This document summarizes Rob Saker's predictions for retail data and AI in 2023. It predicts that retailers will focus on last mile optimization using real-time data and AI to consolidate orders and routing. It also predicts the use of generative AI for personalized product recommendations and images. Composable customer data platforms that integrate best of breed solutions are also predicted to see greater adoption. The document further predicts that peer-to-peer secure data sharing and localized large language models focused on specific industries will emerge.
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Transform your marketing and sales capabilities with Big Data and A.I
1) Why is Customer Data Platform (CDP) ?
Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing
Question: Why can CDP disrupt business model for restaurant industry (B2C) ?
2) How would CDP work in practice ?
Introducing USPA.tech as logical framework for implementing CDP in practice
How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ?
3) How can we implement CDP for business?
Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Gartner named customer data platforms (CDPs) one of the key technologies that will demand marketers’ attention in 2018. Michael Katz, Cofounder and CEO of mParticle, explains why CDPs are not just another acronym and how consumer brands ranging from Airbnb to NBCUniversal to Zappos are using them to optimize omnichannel customer experiences and marketing outcomes, in all the moments that matter.
Originally presented at AdExchanger Industry Preview 2018 by Michael Katz, Cofounder and CEO, mParticle.
CDP - 101 Everything you need to know about Customer Data PlatformsEddy Widerker
There has been a lot of buzz around Customer Data Platforms (CDP) in the past months/years. This presentation gives you a great overview of what a CDP is, the similarities across different systems such as a DMP or CRM. As well as valuable questions that help you determine if your organization needs a Customer Data Platform.
At the same time, if you find yourself being a bit overwhelmed with all of the 1st 2nd or 3rd party data targeting options from, I'd highly recommend using a service like ClearSegment to understand various data providers - it's data collection methodologies as well as their individual segments. https://clearsegment.com/
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
The presentation describes my views around the data we encounter in digital businesses like:
- Looking at common Data collection methodologies,
-What are the common issues within the decision support system and optimiztion lifecycle,
- Where are most of failing?
and most importantly, "How to connect the dots and move from Data to Strategy?"
I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment.
Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems.
As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.
Transform your marketing and sales capabilities with Big Data and A.I
1) Why is Customer Data Platform (CDP) ?
Case study: Enhancing the revenue of your restaurant with CDP and mobile app marketing
Question: Why can CDP disrupt business model for restaurant industry (B2C) ?
2) How would CDP work in practice ?
Introducing USPA.tech as logical framework for implementing CDP in practice
How Can a Customer Data Platform Enhance Your Account-Based Marketing Strategy (B2B) ?
3) How can we implement CDP for business?
Introducing the CDP as customer-first marketing platform for all industries (my key idea in this slide)
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
A solid data architecture is critical to the success of any data initiative. But what is meant by “data architecture”? Throughout the industry, there are many different “flavors” of data architecture, each with its own unique value and use cases for describing key aspects of the data landscape. Join this webinar to demystify the various architecture styles and understand how they can add value to your organization.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Gartner named customer data platforms (CDPs) one of the key technologies that will demand marketers’ attention in 2018. Michael Katz, Cofounder and CEO of mParticle, explains why CDPs are not just another acronym and how consumer brands ranging from Airbnb to NBCUniversal to Zappos are using them to optimize omnichannel customer experiences and marketing outcomes, in all the moments that matter.
Originally presented at AdExchanger Industry Preview 2018 by Michael Katz, Cofounder and CEO, mParticle.
CDP - 101 Everything you need to know about Customer Data PlatformsEddy Widerker
There has been a lot of buzz around Customer Data Platforms (CDP) in the past months/years. This presentation gives you a great overview of what a CDP is, the similarities across different systems such as a DMP or CRM. As well as valuable questions that help you determine if your organization needs a Customer Data Platform.
At the same time, if you find yourself being a bit overwhelmed with all of the 1st 2nd or 3rd party data targeting options from, I'd highly recommend using a service like ClearSegment to understand various data providers - it's data collection methodologies as well as their individual segments. https://clearsegment.com/
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
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.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
Behind the Buzzword: Understanding Customer Data Platforms in the Light of Pr...Rising Media Ltd.
Customer Data Platform (CDP) systems are the newest answer to an old question: how to assemble a complete view of each customer. This session explores the reality of what CDPs can and cannot do, how CDPs differ from other systems, the types of CDP systems available, and how to find the right CDP for your purpose, especially with regard to data science projects and predictive modeling. You will come away with a clear understanding of where CDP fits into the larger data management landscape, what distinguishes CDP from older approaches to customer data management, and the state of the CDP industry in Europe.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
Our report will provide a look into the technology landscape of the future, including:
- Importance of AI in enabling innovation
- Catalysts of future innovations
- Top technology trends in 2023-2024
- Main benefits of AI adoption
- Steps to prepare for future disruptions.
Download your free copy now and implement the key findings to improve your business.
Why Do Banks Need A Customer Data Platform?Lemnisk
Banks traditionally have been known to amass customer information across both online and offline data channels. However, a lot of this data resides in silos and marketers have been unable to leverage this data to run targeted marketing campaigns. Here are the top four reasons why a Customer Data Platform would be best suited for Banks.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
An overview of Customer Data Platforms (CDP) with the industry leader who coined the term, David Raab. Find out how to use Live Customer Data to create a better customer experience and how Live Data Management can give you a competitive edge with a 360 degree view of your clients.
Learn:
- The definition and requirements for Customer Data Platforms
- The differences between Customer Data Platforms and comparative technologies such as Data Warehousing and Marketing Automation
- Reference architectures/approaches to building CDP
- How Treasure Data is used to build Customer Data Platforms
And here's the song: https://youtu.be/RalMozVq55A
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Overview: Big Data Use Cases in Telecom, Retail, Insurance, Automotive, Media & Banking & Finances Industry Segments. How can we map these business challenges to Solutions on AWS Cloud? Let's Find Out!
Big Data is Growing Bigger & Bigger with a prediction of 40 Zeta Bytes of Data by 2020.
> What are the 4 Vs of Big Data?
> Big Data Industry Use Cases:
- Telecommunications
- Retail
- Insurance
- Automotive
- Media
- Banking
Which AWS Components can be mapped to each stage of the Big Data Life Cycle:
AWS S3, AWS EC2, AWS EMR, AWS Redshift, Data Pipelines & many more.
#IBMInsight session presentation "Orchestrating a Customer-Activated Supply Chain"
Assembling the pieces of a customer-activated supply chain involves activities on three dimensions: Sharpen visibility and insight, Partner for innovation, Become customer-activated
IBM supply chain analytics solutions to leverage Big Data
More at ibm.biz/BdEPRX
How to Use a Semantic Layer to Deliver Actionable Insights at ScaleDATAVERSITY
Learn about using a semantic layer to enable actionable insights for everyone and streamline data and analytics access throughout your organization. This session will offer practical advice based on a decade of experience making semantic layers work for Enterprise customers.
Attend this session to learn about:
- Delivering critical business data to users faster than ever at scale using a semantic layer
- Enabling data teams to model and deliver a semantic layer on data in the cloud.
- Maintaining a single source of governed metrics and business data
- Achieving speed of thought query performance and consistent KPIs across any BI/AI tool like Excel, Power BI, Tableau, Looker, DataRobot, Databricks and more.
- Providing dimensional analysis capability that accelerates performance with no need to extract data from the cloud data warehouse
Who should attend this session?
Data & Analytics leaders and practitioners (e.g., Chief Data Officers, data scientists, data literacy, business intelligence, and analytics professionals).
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Improving Data Literacy Around Data ArchitectureDATAVERSITY
Data Literacy is an increasing concern, as organizations look to become more data-driven. As the rise of the citizen data scientist and self-service data analytics becomes increasingly common, the need for business users to understand core Data Management fundamentals is more important than ever. At the same time, technical roles need a strong foundation in Data Architecture principles and best practices. Join this webinar to understand the key components of Data Literacy, and practical ways to implement a Data Literacy program in your organization.
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
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.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
Behind the Buzzword: Understanding Customer Data Platforms in the Light of Pr...Rising Media Ltd.
Customer Data Platform (CDP) systems are the newest answer to an old question: how to assemble a complete view of each customer. This session explores the reality of what CDPs can and cannot do, how CDPs differ from other systems, the types of CDP systems available, and how to find the right CDP for your purpose, especially with regard to data science projects and predictive modeling. You will come away with a clear understanding of where CDP fits into the larger data management landscape, what distinguishes CDP from older approaches to customer data management, and the state of the CDP industry in Europe.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
Digital Transformation is a top priority for many organizations, and a successful digital journey requires a strong data foundation. Creating this digital transformation requires a number of core data management capabilities such as MDM, 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.
Our report will provide a look into the technology landscape of the future, including:
- Importance of AI in enabling innovation
- Catalysts of future innovations
- Top technology trends in 2023-2024
- Main benefits of AI adoption
- Steps to prepare for future disruptions.
Download your free copy now and implement the key findings to improve your business.
Why Do Banks Need A Customer Data Platform?Lemnisk
Banks traditionally have been known to amass customer information across both online and offline data channels. However, a lot of this data resides in silos and marketers have been unable to leverage this data to run targeted marketing campaigns. Here are the top four reasons why a Customer Data Platform would be best suited for Banks.
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
An overview of Customer Data Platforms (CDP) with the industry leader who coined the term, David Raab. Find out how to use Live Customer Data to create a better customer experience and how Live Data Management can give you a competitive edge with a 360 degree view of your clients.
Learn:
- The definition and requirements for Customer Data Platforms
- The differences between Customer Data Platforms and comparative technologies such as Data Warehousing and Marketing Automation
- Reference architectures/approaches to building CDP
- How Treasure Data is used to build Customer Data Platforms
And here's the song: https://youtu.be/RalMozVq55A
How do OpenAI GPT Models Work - Misconceptions and Tips for DevelopersIvo Andreev
Have you ever wondered why GPT models work? Do you ask questions like:
◉ How does GPT work? Why does the same problem receive different answers for different users? Is there a way to improve explainability? ◉ Can GPT model provide its sources? Why does Bing chat work differently? What are my ways to have better performance and improve completions? ◉ How can I work with data in my enterprise? What practical business cases could a generative AI model fit solving?
If you are tired of sessions just scratching the surface of OpenAI GPT, this one will go deeper and answer questions like why, why not and how.
Key Terms; ChatGPT Enterprise; Top Questions; Enterprise Data; Azure Search; Functions; Embeddings; Context Encoding; General Intelligence; Emerging Abilities; Chain of Thought; Plugins; Multimodal with DALL-E; Project Florence
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how data architecture is a key component of an overall enterprise architecture for enhanced business value and success.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Overview: Big Data Use Cases in Telecom, Retail, Insurance, Automotive, Media & Banking & Finances Industry Segments. How can we map these business challenges to Solutions on AWS Cloud? Let's Find Out!
Big Data is Growing Bigger & Bigger with a prediction of 40 Zeta Bytes of Data by 2020.
> What are the 4 Vs of Big Data?
> Big Data Industry Use Cases:
- Telecommunications
- Retail
- Insurance
- Automotive
- Media
- Banking
Which AWS Components can be mapped to each stage of the Big Data Life Cycle:
AWS S3, AWS EC2, AWS EMR, AWS Redshift, Data Pipelines & many more.
#IBMInsight session presentation "Orchestrating a Customer-Activated Supply Chain"
Assembling the pieces of a customer-activated supply chain involves activities on three dimensions: Sharpen visibility and insight, Partner for innovation, Become customer-activated
IBM supply chain analytics solutions to leverage Big Data
More at ibm.biz/BdEPRX
Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.
Why is programmatic taking off? What is this revolution all about?Datacratic
Google Quebec hosted Think Quebec and this year they explored digital marketing as a path to quicker, deeper connections between a brand and its consumer. Inspired by Parkour, the popular urban sport of finding the most direct route to your goal, they presented campaigns and strategies as beautiful as they are successful–another discipline at the junction of art and science. James Prudhomme, CEO Datacratic spoke at Google's Think Quebec. His Talk is entitled "Why is programmatic taking off? What is this revolution all about?"
Womenswear retailer Monsoon Accessorize IT & Ecommerce Director John Bovill explains his hopes for the Project Customer big data project at multichannel consultancy Practicology's 2016 client conference.
As the digital transformation of the retail market accelerates, advanced analytics and big data technologies can help retailers gather critical customer intelligence and optimize operations that lead to better customer experiences and increased sales.
Leverage Real-Time Purchase Intent to Boost Sales & Customer GrowthTinuiti
From audience approach to reliable platforms and tracking measurement, Tinuiti and partners are here to help marketers update their strategies in the face of increased privacy. Join our exclusive series as we unpack the top priorities for your social media advertising in a year where everything is changing.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Google Analytics Konferenz 2019_Google Cloud Platform_Carl Fernandes & Ksenia...e-dialog GmbH
Marketing in the Cloud with Google
It's no secret that "data" and "the cloud" presents a huge opportunity for marketers - but often it's difficult to understand how exactly these famous buzzwords can really help step change performance for a business. In this talk you will learn how Google thinks about marketing in the cloud, what the key use cases are and best practices that will help advertisers prepare for the future.
Data Integration and Marketing Attribution ROIVENUE™
Microsoft and ROIVENUE™ have teamed up to provide a glimpse into the benefits of integrating all your marketing data. All about the latest advancements in data management powered by Azure and how ROIVENUE™ helps marketers identify where to best allocate their digital spend with our Marketing Attribution models and Budget Optimizer™.
AUBG Lecture - Data & Analytics - Importance of data.pptxYasen4
Lecture at the American University in Bulgaria talking about the concept of the T-shaped marketer and the importance of data in making informed decisions.
Predictive Conversion Modeling - Lifting Web Analytics to the next levelPetri Mertanen
Annalect presentation at Superweek 2017: Predictive Conversion Modeling - Lifting Web Analytics to the next level. Presented by Petri Mertanen, Director of Digital Analytics and Ron Luhtanen, Data Science Analyst. #SPWK
Unleashing the Power of Quantum as a Digital Designer for Massive Affiliate C...AgbosBenson
You can use this AI Tool To making good money from online very day, every week and every month.
We have on 5 spots left now.
Download for free and get access to our AI Tool That helped someone like you to make $186,132.45 in a single month.
Get A Unified Record For All Your Customer Data With CDPTechahead Software
It is typically exceedingly challenging for businesses to offer consistent customer experiences across a variety of channels and consumer devices because this data is typically held in silos, whether organizational or technological. For example, a digital product development company collects data from media listings.
Source: https://techfily.com/get-a-unified-record-for-all-your-customer-data-with-cdp/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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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.
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.
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/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Rob Saker
Global VP Retail & Manufacturing, Databricks
What’s Next in Retail Data & AI?
Predictions for 2023
2. Labor wage & capacity
Labor wage
growth has
increased, and
staffing
remains a
challenge
3. Out of stocks near record levels
Consumers
say 1 in 5
items are out
of stock in
supermarkets.
4. Margin Challenges with Delivery
E-commerce
fulfillment in
stores is
having a major
adverse effect
on retailer
margins.
McKinsey 2022: https://www.mckinsey.com/industries/retail/our-insights/achieving-profitable-online-grocery-
order-fulfillment
4.4%
Net P&L -12.9%
Basket
Margin
27.9%
Gross
Margin
5. Pursuing New Revenue Opportunities
Retailers are
vying for new
revenue sources
and bigger share
of ad budgets
with retail media
networks.
6. Shifting Promotional Funding
Suppliers are
shifting budgets
from trade to
digital for
greater
measurement
and flexibility.
90 days
120 days
150 days
Grocery
Convenience
Drug
Before promotion During promotion
E-commerce
Promotion
Start
7. Inflation Around the Globe
Food, Energy and transportation led record inflation in
all regions.
• IMF projected inflation to reach 6.6
percent this year in advanced
economies and 9.5 percent in
emerging market and developing
economies
• Upward revisions of 0.9 and 0.8
percentage points respectively from
three months earlier.
8. I came to NRF 2023 and
saw the latest pie rates
of the Caribbean.
* Not a selfie
9. What Drove Priorities in 2022
Conflicts disrupted
supply of raw
materials to finished
goods.
Geopolitical
Conflicts
Inflation rost in all
markets around the
globe.
Inflation
Rail and ship based
logistics continued
to struggle with
disruptions
Shipping
Disruptions
Despite strong
wage growth,
retailers struggled
to fill ranks.
Labor Costs &
Resource
Availability
10. The State of Data + AI in Retail in 2022
● Fine grained forecasting
● ML based personalization
● Location based targeting
● Real-time Supply Chain Visibility
● Price & promo optimization
● Labor scheduling
● New store location
● Data marketplaces
Major Investments in 2022
Widespread
Adoption
● Migration to real-time data processing
● Consolidation of all data in one data platform
(images, video, structured, streaming)
● Unified Smart Forecasting Services
● Shift to demand sensing vs forecasting
● Revenue growth management
● Advanced customer segmentation
● Automated warehouses
12. Last Mile Optimization: From “Real-time” to Right Time
Balancing customer needs with resource availability to
improve profitability
• Consumers have demand faster
options for delivery.
• Retailers have been buying market
share by subsidizing delivery.
But…
• Retailers often lose money on delivery
from stores due to labor inefficiencies.
Why?
* Still not a selfie
14. Last Mile Pain Points
1
4
55% - Manual processes for planning/dispatching
61% - Lack of real-time visibility once delivery starts
46% - Scheduling Delivery Times
44% - Multiple fulfillment channels & tech
41% - Working with multiple 3rd parties
8% No Pain Points
Biggest Pain Points when Scaling Delivery Models
24% - Travel Distance
36% - Real-time order visibility/tracking
23% - # of Drivers/Size of Fleet
10% - Routing
6%
2% No Pain Points
Cost
Biggest Pain Point in Delivering on Time
15. Last Mile
Management
Suppliers Distribution Retailers Consumers
Delivery
Consumer Insights
Ad Programming
Depletions/Demand Signals
Replenishment
Reordering
Location
Availability
Traffic
Jobs
Orders
Drive time
arrival
Product
Pricing
Inventory
Order
Status
16. Solution
Accelerator
Companies can now
scale out hundreds of
thousands of routes
generated for single
and multi-step
journeys in advance
of route optimization
https://www.databricks.com/solutions/accelerators/scalable-route-generation
Scalable Route Generation
17. Solution
Accelerator
Retailers can
combine real-time
data with analysis to
consolidate orders
and reducing picking
costs.
https://www.databricks.com/solutions/accelerators/order-picking-optimization
The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store
Picking and Packing,
Order Consolidation & Picking
18. 1. Ensure data is available in real-time and integrated for when you run
analysis.
2. Leverage machine learning algorithms to continuously look for ways to
consolidate orders, optimize driving distance, and measure
performance.
3. Incorporate performance feedback into your models.
How to Prepare
Steps to prepare your business for data led last mile
optimization
21. Generative AI for Images
Image generation quality is dependent on breadth and
accuracy of training data.
Thousands or millions Images are
annotated to define features (color, width,
style, shapes, face, pieces of clothing)
Models are trained with images and the
additional context.
This enables computers to automatically
recognize images.
Additional training enables creation, with
feedback when creation is accurate.
25. How to Prepare
Steps to get started with AI Images
1. Capture all relevant images in the Lakehouse
2. Start labeling by using an automated labeling system such as Labelbox.
3. Generate immediate wins with image search, personalization.
4. Work long-term towards image generation.
26. Predictions for 2023
1. Last Mile Optimization
2. Generative AI for personalization
3. Composable CDPs
27. Prediction: Composable CDPs
Adoption of Composable CDP brings best of breed with
integration flexibility and scale.
• Retailers are using this downturn to
invest in consumer engagement to
drive stronger marketing ROI and
capture market share.
• Composable CDPs are foundational to
Retail Media Networks.
Wat’s driving the change?
• 75% of CDP customers expect 5x or
But higher ROI, and most see a positive
ROI in the first year of adoption.
(Twilio)
• • Two top-reported CDP benefits
include a unified customer view (88%)
and analytics (54%). (CDP Institute)
28. What’s Driving Customer Data Platforms
• Drive stronger loyalty &
lower CAC
• Improve incrementality
• Efficiency across
multiple promotion
channels
• Desire to monetize
customer engagement
https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-
insights/commerce-media-the-new-force-transforming-advertising
32. How to Prepare
Steps to get start on your CDP
1. Focus on data source connectivity
2. Leverage machine learning for customer entity resolution
3. Use a Composable CDP approach to maximize scale, accuracy and
flexibility.
33. Predictions for 2023
1. Last Mile Optimization
2. Generative AI for personalization
3. Composable CDPs
4. Peer-to-peer secure data collaboration
34. Prediction: Peer-to-Peer Secure Data Sharing
Why is it attractive?
• Improved collaboration around data can reduce response times by days.
• Enables person to person collaboration, even across companies.
• The value in data monetization is action, not licensing.
But…
• Data marketplaces only enable broadcast of common data sets.
• Existing data sharing requires costly data warehouse licenses or forces companies to
choose the same technology.
35. Data Sharing is No Longer Expensive or Exclusive
• Delta Sharing is open
source
• Works across all clouds
or on-premise.
• Enables users to
consume data from
Excel, Tableau, web and
other data systems.
Retailer Partner
Any use case Any tool Any cloud
On-premises
And many more
Data science
Reporting
Analytics
Access
Permissions
Real-time Data Sharing in Excel
Love it or hate it, Excel is the most popular data tool used by
end users. Exponam has built a plug-in for Excel that allows
users to pull data in directly from Delta Sharing repositories to
update their local analysis.
36. How to Prepare for Data Sharing
Data sharing is no longer expensive or exclusive.
Delta Sharing
1. Centralize your data in one location to manage permissions.
2. Leverage OSS Delta Sharing or Databricks (with Delta Sharing pre-configured).
3. Share secure links to partners.
37. Predictions for 2023
1. Last Mile Optimization
2. Generative AI for personalization
3. Composable CDPs
4. Peer-to-peer secure data collaboration
5. Localized Large Language Models
38. Prediction: Narrow Large Language Models
Why is it attractive?
• Taps into rich product, chat and call center transcripts to provide retailer specificity.
• Cost to train models is rapidly falling.
• Reduce customer service cost/time while improving quality.
• Powers AI chat bots
• Streamline new message creation for new purchases
42. Localized Industry LLMs are appearing
• Startups are training LLMs against
narrow sets of information
• Provides much higher accuracy and
less likelihood of false positives
43. How to Prepare
Delta Sharing
1. Bring all structured and unstructured information into the Lakehouse, including call
center audio, transcripts, online reviews, product information and more.
2. Monitor the environment for retail specific OEMs focusing on narrow LLMs.
3. Start small scale projects
44. The Future of Data + AI in Retail in 2023
● Autonomous
drone delivery
Next 12 Months 2025
1-3 Years
Widespread today
● Retail Media
Networks
● Generative AI
personalization
(match the look,
outfit on person)
● Localized LLMs
● Grab and go stores
● Automated
replenishment to
home
● Real-time data processing
● Data platform modernization
● Unified Smart Forecasting
● Revenue growth management
● Personalization
● Location based targeting
● Real-time Supply Chain Visibility
● Price & promo optimization
● Labor scheduling
● New store location
● Data marketplaces
● Composable CDPs
● Demand Sensing
● Last mile optimization
● Automated
warehouses
● Drone delivery (pilots)
● Personalized Pricing
● Peer-to-peer data
sharing
Predicted date of widespread adoption
46. Thank you
Rob Saker
VP Global Industry Leader, Retail and Manufacturing
https://www.linkedin.com/in/robsaker/
https://twitter.com/robsaker
Editor's Notes
Today we are here to talk about the Lakehouse for Retail, but before we jump into that, I would love to take a step back and talk about what is happening in the retail and consumer goods industries
In one interesting study, they looked at the price increases in desserts across several tropical islands.
In US dollars, they found that the average price of a slice of coconut cream pie in Jamaica had increased to $1.30. In Puerto Rico, that same slice was 1.92. And in the Bahamas, that same slice was over three dollars at $3.02.
So when you go back home to your kids, you can tell them.
What are the innovations that we expect to see over the next several years in retail? And what’s realistic today?
While we think there are many new and unimagined innovations that AI will bring to retail, the reality is that AI is delivering incredible benefits to retail today.
Fine grained personalization and forecasting refreshed frequently are delivering much higher accuracy for retailers today. This is leading to substantial cost savings and revenue growth.
Retailers are keen to respond to COVID and know when to ship products, schedule staff and more. We’re seeing retailers leverage alternative data sets to predict foot traffic for the coming days, and optimize activities accordingly
In the next 12 months, we expect to see the next wave of innovation move from piloting to widespread adoption.
Possibly the most exciting trend is the adoption of unified forecasting services. Companies previously forecasted separately for commercial, supply chain and finance divisions, and then brought this together in a clunky format. They’re now using
The biggest driver of investment in retail over the next 12 months will be in reducing the cost to serve e-commerce. Robotic curbside pickup, using AI to reduce returns, and even instituting new ways of handling returns will all start appearing as retailers seek to improve profitability in the digital channel.
In 1-3 years, companies will advance and begin to introduce capabilities that redefine their business around AI. We’re seeing experimentation around this now, but these are the types of capabilities that require years of development.
Apparel retailers will move from match the look, where you upload your favorite photo to an apparel site and it makes suggestions to you, to the ability to show what clothing will look like on your body. Using generative adversarial networks (similar to computer graphics in movies), web sites and apps will be able to take photos and videos of customers and render clothing on them.
Grab and go stores will start to see adoption as 5G networks come into effect and reduce the cost of cabling.
And we predict that we’ll see the first few drone delivery pilots launch in major cities.
And by 2025, we should see innovations that fundamentally change the industry.
Customers will treat retail as a subscription service, leaving much of their routine ordering to the retailers. Retailers will build smart algorithms that learn and anticipate needs, and automatically replenish items in a customers home.
And by 2025, we expect to see drone delivery in widespread usage.
What are the innovations that we expect to see over the next several years in retail? And what’s realistic today?
While we think there are many new and unimagined innovations that AI will bring to retail, the reality is that AI is delivering incredible benefits to retail today.
Fine grained personalization and forecasting refreshed frequently are delivering much higher accuracy for retailers today. This is leading to substantial cost savings and revenue growth.
Retailers are keen to respond to COVID and know when to ship products, schedule staff and more. We’re seeing retailers leverage alternative data sets to predict foot traffic for the coming days, and optimize activities accordingly
In the next 12 months, we expect to see the next wave of innovation move from piloting to widespread adoption.
Possibly the most exciting trend is the adoption of unified forecasting services. Companies previously forecasted separately for commercial, supply chain and finance divisions, and then brought this together in a clunky format. They’re now using
The biggest driver of investment in retail over the next 12 months will be in reducing the cost to serve e-commerce. Robotic curbside pickup, using AI to reduce returns, and even instituting new ways of handling returns will all start appearing as retailers seek to improve profitability in the digital channel.
In 1-3 years, companies will advance and begin to introduce capabilities that redefine their business around AI. We’re seeing experimentation around this now, but these are the types of capabilities that require years of development.
Apparel retailers will move from match the look, where you upload your favorite photo to an apparel site and it makes suggestions to you, to the ability to show what clothing will look like on your body. Using generative adversarial networks (similar to computer graphics in movies), web sites and apps will be able to take photos and videos of customers and render clothing on them.
Grab and go stores will start to see adoption as 5G networks come into effect and reduce the cost of cabling.
And we predict that we’ll see the first few drone delivery pilots launch in major cities.
And by 2025, we should see innovations that fundamentally change the industry.
Customers will treat retail as a subscription service, leaving much of their routine ordering to the retailers. Retailers will build smart algorithms that learn and anticipate needs, and automatically replenish items in a customers home.
And by 2025, we expect to see drone delivery in widespread usage.
