Learn about the CDO Advisors Primed Analytic Process. This is our data science methodology that was created after years of practical experience. Learn more at www.primed-ap.com. This presentation is from the Global Predictive Analytic Conference April 3rd, 2018.
Underpinning all data management initiatives is the fundamental need to get data quality right. Poor data quality can be costly, impact customer service, lead to errors in risk management and regulatory reporting, and more. So, how can you improve data quality? How can you use rules, standardisation and technology to make improvements? And how is ‘right’ data quality measured?
Listen to the webinar to find out about:
Requirements for data quality
Challenges of achieving data quality
Data quality metrics
Supporting tools and technology
Operational and business gains
This webinar discussed the purpose of data analytics and how it can be a light in the darkness for your organization to make better decisions for the future. The webinar covered the purpose of data analysis and its definition, the fundamental steps to take to perform data analysis to problem solve, and closed with next steps that attendees can take to further develop data analysis and business intelligence within their organizations.
During this webinar, attendees learned about the following:
- How data analytics functions to help your organization improve.
- The process for using data analytics to solve problems.
- Next steps to take to build data analysis within your organization.
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The Data Driven Enterprise - Roadmap to Big Data & Analytics Success
Presentation used at the series of Breakfast seminar around Australia hosted by Lenovo/Intel/SAP/EY
Enterprise Analytics Strategy: Taking Business Analytics to the UserRubén Mancha
The last mile of the business analytics transformation--taking business analytics to the user--requires the alignment of goals, data, and models with business processes, technology and key performance indicators.
Underpinning all data management initiatives is the fundamental need to get data quality right. Poor data quality can be costly, impact customer service, lead to errors in risk management and regulatory reporting, and more. So, how can you improve data quality? How can you use rules, standardisation and technology to make improvements? And how is ‘right’ data quality measured?
Listen to the webinar to find out about:
Requirements for data quality
Challenges of achieving data quality
Data quality metrics
Supporting tools and technology
Operational and business gains
This webinar discussed the purpose of data analytics and how it can be a light in the darkness for your organization to make better decisions for the future. The webinar covered the purpose of data analysis and its definition, the fundamental steps to take to perform data analysis to problem solve, and closed with next steps that attendees can take to further develop data analysis and business intelligence within their organizations.
During this webinar, attendees learned about the following:
- How data analytics functions to help your organization improve.
- The process for using data analytics to solve problems.
- Next steps to take to build data analysis within your organization.
Analytics with Descriptive, Predictive and Prescriptive Techniquesleadershipsoil
How the analytics industry has been affected by descriptive, predictive and prescriptive techniques and how these traditional analytical techniques are going to transform the industry in future
The Data Driven Enterprise - Roadmap to Big Data & Analytics SuccessBigInsights
The Data Driven Enterprise - Roadmap to Big Data & Analytics Success
Presentation used at the series of Breakfast seminar around Australia hosted by Lenovo/Intel/SAP/EY
Enterprise Analytics Strategy: Taking Business Analytics to the UserRubén Mancha
The last mile of the business analytics transformation--taking business analytics to the user--requires the alignment of goals, data, and models with business processes, technology and key performance indicators.
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
The volume and velocity of available data brings with it a huge amount of new opportunities for marketers. However, without the analytics know-how to avail of this data, these are opportunities that are often missed. Moreover, the variety of different data sources and analytics platforms only add to this complexity.
This presentation covers:
- How to define and communicate an analytics framework
- How to set up analytics dashboards for a range of stakeholders
- The people and skills you need for an optimal analytics team
- Practical tips for improving your campaign measurement
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
Data Driven Strategy Analytics Technology Approach CorporateSlideTeam
This complete deck can be used to present to your team. It has PPT slides on various topics highlighting all the core areas of your business needs. This complete deck focuses on Data Driven Strategy Analytics Technology Approach Corporate and has professionally designed templates with suitable visuals and appropriate content. This deck consists of total of thirteen slides. All the slides are completely customizable for your convenience. You can change the colour, text and font size of these templates. You can add or delete the content if needed. Get access to this professionally designed complete presentation by clicking the download button below. https://bit.ly/3yjusdQ
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.
The demand for data insights to drive decisions is higher today than ever before. This isn't just because volumes of accessible data are growing, but also because people are more data literate and accustomed to engaging information experiences from consumer apps like LinkedIn, Google Maps, & Yelp.
This same thirst for intelligence is probably apparent in your user base, whether you realize it or not - and taking the time to invest in a data & analytics strategy for your product can yield significant customer & business benefits over time.
About the Speakers:
Michelle Bradbury,Director of Product Management, Pentaho
Michelle has over 18 years of experience in technology product & project management. She enjoys collaboratively creating & delivering highly compelling products and has held roles at organizations including Microsoft, Fujitsu, & CapitalOne. Michelle's areas of expertise include database and data warehouse architecture and development, project and budget management, as well as process definition and implementation for group cohesiveness.
Ben Hopkins, Product Marketing Manager, Pentaho
Ben is focused on embedded analytics & OEM partnerships. He has also held product marketing roles at Marketo and Salesforce.com. He holds an MBA from the U.C. Berkeley Haas School of Business as well as a BA in Economics from Harvard College.
Pentaho is delivering the future of analytics with a comprehensive platform for data integration & business intelligence. Learn more at www.pentaho.com.
Upcoming Events
Would you like to lead innovation efforts within your company? Attend upcoming product innovation courses. Visit: http://bit.ly/CILCourse
Looking for a coach to accelerate your product marketing & management career?
Set up a free initial 30-minute appointment for more information: http://bit.ly/1gBFdaD.
Want To Certify Your Team?
If you have a product team of 10 or more that you want to certify, contact AIPMM at certification@aipmm.com.
About AIPMM
The AIPMM is the trusted authority in product management. It is where product professionals go for answers. With members in over 75 countries, it is the worldwide certifying body of product team professionals.
It is the world's largest professional organization of product managers, brand managers, product marketing managers and other product team professionals who are responsible for guiding their organizations, or clients, through a constantly changing business landscape.
AIPMM's certification programs are internationally recognized because they allow product professionals to demonstrate their expertise and provide corporate members an assurance that their product management and marketing teams are operating at a high competency level.
Visit http://www.aipmm.com.
Call For Speakers: http://bit.ly/1b006vm
Subscribe: http://www.aipmm.com/subscribe
Articles: http://www.aipmm.com/html/newsletter/article.ph
Membership: http://www.aipmm.com/join.php
This presentation was from a joint BCS/DAMA event on 20/6/13 discussing different aspects of assessing data quality and the role that data quality dimensions can play.
This presentation by James Phare, Data to Value and Mark Hodson, X88 looks at the role that data profiling tools can play in assessing data quality.
The video for this presentation is at https://www.youtube.com/watch?v=XbSNw9gqoEo
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
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.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Your smarter data analytics strategy - Social Media Strategies Summit (SMSS) ...Clark Boyd
The volume and velocity of available data brings with it a huge amount of new opportunities for marketers. However, without the analytics know-how to avail of this data, these are opportunities that are often missed. Moreover, the variety of different data sources and analytics platforms only add to this complexity.
This presentation covers:
- How to define and communicate an analytics framework
- How to set up analytics dashboards for a range of stakeholders
- The people and skills you need for an optimal analytics team
- Practical tips for improving your campaign measurement
Are you an inquisitive person?
Do you have the enthusiasm and willingness to learn new topics?
Do you want to be a Data Scientist and make pots of money?
Do you like to know the future job prospects for Data Science?
Download my recent (12th January, 2021) presentation titled “Analytics – Future Trend and Job Prospects”.
Data Driven Strategy Analytics Technology Approach CorporateSlideTeam
This complete deck can be used to present to your team. It has PPT slides on various topics highlighting all the core areas of your business needs. This complete deck focuses on Data Driven Strategy Analytics Technology Approach Corporate and has professionally designed templates with suitable visuals and appropriate content. This deck consists of total of thirteen slides. All the slides are completely customizable for your convenience. You can change the colour, text and font size of these templates. You can add or delete the content if needed. Get access to this professionally designed complete presentation by clicking the download button below. https://bit.ly/3yjusdQ
Foundational Strategies for Trust in Big Data Part 2: Understanding Your DataPrecisely
Teams working on new initiatives whether for customer engagement, advanced analytics, or regulatory and compliance requirements need a broad range of data sources for the highest quality and most trusted results. Yet the sheer volume of data delivered coupled with the range of data sources including those from external 3rd parties increasingly precludes trust, confidence, and even understanding of the data and how or whether it can be used to make effective data-driven business decisions.
The second part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Trillium Discovery for Big Data with its natively distributed execution for data profiling supports a foundation of data quality by enabling business analysts to gain rapid insight into data delivered to the data lake without technical expertise.
Foundational Strategies for Trust in Big Data Part 1: Getting Data to the Pla...Precisely
Teams working on new business initiatives, whether for enhancing customer engagement, creating new value, or addressing compliance considerations, know that a successful strategy starts with the synchronization of operational and reporting data from across the organization into a centralized repository for use in advanced analytics and other projects. However, the range and complexity of data sources as well as the lack of specialized skills needed to extract data from critical legacy systems often causes inefficiencies and gaps in the data being used by the business.
The first part of our webcast series on Foundation Strategies for Trust in Big Data provides insight into how Syncsort Connect with its design once, deploy anywhere approach supports a repeatable pattern for data integration by enabling enterprise architects and developers to ensure data from ALL enterprise data sources– from mainframe to cloud – is available in the downstream data lakes for use in these key business initiatives.
Systems of Insights: BI Trends and the Smart Tools of the FutureYellowfin BI
Business intelligence (BI) is at the top of the enterprise agendas as they transform from data driven to insights driven business models. The opportunity created a large, fragmented market of BI vendors, and the older market segmentation models (technology centric vs. user centric, reporting vs. data visualization, on-premise vs. cloud) no longer work. Then how do you select the right BI platform from dozens of contenders who often pitch a very similar proposition? On this webinar you will learn about the:
- Role of BI in the insights-driven business model
- Commoditization of some BI platform features
- Emergence of a new set of key BI platform differentiators
- Demo of 2-3 new features of Yellowfin 7.4
Yellowfin, together with Forrester business intelligence expert Boris Evelson, presented Systems of Insight, as a webinar on October 31, 2017.
For more information visit http://www.yellowfinbi.com
Enabling data scientists within an enterprise requires a well-thought out approach from an organization, technology, and business results perspective. In this talk, Tim and Hussain will share common pitfalls to data science enablement in the enterprise and provide their recommendations to avoid them. Taking an example, actionable use case from the financial services industry, they will focus on how Anaconda plays a pivotal role in setting up big data infrastructure, integrating data science experimentation and production environments, and deploying insights to production. Along the way, they will highlight opportunities for leveraging open source and unleashing data science teams while meeting regulatory and compliance challenges.
The demand for data insights to drive decisions is higher today than ever before. This isn't just because volumes of accessible data are growing, but also because people are more data literate and accustomed to engaging information experiences from consumer apps like LinkedIn, Google Maps, & Yelp.
This same thirst for intelligence is probably apparent in your user base, whether you realize it or not - and taking the time to invest in a data & analytics strategy for your product can yield significant customer & business benefits over time.
About the Speakers:
Michelle Bradbury,Director of Product Management, Pentaho
Michelle has over 18 years of experience in technology product & project management. She enjoys collaboratively creating & delivering highly compelling products and has held roles at organizations including Microsoft, Fujitsu, & CapitalOne. Michelle's areas of expertise include database and data warehouse architecture and development, project and budget management, as well as process definition and implementation for group cohesiveness.
Ben Hopkins, Product Marketing Manager, Pentaho
Ben is focused on embedded analytics & OEM partnerships. He has also held product marketing roles at Marketo and Salesforce.com. He holds an MBA from the U.C. Berkeley Haas School of Business as well as a BA in Economics from Harvard College.
Pentaho is delivering the future of analytics with a comprehensive platform for data integration & business intelligence. Learn more at www.pentaho.com.
Upcoming Events
Would you like to lead innovation efforts within your company? Attend upcoming product innovation courses. Visit: http://bit.ly/CILCourse
Looking for a coach to accelerate your product marketing & management career?
Set up a free initial 30-minute appointment for more information: http://bit.ly/1gBFdaD.
Want To Certify Your Team?
If you have a product team of 10 or more that you want to certify, contact AIPMM at certification@aipmm.com.
About AIPMM
The AIPMM is the trusted authority in product management. It is where product professionals go for answers. With members in over 75 countries, it is the worldwide certifying body of product team professionals.
It is the world's largest professional organization of product managers, brand managers, product marketing managers and other product team professionals who are responsible for guiding their organizations, or clients, through a constantly changing business landscape.
AIPMM's certification programs are internationally recognized because they allow product professionals to demonstrate their expertise and provide corporate members an assurance that their product management and marketing teams are operating at a high competency level.
Visit http://www.aipmm.com.
Call For Speakers: http://bit.ly/1b006vm
Subscribe: http://www.aipmm.com/subscribe
Articles: http://www.aipmm.com/html/newsletter/article.ph
Membership: http://www.aipmm.com/join.php
This presentation was from a joint BCS/DAMA event on 20/6/13 discussing different aspects of assessing data quality and the role that data quality dimensions can play.
This presentation by James Phare, Data to Value and Mark Hodson, X88 looks at the role that data profiling tools can play in assessing data quality.
The video for this presentation is at https://www.youtube.com/watch?v=XbSNw9gqoEo
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
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.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Predictive analytics are increasingly a must-have competitive tool. A well-defined workflow and effective decision modeling approach ensures that the right predictive analytic models get built and deployed.
The New Self-Service Analytics - Going Beyond the ToolsKatherine Gabriel
In today’s business climate, using data to make quick decisions is a common ask across organizations. To fulfill such asks business users want more, faster, and better access to data and analytic tools. IT wants to balance this need for speed with the responsibility to protect the data assets from security, privacy, and quality risks. A common solution to this scenario is self-service BI or self-service analytics. Chances are you are already using self-service BI in some way, shape, or form or have heard a pitch from an analytic tool vendor!
Self-service BI has been around for several decades and yet business users keep asking for more and more. Has self-service BI failed to deliver on its promise? Is it time to revisit what self-service really means? How can business and IT work together to achieve better decision-making outcomes for their organization?
We cover:
• How to demystify what self-service analytics means
• New trends driving the self-service analytics evolution
• Best practices and lessons learned from real-life examples
• Recommendations for making progress within your organization
Advance your self-service journey.
At ING Bank, machine learning models are a key factor in making relevant engagements with our customers, empowering them to stay a step ahead in life and in business. In our efforts to make the model building process more rapid, compliant, validated and accessible to roles other than data scientists (such as data analysts or customer journey experts), we have structured it for an easy creation of propensity models.
In this talk, I will present this structure, focusing on pipelining data science models in Apache Spark. In particular, I will show how we use Apache Sqoop & Ranger to comply with GDPR, build a data science workflow on top of python and Jupyter, extend the SparkML libraries on PySpark to create custom standardizers and cross-validators, and show an in-house developed monitoring tool built on top of Elasticsearch for model evaluation.
Finally, I will describe the type of engagement analysts and customer journey experts have with the result set of the models created, and how we refine our dashboards (in IBM Cognos) accordingly.
Speaker: Dor Kedem, Lead Data Scientist
ING Bank
Gain a Holistic View of your Customer's JourneyPlatfora
Today, companies are capturing information about customers at every touchpoint, but the reality is that most companies are working with siloed marketing data because they’re using disparate tools to track online, offline, web, social, mobile, and advertising data.
In this presentation, Rod Fontecilla, VP of Application Modernization at Unisys, explains how his team uses Platfora to analyze, interact and understand data to drive customer success at Unisys.
Rod will highlight three specific Unisys use cases of Platfora, one of which involved a timely text survey sentiment analysis that produced insights enabling a course correction in favor of improved customer satisfaction.
Manufacturing, a slow-adopter of Analytics, is now catching up in leaps and bounds. Across all business domains, applying analytics is providing answers to the most critical questions of the business.With exponential expansion of data, data driven insights have become a strategic necessity.
This booklet explores a few use cases of Big Data for manufacturing and how it can be leveraged.
For more info visit: https://www.teamcomputers.com/businessanalytics/Manufacturing/Booklet-Manufacturing-Digital.pdf
ADV Slides: What the Aspiring or New Data Scientist Needs to Know About the E...DATAVERSITY
Many data scientists are well grounded in creating accomplishment in the enterprise, but many come from outside – from academia, from PhD programs and research. They have the necessary technical skills, but it doesn’t count until their product gets to production and in use. The speaker recently helped a struggling data scientist understand his organization and how to create success in it. That turned into this presentation, because many new data scientists struggle with the complexities of an enterprise.
Minggu-02 Big Data Business Model Maturity Index.pdfazkamuhammad11
Dalam rangka menyambur bisnis big data yang bisa dibelikan sebagai ambang penerapan sinergi yang memungkinkan big data berkecimpung pada brand clothing supermakepeace akan memberikan kunci sukses yang sangat ringan kepada rekan rekan yang mau terlibat terhadap brand supermakepeace ini, dengan itu kita bergerak secara konsiten=n denga brand cothing supermakepeace akan membuat kita masing2 berkecimpung di mirasa dan sekarang saya mengerjakan yang seharusnya tidak saya kerjakan karena dengan ini hidup terasa sangat hanya dengan mengantuk dengqan mengetik agar menemukan sedikit fb ads yang melanda kasih sayag dan dengan itu memvuat sayang;; dari pemerintah menjadi perintah alah siah botaaytn aaada acara berlingan iasng mata yang menebark
how i managed to Develop a Analytics story for services about 4 years back. Contains
Maturity Model, Business Potential, Services Structures Areas that analytics can be applied to
20150108 create time stamp
Ironside's VP of Strategy & Innovation, Greg Bonnette, delivered a presentation on "How to Build a Winning Strategy for Data & Analytics" to provide a framework for data-driven decision making.
Automating and Orchestrating Processes and Decisions Across the EnterpriseDenis Gagné
PRESENTED BY
Carl Lehmann – Principal Analyst, 451 Research
Denis Gagne – CEO and CTO, Trisotech
DESCRIPTION
When business processes must execute complex decisions across the enterprise, most process automation platforms and rules management engines fall short. While competent in rules-based process modeling and automation they’re unable to model, automate and orchestrate complex decision-making processes, for example, in areas such as in clinical contexts, insurance risk management, and structured financial services, among others.
451 Research is tracking an emerging class of automation and orchestration technology that is becoming competent in both.
Join us to explore the industry trends driving the need for joint process and decision automation.
The benefits derived from a unified approach to both.
The technical apparatus needed to automate and orchestrate process and decision models.
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.
Show drafts
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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.
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).
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/
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
4. What is Predictive Analytics?
Predictive Analytics is used to improve the performance of your business by
focusing on:
• Improving Revenue
• Decreasing Expenses
• Improving Service for Internal or External Customers
Predictive Analytics uncovers patterns and relationships in data by leveraging
historical data to apply to current information.
Predictive Analytics is forward looking and attempts to anticipate outcomes
and behaviors based on mathematical models not assumptions or gut
feeling.
copyright 2018 CDO Advisors - www.cdoadvisors.com 4
5. Predictive Analytics Framework
Preparation - Initial phase that focuses on partnering with the
business to determine what is to be
accomplished.
Investigation - Exploring the organizations data assets to see
what data is available
Modeling - Is where the data experimentation takes place
Evaluation - Reviewing the best performing models with the
business users
Deployment - Model is loaded with production data to creation
actionable insights
copyright 2018 CDO Advisors - www.cdoadvisors.com 5
6. Customer Churn Questions
• Can we predict which customers have the highest risk of churn?
• Do we have data that can be useful to understand patterns in why
customers have churned?
• Can we access and process the data?
• Is the data of sufficient quality?
• What kind of models can we build?
• How accurate are the models?
• How do we make the model’s outcome actionable?
copyright 2018 CDO Advisors - www.cdoadvisors.com 6
7. Customer Churn - Preparation
Business Users – Problem Statement
• Define the business problem that you want to better
understand
• How will you track Experimental and Control group to track the
impact of the analytics
• Establish the current Churn Rate baseline
• Define the parameters of the project
• Line of Business, Product, Segmentation
• Amount of time to spend on the problem
• Determine where model output will be used in operations
Data Scientists – Questions
• How will you use the output from the model?
• What prior analysis has been done?
• What has been done successfully to reduce churn in the past?
copyright 2018 CDO Advisors - www.cdoadvisors.com 7
8. Telco Churn Sample Data Elements
• Spouse
• Dependents
• Internet Service
• Online Security
• Tech Support
• Contract Type
• Paperless Billing
• Payment Method
• Lifetime Months Tenure
• Monthly Charges
copyright 2018 CDO Advisors - www.cdoadvisors.com 8
9. Customer Churn - Investigation
Business Users – Process Clarification
• Ensure the process for customer lifecycle is well defined
• What applications have data that would be beneficial to use?
• Are there known external data that could be used?
Data Scientists – Data and Process Questions
• What data is available?
• Leverage a variety of tools and methods to understand the
data?
• Visualization tools
• Statistical analytics
• What is the quality of the data?
• Distribution of data
• Quality of data
• Volume of data
copyright 2018 CDO Advisors - www.cdoadvisors.com 9
10. Customer Churn - Modeling
Business Users – Engagement on Modeling
• Review output of trial models as requested
• Check assumptions that are made on the data
• Ensure output will satisfy business problem
Data Scientists – Experimentation of Models
• Create and build models
• Trial and error on the output of the models
• Experimentation on different data elements
• Model accuracy
• Different algorithms
• Optimization of models
• Testing of the model
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11. Data Mining – Decision Tree (Partial)
copyright 2018 CDO Advisors - www.cdoadvisors.com 11
12. Customer Churn - Evaluation
Business Users
• Review model source data, modifications and process
• Review output of best performing models
• Ensure output can be used in business operations
Data Scientists
• Present models to the business users
• Share discovers in the data:
• What patterns were observed
• Review data chosen, modifications, quality issues
addressed
• Review how the model output will be shared with the business
• Excel output
• Written to a database
copyright 2018 CDO Advisors - www.cdoadvisors.com 12
14. Customer Churn - Deployment
Business Users – Operational Actions
• Implement changes to operational processes based on the new data
from the model
Data Scientists – Monitoring and Progress of Actions
• Monitor the progress of the control and experimental group
• Review results of the operational changes monthly with business
users
• If the model is reducing churn, expand to other areas and start the
process from the preparation stage
• Determine when to re-evaluate the model, when does the current
models accuracy no longer work
copyright 2018 CDO Advisors - www.cdoadvisors.com 14
16. Marketing Response Questions
• Can we predict which people are most likely to respond to our campaign?
• What kind of campaign are we doing? (email, mail, sms)
• Do we have data that can be useful to understand patterns prior
campaigns?
• Can we access and process the data?
• Is the data of sufficient quality?
• What kind of models can we build?
• How accurate are the models?
• How do we make the model’s outcome actionable?
copyright 2018 CDO Advisors - www.cdoadvisors.com 16
17. Marketing Response Sample Data Elements
• Age
• Gender
• Job
• Marital Status
• Education
• Housing Loan
• Personal Loan
• Automobile Ownership
• Postal Code
• Census Block
• Contact (method)
copyright 2018 CDO Advisors - www.cdoadvisors.com 17
18. Marketing Response - Flow
Preparation - Initial phase that focuses on partnering with the
business to determine what is to be accomplished.
Investigation - Exploring the organizations data assets to see what data
is available
Modeling - Is where the data experimentation takes place
Evaluation - Reviewing the best performing models with the
business users
Deployment - Model is loaded with production data to creation
actionable insights
copyright 2018 CDO Advisors - www.cdoadvisors.com 18
20. Getting started with Predictive Analytics?
• Start with the data you have
• Identify a core business problem that you want to solve
• Determine the required data elements and quality of data
• Build models….Evaluate models…test models….
• Implement the model
• Limited scope to build confidence
• A/B Testing to show actual performance of the model versus control group
• Monitor and measure the results
• How is the model performing versus normal business operations?
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21. Questions or Follow Up
Derek Wilson, CEO – CDO Advisors
dwilson@cdoadvisors.com
www.cdoadvisors.com
Office : 832-819-5744
Connect with me on LinkedIn –
https://www.linkedin.com/in/derekewilson/
copyright 2018 CDO Advisors - www.cdoadvisors.com 21
Editor's Notes
How many people are business operations?
How many people are data scientist?
PRIMED-AP
Analytic Process
Determine which segment of customers to model churn
Is it by geographic area, product line, new customers versus established.