• Shifting Operations to Offshore –
Captives
• Partnerships, COE, Investments,
Utilities = Value Add
• BI Sophistication has kept managers
in charm
• Integrated solutions with Digital
Initiatives
• Large Data Initiatives – Lakes,
Metadata, External Data
• IOT / more sensors, new data
• Unstructured Data, Media and
therefore Big Data
• Shift from Model to Compute
• Specific Business Use Cases
• Shift from Management to
Operations and thereby Customer
• Privacy and Security will be a big
issue
• More utilities and Plug-n-Play
14. What to look for..
• Deep integration with a Business
outcome [ MyDrive]
• Show and Tell / Productized
services
• Eco System Partnerships
• Non-Linear Scale in the Business
Model
• Easy to Consume, Utility, Pricing
• Ability to Partner / Co-innovate
• Future Proofing customers.
• Agile Delivery Models
• Charging and Collection Model
[RDC]
• Application potential across the
Economy [ MyDrive]
• Time to deploy and transform [
Splunk ]
Business Model Factors
15. Solution Capability Development
Business Value Modeling.
Analytics Program Model..
Business Value and thereby Performance Hotspots drive solutions and messages
Sales &
Marketing
Member
Mgmt & UW
Provider
Mgmt
Claims
Mgmt
Customer
Service
Medical
Mgmt
Revenue - GTM
Business Case
Account Intel
Pitch /
Proposal
Partnership /
POC
Events / ABM
Engagements
Quote
Generation
Broker
Mgmt
Campaig
n Mgmt
Market
Research
Member
Retention
1. Brand Perception / Perf
Ratio
2. Influence Ratio
3. Number of leads
4. Cost per lead
5. Medium Conversion Rate
6. Avg Premium Val
7. Days visit to purchase
8. Task Completion Rate SOLUTION
CATALOG
KEY
OUTCOMES
Key
Resources
Partnership Algorithm
Training Research LAB/ COE
Understand Business Landscape:
What value is business after? Key pain
points in decision making / operations
Leverage Internal Capability:
No duplication of work already done /
capability already in existence
In Sight of the Customer:
Develop capability through the
customer, interface, POC / Pilots
Develop Ecosystem for delivery:
Relationships with established &
emergent OEM who will drive the
market
Time Bound:
Ensure outcomes with time frame. 3
months to customer and 6 months to
pilot
Develop Systemic Solutions:
Consulting to understand customer,
quick entry, low change and capital….
1
2
3
4
5
6Data
Process
Actions
Analytics
Visualization
Capability Framework
1
2
3
Key principles
Program Status
16. Business Themes and Analytics COE
Marketing RoI & Growth analytics
Customer acquisition analytics
Customer retention analytics
Social media driven analytics
Customer/Employee fraud & risk
Competitive intelligence analytics
Supply chain analytics
MFG process quality & compliance
Early warning analytics
Asset Perf. Maint. & warranty
Network analytics
Service Problem Analysis
Service Logistic & Resource Alloc.
Governance, Risk & compliance
Integrated financial perf. - EPM
Store operations Analytics
Merchandising & Pricing analytics
Claims analytics
Pre-Trade Post Trade Analytics
Drug discovery analytics
Post market analytics (Pharma)
Care & Safety analytics
Care analytics
Member Retention Analytics
Smart meter analytics
Technology
Business Automation Modeling
Data
Analysis
Visuals
Process
People
Methods
Tools
Vertical
Themes
Customer
Lifecycle
Service &
Warranty
GRC
EPM/WIPM
• Product Mgrs [10]
• Clustered Solution
Themes + verticals
• Teams for Verticals
program mgmt
• Modelers &
Technologist report
in.
• Business Consulting
• Innovation &
Transformation
Client Pitch /
Engagement
• Analytics Program
Management
• Long term look at
business Automation
solutions
• Modelers
• Cluster Solution
Themes
• Exploring Analysis Tools
• Develop Models/Methods
• # Of experiments
• Play with data
• Information Technologist
• Cluster 1
• All Data Gather &
Aggregation technologies
• Solution Warranty / Scale
• Speed, Variety – API
• # Of experiments
• Manage COEEnv.
RCTG, HLS, E&U,
Insurance, Securities
Common + special
aspects.. 5PDM,
expanded slowly.
Telecom, RCTG, E&U,
Banking, Insurance
2 PDM
1 BFSI, 1 OTH
MFG, E&U, Telecom,
1 PDM ALL
BFSI
1PDM ALL
All verticals, close collab
with WCS
17. Systematic Modeling Approach to Persistency
Propensity
Premium
Communication Strategy
Customer Segments
Act
To neutralize
the intent
Collect
Business need
and Data
Data Integration
Demographics for
Agency Information
Product Information
Pscyhographic History
Additional Sources of Data.
Optimize Data
Data Analysis +
Imputation
Bivariate Variable
Business Objectives
Major Risks Affecting Business
Customer Segments Scope
What’s Communication Strategy
Predict
The potential
customers
Analytical Model
Monitor + Feedback
Monitoring + Reports
Input feedback from operations
to further fine tune the model.
18. The Generic Analytical Modeling Process
DATA
COLLECTION
Business Problem
Definition
BUSINESS
PROBLEM
DEFINITION
DATA PREPARATION MODEL
DEVELOPMENT
MODEL DEPLOYMENT
&
MAINTENANCE
Business Problem
Statement
Collect & Analyze
Business
Requirements
Define Goals And
Objectives
FEEDBACK
Define Data
Requirements
Identify Data
Sources
Unstructured,
Structured, Internal
& External
Data Cleansing
Data Aggregation
Derived Variables
Model Selection
Build Connectors &
Data Marts
Data
Transformation
Variable Selection
Modeling Alternatives
Model Building
Model Training
Model Evaluation
Pilot Implementation
Model Validation
Recalibration
Monitoring
Business Process
Integration
Business Processes
& Systems
Knowledge
Data Modeling &
Business
Data Modeling
Knowledge
Intensive
Core: Business
Knowledge Intensive
Analytical Modeling
and Business
Knowledge
10-20% of Total
Effort
20-30% of Total
Effort
25-30 % of Total
Effort
5-10% of Total Effort 20-30% of Total Effort
PHASESKEYACTIVITIES:CORE&NON-CORE
KNOWLE
DGE
COST
19. Reporting & BP IntegrationAnalytical Support Team
Data Integration
MODELINGINFRASTRUCTURE
Internal Data [AIG]
Enterprise
Doc Manager
Loss
Notification
System
Claim
Admin
System
Policy
Admin
System
GL/Paymen
t
Engine
Data Preparation
Dashboards
& Reports
ANALYSISTEAM
External Data
Credit Records
Social Networks
Others
Data Marts,
ETL
Mapping,
Connectors
Analytics - Structural View
Core Analytical Modeling Team
Generic Analytical Models
Segmentation
Regression
Predictive Analytics
Core Insurance Analytical Models
Capital Adequacy Models
Pricing & Rating Models
Reserving Models
Risk Transfer Mechanisms
Modeling Foundation Data Governance
Specialized Data Marts Insurance Models & Standards Data Mining Tools
Modeling Repositories & Practices
Fraud Models
OUTCOME
20. Interventions through Data & Analytics
Data
Data Quality &
Cleansing
Pricing & Rating
Models
Dashboards:
Events &
Triggers
External Data
Data Integration
Services
Visualization
System
Integration - AIG
Reporting
Services
Reserving
Models
KPO / BPO
Services
Monitor model
performance
Modeling Business Services
Internal Data
Specialized
Research
Services
Model Validation
Unstructured
Data
Data - Readiness
Assessments
Actuarial Data Marts: Creation and
maintenance
Capital Adequacy
Models
Risk Transfer
Mechanisms
Model
Maintenance
Services for
Market Research
21. Vishwanath Ramdas
Head Analytics FCC Compliance , Large MNC Bank
8 years in the industry with 17 Y experience in Business Transformation.