CPG manufacturers need to understand big data and understand the value of big data. This presentation explains big data, the evolution of big data and how big data can be used.
The document discusses how CPG manufacturers can develop an effective big data strategy to gain a competitive advantage. It recommends starting with defining clear goals, such as growth, and focusing on shopper insights. It also stresses the importance of having a coherent strategy in place before embarking on big data initiatives. Additionally, it suggests that CPG companies leverage demand signal management solutions to help operationalize their big data strategies and quickly realize tangible business benefits from big data.
Slides from May 2018 St. Louis Big Data Innovations, Data Engineering, and Analytics User Group meeting. The presentation focused on Data Modeling in Hive.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
The document discusses how CPG manufacturers can develop an effective big data strategy to gain a competitive advantage. It recommends starting with defining clear goals, such as growth, and focusing on shopper insights. It also stresses the importance of having a coherent strategy in place before embarking on big data initiatives. Additionally, it suggests that CPG companies leverage demand signal management solutions to help operationalize their big data strategies and quickly realize tangible business benefits from big data.
Slides from May 2018 St. Louis Big Data Innovations, Data Engineering, and Analytics User Group meeting. The presentation focused on Data Modeling in Hive.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
The majority of successful organizations in today’s economy are data-driven, and innovative companies are looking at new ways to leverage data and information for strategic advantage. While the opportunities are vast, and the value has clearly been shown across a number of industries in using data to strategic advantage, the choices in technology can be overwhelming. From Big Data to Artificial Intelligence to Data Lakes and Warehouses, the industry is continually evolving to provide new and exciting technological solutions.
This webinar will help make sense of the various data architectures & technologies available, and how to leverage them for business value and success. A practical framework will be provided to generate “quick wins” for your organization, while at the same time building towards a longer-term sustainable architecture. Case studies will also be provided to show how successful organizations have successfully built a data strategies to support their business goals.
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
IDERA Slides: Managing Complex Data EnvironmentsDATAVERSITY
Companies are expanding their information systems beyond relational databases to incorporate big data and cloud deployments, creating hybrid configurations. Database professionals have the challenges of managing multiple data sources and running queries for analytics against diverse databases in these complex environments.
IDERA’s Lisa Waugh will discuss how to deal with the growing challenges of having data residing on different database platforms by using a single IDE.
The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.
This document provides an introduction to big data and analytics. It discusses definitions of key concepts like business intelligence, data analysis, and big data. It also provides a brief history of analytics, describing how technologies have evolved from early business intelligence systems to today's big data approaches. The document outlines some of the key components of Hadoop, including HDFS and MapReduce, and how it addresses issues like volume, variety and velocity of big data. It also discusses related technologies in the Hadoop ecosystem.
This whitepaper outlines a reference architecture for real-time data governance using DataOps principles. The architecture defines logical units including lines of business, spaces, datasets, actors and identities to enable federated governance over heterogeneous systems. It allows each line of business to be aligned with the appropriate data, applications, rules and processes. The architecture specifies roles for access management and enforcement of governance policies across business units and technical environments.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
This document summarizes a presentation on self-service data analysis, data wrangling, data munging, and how they fit together with data modeling. It discusses how these techniques allow business stakeholders and data scientists to prepare and transform data for analysis without extensive technical expertise. While these tools increase flexibility, they can also decrease governance if not used properly. The document advocates finding a balance between managed data assets and exploratory analysis to maximize insights while maintaining data quality.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
This document discusses Zurich Insurance Group's use of cloud analytics platforms and technologies. It outlines how Zurich leverages multiple data sources and tools for data exploration, integration, modeling and deployment. Key elements of their ecosystem include a data lake on Azure, various analytics tools, containerization, and DevOps processes to automate deployments and upgrades. The goal is to accelerate insights, improve agility and reduce costs through this cloud-based analytics environment.
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
The recent focus on Big Data in the data management community brings with it a paradigm shift—from the more traditional top-down, “design then build” approach to data warehousing and business intelligence, to the more bottom up, “discover and analyze” approach to analytics with Big Data. Where does data modeling fit in this new world of Big Data? Does it go away, or can it evolve to meet the emerging needs of these exciting new technologies? Join this webinar to discuss:
Big Data –A Technical & Cultural Paradigm Shift
Big Data in the Larger Information Management Landscape
Modeling & Technology Considerations
Organizational Considerations
The Role of the Data Architect in the World of Big Data
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...Cloneskills
• Objective of this demonstration is to provide enough functional and technical details about our pre-configured SAP HANA enabled predictive analytics on SAP COPA and social media data - “Big Data”
• In this demonstration we will be presenting the out of the box analytics capabilities of SAP HANA. Viewers will learn on how our pre-packaged solutions will cut-down the implementation time, and risk with low predictable cost
• An ideal Advanced Analytics solution should have the capability to extract business values from unstructured information and convert that into actionable insight. We will show how to analyze and integrate an un-structured social media data to provide valuable insight on customer behavior and sales trends. All these on our hosted solutions over an Amazon cloud infrastructure
• Our readily deployable solutions uses the new features of SAP HANA 1.0 SPS5 including the new Text Analysis engine for entity extraction (for example persons, locations, products), and "Voice of Customer" fact extraction (for example sentiments, requests, topics)
If you have a retail product that needs a sales boost or you're launching a new product - you should consider a custom designed and manufactured point of sale product display unit.
The last year has put a new lens on what speed to insights actually mean - day-old data became useless, and only in-the-moment-insights became relevant, pushing data and analytics teams to their breaking point. The results, everyone has fast forwarded in their transformation and modernization plans, and it's also made us look differently at dashboards and the type of information that we're getting the business. Join this live event and hear about the data teams ditching their dashboards to embrace modern cloud analytics.
This document provides an introduction to big data and analytics. It discusses definitions of key concepts like business intelligence, data analysis, and big data. It also provides a brief history of analytics, describing how technologies have evolved from early business intelligence systems to today's big data approaches. The document outlines some of the key components of Hadoop, including HDFS and MapReduce, and how it addresses issues like volume, variety and velocity of big data. It also discusses related technologies in the Hadoop ecosystem.
This whitepaper outlines a reference architecture for real-time data governance using DataOps principles. The architecture defines logical units including lines of business, spaces, datasets, actors and identities to enable federated governance over heterogeneous systems. It allows each line of business to be aligned with the appropriate data, applications, rules and processes. The architecture specifies roles for access management and enforcement of governance policies across business units and technical environments.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Self-Service Data Analysis, Data Wrangling, Data Munging, and Data Modeling –...DATAVERSITY
This document summarizes a presentation on self-service data analysis, data wrangling, data munging, and how they fit together with data modeling. It discusses how these techniques allow business stakeholders and data scientists to prepare and transform data for analysis without extensive technical expertise. While these tools increase flexibility, they can also decrease governance if not used properly. The document advocates finding a balance between managed data assets and exploratory analysis to maximize insights while maintaining data quality.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Using Data Platforms That Are Fit-For-PurposeDATAVERSITY
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
This document discusses Zurich Insurance Group's use of cloud analytics platforms and technologies. It outlines how Zurich leverages multiple data sources and tools for data exploration, integration, modeling and deployment. Key elements of their ecosystem include a data lake on Azure, various analytics tools, containerization, and DevOps processes to automate deployments and upgrades. The goal is to accelerate insights, improve agility and reduce costs through this cloud-based analytics environment.
Lessons in Data Modeling: Data Modeling & MDMDATAVERSITY
Master Data Management (MDM) can create a 360 view of core business assets such as Customer, Product, Vendor, and more. Data modeling is a core component of MDM in both creating the technical integration between disparate systems and, perhaps more importantly, aligning business definitions & rules.
Join this webcast to learn how to effectively apply a data model in your MDM implementation.
Data Lake Architecture – Modern Strategies & ApproachesDATAVERSITY
Data Lake or Data Swamp? By now, we’ve likely all heard the comparison. Data Lake architectures have the opportunity to provide the ability to integrate vast amounts of disparate data across the organization for strategic business analytic value. But without a proper architecture and metadata management strategy in place, a Data Lake can quickly devolve into a swamp of information that is difficult to understand. This webinar will offer practical strategies to architect and manage your Data Lake in a way that optimizes its success.
The recent focus on Big Data in the data management community brings with it a paradigm shift—from the more traditional top-down, “design then build” approach to data warehousing and business intelligence, to the more bottom up, “discover and analyze” approach to analytics with Big Data. Where does data modeling fit in this new world of Big Data? Does it go away, or can it evolve to meet the emerging needs of these exciting new technologies? Join this webinar to discuss:
Big Data –A Technical & Cultural Paradigm Shift
Big Data in the Larger Information Management Landscape
Modeling & Technology Considerations
Organizational Considerations
The Role of the Data Architect in the World of Big Data
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
ADV Slides: Comparing the Enterprise Analytic SolutionsDATAVERSITY
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
SAP HANA & HADOOP Implementation - Predictive Analytics – CPG and Retail on U...Cloneskills
• Objective of this demonstration is to provide enough functional and technical details about our pre-configured SAP HANA enabled predictive analytics on SAP COPA and social media data - “Big Data”
• In this demonstration we will be presenting the out of the box analytics capabilities of SAP HANA. Viewers will learn on how our pre-packaged solutions will cut-down the implementation time, and risk with low predictable cost
• An ideal Advanced Analytics solution should have the capability to extract business values from unstructured information and convert that into actionable insight. We will show how to analyze and integrate an un-structured social media data to provide valuable insight on customer behavior and sales trends. All these on our hosted solutions over an Amazon cloud infrastructure
• Our readily deployable solutions uses the new features of SAP HANA 1.0 SPS5 including the new Text Analysis engine for entity extraction (for example persons, locations, products), and "Voice of Customer" fact extraction (for example sentiments, requests, topics)
If you have a retail product that needs a sales boost or you're launching a new product - you should consider a custom designed and manufactured point of sale product display unit.
Creating Business Value - Use Cases in CPG/RetailBig Data Pulse
This document discusses how big data analytics can help consumer packaged goods, fast moving consumer goods, retail, and e-commerce companies. It provides examples of use cases like predictive demand forecasting, pricing optimization, and markdown optimization. One case study describes how a department store used a forecasting and optimization model to improve markdown strategies and increase margins by $90 million annually. In conclusion, analyzing large, diverse customer data in real-time can provide actionable insights to increase market share, revenue and profits.
This document provides an introduction to a course on big data analytics. It discusses the characteristics of big data, including large scale, variety of data types and formats, and fast data generation speeds. It defines big data as data that requires new techniques to manage and analyze due to its scale, diversity and complexity. The document outlines some of the key challenges in handling big data and introduces Hadoop and MapReduce as technologies for managing large datasets in a scalable way. It provides an overview of what topics will be covered in the course, including programming models for Hadoop, analytics tools, and state-of-the-art research on big data technologies and optimizations.
Spatial Processing with SAP HANA Infographic shows the different spatial data types, functions, services, and content available natively on the platform and also delivered via 3rd party mapping services. For more information please visit saphana.com/spatial
This document provides a summary of Gilt's performance for Luxury Link and outlines their merchandising strategy for Q2. It shows that Gilt drives a significant portion of traffic and sales. Data on customer demographics, behaviors, and best performing content is analyzed. Actions taken include changes to site tiles, emails, and landing pages based on learnings. The ongoing Q2 plan is to regularly audit analytics, adjust merchandising calendars, and test themes and destinations with a focus on high value customers and international members.
CPG Innovation From Ideation to Aisle: New Techniques for Staying Ahead of Co...Instantly
Eighty-five percent of new products fail. How do you beat those odds? Instantly VP of Product Innovation Justin Wheeler and Supermarket Guru Phil Lempert offer up different solutions to make sure your next new product avoids failure.
Click here for the full recording of Wheeler and Lempert during our August 6, 2015 webinar: http://bit.ly/1P7zL2c
The document discusses challenges facing retail, consumer packaged goods, travel, and logistics firms. It outlines four key challenges: unifying the business ecosystem dealing with customers, streamlining complex supply chains, ensuring operational and process efficiency, and leveraging social media intelligence. It then describes Mahindra Satyam's solutions to address these challenges, including offerings for order management, extending legacy systems, customer service, working capital reduction, location-based mobility, and using social media for business intelligence. The solutions aim to provide greater agility, operational efficiency, improved time to market, on-demand content, and location-based responses to patients.
Marketelligent provides analytic services to help Consumer Packaged Goods (CPG), Manufacturing, and Retail companies make better business decisions. Their services include optimizing marketing investments, managing product pricing and promotions, rationalizing stock keeping units, understanding markets and shopper behavior, and designing supply chains and distribution networks. They offer global analytic capabilities leveraging domain expertise to maximize sales and profits.
This document provides an overview of big data analytics, strategies, and the WSO2 big data platform. It discusses how the amount of data in the world is growing exponentially due to factors like increased data collection and the internet of things. It then summarizes the WSO2 big data platform for collecting, processing, analyzing and visualizing large datasets. Key components include the complex event processor for query processing and the business activity monitor for dashboards. The document concludes by outlining new developments and features being worked on, such as distributed complex event processing and machine learning integration.
CPG Companies: Evolving Your Analytics-driven Organizationsaccenture
Accenture surveyed 90 large, global consumer packaged goods companies and found three important dimensions toward building an analytics-driven organization.
Read our other analytics research on accenture.com: http://www.accenture.com/CPGanalytics
The document discusses the use of procurement analytics. It begins by explaining what procurement analytics is and why organizations should use it. Analytics can increase demand forecasting accuracy and contract negotiation power. The document then discusses how analytics can be applied in areas like vendor evaluation, spend analysis, and demand forecasting. It also outlines challenges to implementation and provides recommendations for next steps like gaining leadership support, collaborating cross-functionally, developing skills, and integrating systems.
Big Data in Retail - Examples in ActionDavid Pittman
This use case looks at how savvy retailers can use "big data" - combining data from web browsing patterns, social media, industry forecasts, existing customer records, etc. - to predict trends, prepare for demand, pinpoint customers, optimize pricing and promotions, and monitor real-time analytics and results. For more information, visit http://www.IBMbigdatahub.com
Follow us on Twitter.com/IBMbigdata
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Understanding the DSR Market looks at the differences between a team and enterprise solution for handling multiple data sources in the consumer goods industry.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
Hadoop 2.0: YARN to Further Optimize Data ProcessingHortonworks
Data is exponentially increasing in both types and volumes, creating opportunities for businesses. Watch this video and learn from three Big Data experts: John Kreisa, VP Strategic Marketing at Hortonworks, Imad Birouty, Director of Technical Product Marketing at Teradata and John Haddad, Senior Director of Product Marketing at Informatica.
Multiple systems are needed to exploit the variety and volume of data sources, including a flexible data repository. Learn more about:
- Apache Hadoop 2 and YARN
- Data Lakes
- Intelligent data management layers needed to manage metadata and usage patterns as well as track consumption across these data platforms.
Big data analytics provides various advantages like better decision making and preventing fraudulent activities. The document discusses introduction to big data analytics including what is big data, evolution of big data, types of data, characteristics of big data, applications of big data, distributed file systems, and NoSQL databases. NoSQL databases are useful for big data as they can scale horizontally and support unstructured data from sources like social media.
Architecting for Big Data: Trends, Tips, and Deployment OptionsCaserta
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
The total data industry is growing rapidly, projected to increase from $7.3 billion in 2011 to over $50 billion by 2017. There are several drivers fueling this growth, including the digitization of machine data and increasing consumer data. The total data landscape can be broken down into key segments - operational databases, analytic databases, reporting/analytics, data management, performance management, event processing, distributed data cache, search, and Hadoop. Each segment faces challenges to growth but also opportunities through innovations that improve integration, automation, cloud services, and data preparation. The data management and analytics industry encompasses business intelligence/reporting, predictive analytics, machine learning, and data governance.
BAR360 open data platform presentation at DAMA, SydneySai Paravastu
Sai Paravastu discusses the benefits of using an open data platform (ODP) for enterprises. The ODP would provide a standardized core of open source Hadoop technologies like HDFS, YARN, and MapReduce. This would allow big data solution providers to build compatible solutions on a common platform, reducing costs and improving interoperability. The ODP would also simplify integration for customers and reduce fragmentation in the industry by coordinating development efforts.
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
I am an accomplished certified Data Science professional with 8 + years of experience, looking for Data Scientist/Data Analyst/Data Engineer Position in a reputed organization. I have played strategic role in driving business solution and business growth through innovation and thought leadership in analytics technology/product domain. With an avid intellectual curiosity, and the ability to mine hidden gems located within large sets of structured, semi-structured and unstructured data. Able to leverage a heavy dose of mathematics and applied statistics with visualization and a healthy sense of exploration. Delivers efficient and reliable IT solutions and excels in building/leading teams in high-pressure environments. Have experience on Big Data Hadoop, Data Science, Data Mining, Business Intelligence & Analytics, Database Architecture and Incident Management area. In the recent past lot of my work has been in the Predictive & Prescriptive Analytics arena
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
Addressing the Omnichannel dilemma should be a top priority for CPG manufacturers. The omnichannel brings with it, a wide array of new challenges. It's time for retailers and manufacturers to get OmniSmart about attracting, converting and delighting customers!
Simplifying Data Interoperability with Geo Addressing and EnrichmentPrecisely
Working with addresses is hard! Each address in the U.S. has 13 attributes, and there are over 300 attributes to consider worldwide. Precisely’s geo addressing combines address verification, geocoding, and returning valid and accurate address suggestions, plus it appends a unique and persistent ID that enables an address to serve as the common element for simplifying data enrichment and enables context around said address.
Different business needs require a unique standardization, verification, and enrichment approach. Questions such as:
- Is a house in an area of high fire risk?
- How can we target our advertising to families living in apartments?
- Which downtown businesses can I serve with my existing fiber-optic infrastructure?
Are easy to answer with geo addressing and data enrichment.
Join us to learn how:
- Adding context to data such as points of interest, property details, demographics, and boundaries (neighborhoods, postal codes, flood zones) can help bring agility and insights to improve business processes
- Scalable, international geo addressing can simplify the matching, cleansing, verification, and geocoding of diverse business data across an organization
- A unique and persistent identifier helps simplify the labor of joining disparate data sets for seamless data interoperability without costly and time-consuming spatial processing
Reinvent Your Data Management Strategy for Successful Digital TransformationDenodo
This document discusses reinventing data management strategies for digital transformation. It notes that IT spends a large amount on ETL and storage but most data is not used. It also notes a growing gap between business needs for fast data access and analysis and IT's ability to provide it. The document proposes data virtualization as a solution to give both business and IT agility by providing unified access to all data sources. It provides examples of how data virtualization helped organizations like Indiana University and HUD improve strategic decision making and prevent fraud.
This document discusses data science, big data, and big data architecture. It begins by defining data science and describing what data scientists do, including extracting insights from both structured and unstructured data using techniques like statistics, programming, and data analysis. It then outlines the cycle of big data management and functional requirements. The document goes on to describe key aspects of big data architecture, including interfaces, redundant physical infrastructure, security, operational data sources, performance considerations, and organizing data services and tools. It provides examples of MapReduce, Hadoop, and BigTable - technologies that enabled processing and analyzing massive amounts of data.
Creating a Data Driven Organization - StampedeCon 2016StampedeCon
Companies today are all focused on finding new consumption models to better utilize the data they produce. This presentation will provide insights and best practices for creating the organization and sponsorship necessary to set the foundation for success.
For this session, Dan will provide an overview of the process and methodologies he employs to establish and sustain a Data Driven Culture. Key topics will include:
Data Driven Culture
Executive Sponsorship
Organizational Structure – Collaboration Hubs and Bi-Modal Analytics
Role of Hadoop and Big Data as Part of Data Driven Culture
Slides from a recent Big Data Warehousing Meetup titled, Big Data Analytics with Microsoft.
See Power Pivot/ Power Query/ Power View/ Power Maps and Azure Machine Learning be used to analyze Big Data.
One challenge of dealing with Big Data project is to acquire both structured and instructed information in order to find the right correlation. During the event, we explained all the steps to build your model and enhance your existing data through Microsoft's Power BI.
We had an in-depth discussion about the innovations built into the latest stack of Microsoft Business Intelligence, and practical tips from Technology Specialist’s from Microsoft.
The session also featured demos to help you see the technology as an end-to-end solution.
For more information, visit www.casertaconcepts.com
DAS Slides: Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in data architecture, along with practical commentary and advice from industry expert Donna Burbank.
This document discusses big data, defining it as large volumes of diverse data that come in at high velocity, requiring new techniques and technologies to capture, store, distribute, manage and analyze the data to extract value and enable better decision making. It notes that 90% of data in the world has been created in just the past two years, and data is doubling every two years. Big data tools are needed to analyze both structured and unstructured data from a variety of internal and external sources to gain business insights. Examples provided discuss using big data in insurance to identify risk patterns and sales leads, and in banking to analyze customer transactions and enrich customer profiles.
This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
Similar to Big data why big data is huge for CPG manufacturers (20)
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
2. JANET DORENKOTT, BIO
• Over 20 years of experience in information technology.
• Founded Relational Solutions in 1996 and co-owns with Rob York.
• Focused on data warehousing, data integration & business intelligence solutions
• Specialize in the complex issues associated with integrating point of sale and syndicated data
for the CPG industry & developed applications including POSmart and BlueSky, designed for
handling data complexities unique to CPG companies.
• Member of Retailwire’s Braintrust
• Founder of the Demand Signal Repository Institute on LinkedIn.
• Participated in the implementation of over 200 data warehouse and BI projects for companies
that include Chrysler, Chase, Timken, Xerox, Glaxo, Smuckers, P&G and many others.
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
3. GOALS FOR TODAY
• TO DEFINE BIG DATA
• EXPLAIN HOW BIG DATA CAN IMPROVE BUSINESS
• EXPLAIN HOW TO USE IT
• SHOW THE IMPORTANCE OF LEVERAGING SOCIAL MEDIA
4. “Top 10
“Companies
on the Move”
BlueSky
Integration
Studio
“Best at
integrating POS
with Internal
data”
Cleveland
Weatherhead 100
Fastest Growing
Businesses
Oracle
Developer of
the Year
Data Warehouse
& BI Consulting
1996 - 98 1999 - 01 2002 – 04 2005 - 06 2007 - 08 2009 - 10 2011 – 12 2013
“Data Warehouse
of the Year!”
BlueSky
“Coolest New
Technologies”
DataStage
ETL Best
Implementors
Award
Informatica’s
Partner of the
Year
Selects BIS to
integrate POS &
TradeEdge
Selects
POSmart to
embed in DSR
Best Software”
Finalist
BIG DATA… IT’S IN OUR BLOOD!
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
5. BUSINESS INTELLIGENCE
• Leverages data to provide users with “Fact Based Decision” capability.
• Derived from an enterprise data warehouse for management decisions
• Reports are also derived from “stove pipe” solutions, ERP applications and homemade
integration processes.
• Operational reports are not the same as Analytical reports.
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
6. TRANSACTIONAL VS. ANALYTICAL REPORTING
TRANSACTIONAL SYSTEM
• DATABASE STRUCTURE DESIGNED FOR
DATA ENTRY, UPDATE, AND PROCESSING.
• OPERATIONAL REPORTS.
• REPORTING USERS CAN IMPACT
PROCESSING - QUICKLY BECOMES A SLOW
ENVIRONMENT
• PURCHASED APPLICATIONS CONTAIN
STANDARD REPORTS
• INCONSISTENT DUE TO “TWINKLING”
• NO ACCESS TO SOME INFO
• REPORTS CAN TAKE DAYS OR BE
IMPOSSIBLE TO GET
• NORMALIZED MODEL FOR FAST INPUT
DATA WAREHOUSE
• DATA MODEL DESIGNED FOR ANALYTICAL
REPORTING AND AD-HOC QUERIES, BOTH
FROM A CREATION AND A PERFORMANCE
STANDPOINT
• FREQUENTLY CONTAINS DETAIL DATA AND
PRE-AGGREGATED SUMMARIES FOR FAST
REPORTING
• TOOLS ALLOW END USERS TO INQUIRE,
DRILL FROM SUMMARY TO DETAIL
• REPORTING USERS DO NOT IMPACT THE
TRANSACTIONAL SYSTEM
• OFTEN COMBINES DATA FROM MULTIPLE
TRANSACTIONAL SYSTEMS
• CONSISTENT – BUSINESS RULES
• TYPICALLY DENORMALIZED
Data
Mart
Transactional
System
e.g.
SAP
JDE
Oracle Apps
JDA
Homegrown
Data
Mart
Data
Mart
Data
Mart
Data
Mart
Data
Mart
Periodic Data Feeds
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
7. BIG DATA STARTED WITH ERP AND DATA WAREHOUSING
• DATA MART: FOCUSED
COLLECTION OF SIMILAR DATA
FOR REPORTING PURPOSES
Sales
Data Mart
Finance
Data Mart
Forecasting
Data Mart
International Sales
Data Mart
Vendor Information
Data Mart
DATA WAREHOUSE:
INTEGRATION OF MULTIPLE
DATA MARTS INTO AN
ENTERPRISE SOLUTION
Marketing
Data Mart
Common
Reference
Values
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
8. THE BIG DATA EXPLOSION!
Accounting
Shipments
Order
Processing
Manufacturing
Transactional/ERP
Analytical
Big Data
Currency Conversion
Weather Trends
SMS/MSS
Photo’s
Syndicated Data
Web & Outside Data Sources
EDW
CRM
Loyalty
Segmentation
Panel Data
Wholesaler, Distributor
& Broker Data
Promotion Results
Web Logs
EDI
Retailer POS Web Logs
3rd Party Data
Click Stream
Audio
Textual Content
Video
Reputation
Management
Social Media
Chatter
Blogs
Location Info
3-D Content
Schmatics
Geo-Spacial
Speech to
Text
Demographics
Emerging Market
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
9. WHAT’S THE DIFFERENCE?
Un-Structured
• Social Media
• Chatter, Text
Analytics, Blogs,
Tweets, Comments,
Likes, Followers,
Social Authority,
Clicks, Tags, etc.
• Digital, Video
• Audio
• Geo-Spacial
Multi-Structured
/Hybrid
• Emerging Market Data
• Loyalty
• E-Commerce
• Other Third Party Data
• Weather
• Currency Conversion
• Demographic
• Panel
• POS, POL, IR, EDI, RFID, NFC, QR,
IRI, Rsi, Nielsen, Other
Syndicated, IMS, MSA, etc.
Structured
ERP & DW
• Main Frame
• SQL Server
• Oracle
• DB2
• Sybase
• Access, Excel, txt, etc
• Teradata
• Neteeza, Other mpp
• SAP, JDE, JDA, Other ERP.
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
11. IT’S NOT JUST SIZE ,
VARIETY!
EDI
RFID
SAP
DB2
Oracle
TXT
SQL
AS2
CRM
TPO JDE
QR
ACESS
Mobile
EXCEL
NPD
IMS
TPM
E-Comerce
CRM
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
12. IT’S NOT JUST VOLUME & VARIETY!
VELOCITY MATTERS!
• Daily
• Weekly
• Monthly
• Quarterly
• Annually
• Every Hour
• Every Minute
• Every Second
• Every Nano-Second!
• Constantly Changing
• Constantly Growning!
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
13. IT’S NOT JUST VOLUME & VARIETY & VELOCITY.
COMPLEXITY!
• Aligning Hierarchy’s
• Integrating Internal Master Data with Retailer Master Data
• Applying Various Calendars
• Regional Territories
• Geographic alignment
• Currency Conversion
• Emerging Market
• Loyalty
• Market Basket
• Cleansing Issues
• Re-cast Data
• Slowly Changing Dimensions (how you want to handle
history, new stores, etc).
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
14. WHAT IS HADOOP?
•HADOOP IS AN OPEN SOURCE DATA LIBRARY WITH 2 KEY COMPONENTS:
1. DISTRIBUTED FILE SYSTEM (HDFS) – FOR HIGH BANDWIDTH, CLUSTER BASED STORAGE
2. DATA PROCESSING FRAMEWORK – USES “MAPREDUCE” TO DISTRIBUTE/MAP LARGE DATA SETS ACROSS
MULTIPLE SERVERS. EACH SERVER CREATES A SUMMARY OF THE DATA THAT HAS BEEN ALLOCATED TO IT. FROM
THERE, DATA IS “REDUCED” OR “AGGREGATED.” SIMPLY PUT, IT IS MAPPED, THEN REDUCED.
“HADOOP LETS YOU DEAL WITH VOLUME, VELOCITY AND VARIETY OF DATA. IT TRANSFORMS COMMODITY
HARDWARE AND PROVIDES AUTOMATIC FAILOVER.”
OWEN O’MALLEY, ARCHITECT FOR MAPREDUCE & SECURITY.
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
15. WHAT IS MAPREDUCE?
• A PARALLEL PROGRAMMING FRAMEWORK
• MADE POPULAR BY GOOGLE
• GENERATE SEARCH INDEXES
• WEB SCORING ALGORITHMS
• C++, JAVA, PYTHON, ETC.
• HARNESS 1000S OF CPUS
• MAPREDUCE PROVIDES
• AUTOMATIC PARALLELIZATION
• FAULT TOLERANCE
• MONITORING & STATUS UPDATES
“MAPREDUCE ALLOWS PROGRAMMERS
WITHOUT ANY EXPERIENCE WITH PARALLEL
AND DISTRIBUTED SYSTEMS TO EASILY
UTILIZE THE RESOURCES OF A LARGE
DISTRIBUTED SYSTEM.”
- JEFFREY DEAN AND SANJAY GHEMAWAT,
GOOGLE, INC., 2004
Map Function
Scheduler
Results
map
shuffle
reduce
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
16. MAPREDUCE IS SIMPLE WORD COUNT
Unstructured
Data Input
Boat Yacht Lake
House House Lake
Boat House Yacht
Fish Fish Fish
Splitting Mapping Shuffling Reducing Result
Boat Yacht Lake
House House Lake
Boat House Yacht
Fish Fish Fish
Boat, 1
Yacht, 1
Lake, 1
House, 1
House, 1
Lake, 1
Boat, 1
House, 1
Yacht, 1
Fish, 1
Fish, 1
Fish, 1
Boat, 1
Boat, 1
Yacht, 1
Yacht, 1
Lake, 1
Lake, 1
House, 1
House, 1
House, 1
Fish, 1
Fish, 1
Fish, 1
Boat, 2
Yacht, 2
Lake, 2
House, 3
Fish, 3
Boat, 2
Yacht, 2
Lake, 2
House, 3
Fish, 3
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
17. COMMON TERMINOLOGY
• PIG – HIGH LEVEL LANGUAGE THAT CONVERTS WORK TO MAPREDUCE
• HIVE – TRANSFORMS & CONVERTS TO MAPREDUCE USING SQL
• HBASE – SCALABLE, DISTRIBUTED DATABASE. PROVIDES A SIMPLE INTERFACE TO
DATA (I.E. FACEBOOK MESSAGES UTILIZE THIS)
• ZOOKEEPER – PROVIDES COORDINATION FOR SERVERS
• HCATALOG – METADATA PULLED OUT OF HIVE
• MAHOUT – MACHINE LEARNING LIBRARY
• SCOOP – TOOL TO RUN MAPREDUCE APPS THAT PULL OR PUSH OUT OF SQL OR
ORACLE
• CASCADE – TRANSLATES DOWN INTO MAPREDUCE
• OOZIE – WORKFLOW COORDINATION TO LEARN MAPREDUCE JOBS
• FUSE DFS – USED TO ACCESS LINUX FILES
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
18. HOW CAN BIG DATA BE USED?
• BIG DATA CAN BE USED TO MICRO-SEGMENT
CUSTOMERS, ANALYZE SENTIMENT, PREDICT
BEHAVIOR, PERSONALIZE OFFERS, CROSS-SELL
AND UPSELL ACROSS CHANNELS, MANAGE
REPUTATION, INCREASE SALE AND PROFITS.
• COMPANIES NEED TO “WALK BEFORE YOU RUN.”
• THE “BUILD IT & THEY WILL COME” PHILOSOPHY
RARELY WORKS. IDENTIFY A BUSINESS NEED.
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
19. SOCIAL MEDIA REQUIRES YOU TO
LISTEN
ENGAGE
INFORM
OFFER
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
20. LEVERAGING THE DATA MEANS YOU NEED TO
ACCESS
ANALYZE
ACT
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
21. IS SOCIAL MEDIA REALLY WORTH
LEVERAGING?
ACCORDING TO THE PEW RESEARCH CENTER:
• 100 MILLION ACTIVE USERS
• 50 MILLION LOG ON TO TWITTER EVERYDAY
• 55% ARE MOBILE USERS
-------------------------------------------
• AVERAGE TWEETS SENT PER DAY (IN MILLIONS):
• IN JANUARY, 2010 – 50 MILLION TWEETS PER SECOND
• IN FEBRUARY, 2011 – 140 MILLION TWEETS PER SECOND
• IN SEPTEMBER, 2011 – 230 MILLION TWEETS PER SECOND
• There were 2.5 million tweets regarding Steve Jobs’
death in the first 13 hours after it was reported, which is
about 53 tweets per second.
• 6,939 Tweets per second in Japan on New Years Eve at
Midnight
According to McKinsey Global Institute:
• Facebook – 700,000,000,000 minutes spent/month
• Google – 34,000 search/sec
• Email – 838,000,000 messages in 2013
• Twitter – 500,000,000 tweets/day
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
22. IT’S ONLY JUST BEGUN!
• LINKEDIN
• FACEBOOK
• YOUTUBE
• SLIDESHARE
• BRIGHTTALK.COM
• SCRIBED
• NAYMZ
• JIGSAW
• SPOKE
• G+
• TWITTER
• VINE
• INSTAGRAM
• BING
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
24. KNOW YOUR SOCIAL REPUTATION
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
25. KNOW WHERE YOUR SENTIMENT IS COMING FROM
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
26. SEE WHERE YOUR CHAMPIONS ARE
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
27. UNDERSTAND WHERE YOU NEED DAMAGE CONTROL
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
28. WHAT ARE YOUR FOLLOWERS SAYING
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,
29. GOALS FOR TODAY – ACCOMPLISHED!
• TO DEFINE BIG DATA – VOLUME, VARIETY, VELOCITY & COMPLEXITY
• EXPLAIN HOW BIG DATA CAN IMPROVE BUSINESS – LISTEN, ENGAGE, INFORM & OFFER
• EXPLAIN HOW TO USE IT – LEVERAGING A FOUNDATION
• SHOW THE IMPORTANCE OF LEVERAGING SOCIAL MEDIA – INTEGRATE WITH OTHER DATA
30. THANK YOU & STAY TUNED!
• FOLLOW JANET DORENKOTT ON LINKEDIN, EMAIL JANETD@RELATIONALSOLUTIONS.COM
• CALL US AT 440-899-3296, JANET IS X225 / KAREN IS X 232
• FOLLOW RELATIONAL SOLUTIONS ON LINKEDIN, TWITTER @POSMARTBLUESKY & ON
FACEBOOK
• JOIN OUR “DEMAND SIGNAL REPOSITORY INSTITUTE” & “BIG DATA ASSOCIATION” GROUP ON
LINKEDIN
• SUBSCRIBE TO THE RELATIONAL SOLUTIONS CHANNEL ON YOUTUBE:
• RELATIONAL SOLUTIONS CHANNEL
• VISIT US AT WWW.RELATIONALSOLUTIONS.COM OR CALL 440-899-3296 X225
• LEARN MORE FROM OUR WEBINARS & DOWNLOAD OUR WHITEPAPERS
• SEE PRODUCT DEMO’S & DOWNLOAD TRIALS FROM OUR WEBSITE
Property of Relational Solutions, Inc. By Janet Dorenkott June, 2013,