I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transf...amdia
Big data and marketing is becoming an important tool for companies. The document discusses how big data can be used for personalization, listening to customers, and responding to better serve their needs. It outlines the key steps in the process from data collection and analysis to insights and actions. Various big data tools and techniques are mentioned to understand customer behavior and trends in order to tailor marketing and customer experiences. The importance of data visualization to tell the story of patterns and create useful insights for businesses is also highlighted.
The document discusses how data activation, using a data management platform (DMP), allows marketers to take customer data from various sources and integrate it across marketing channels like websites, CRM, social media, and more in order to personalize experiences for customers and prospects. Data activation provides benefits like improved targeting, personalization, and conversions compared to traditional data management approaches. Key uses of data activation include prospecting, retargeting, lookalike modeling, site personalization, dynamic creative optimization, and CRM messaging.
Marketers are increasingly using data management platforms (DMPs) to power 360-degree analytics by centrally analyzing audience, campaign, and performance data from multiple sources. A DMP allows marketers to aggregate both first-party and third-party data in one place to gain insights. Through 360-degree analytics, marketers can analyze campaign performance across different audiences and channels to improve targeting and marketing ROI. The document provides guidance on using a DMP to engage in 360-degree analytics through data collection, analysis, and optimization of audience targeting strategies.
Hack Your Customer's Journey - Connect the Data You Already Have to Increase ...Luciano Pesci, PhD
The Customer Journey is a perfect framework for organizing your existing data to find insights that drive higher marketing ROI.
Learn how to organize the digital marketing data you already have into a “map” of the customer’s journey, from their initial awareness of your product/service through their consideration and finally to their decision to purchase.
You can watch the accompanying Webinar here: https://youtu.be/DKBr4PTANDA
Using modern intent data to uncover your best oppsClearbit
Intent data unearths leads who are most engaged that you might not even know about yet and gives you the context to get your timing and personalization right.
Learn how you can find and act on high-intent prospects.
Predicting the future of b2b marketing with NexusCyance
How predictive analytics is transforming b2b marketing by squeezing the value from customer data and driving effective marketing targeting and campaign strategies.
The Brihaspati Infotech is a leading ecommerce development company that specializes in custom ecommerce website development using Magento, Drupal, Big Commerce, Woocommerce & Shopify.
I volunteered my time to share about big data to those looking to understand the space.
This was for Networking with Grace, a group that is focused on helping those get back to work. I put this presentation together to help people learn about big data and how to transition their skill sets to the space.
#MarketingShake - Edward Chenard - Descubrí el poder del Big Data para Transf...amdia
Big data and marketing is becoming an important tool for companies. The document discusses how big data can be used for personalization, listening to customers, and responding to better serve their needs. It outlines the key steps in the process from data collection and analysis to insights and actions. Various big data tools and techniques are mentioned to understand customer behavior and trends in order to tailor marketing and customer experiences. The importance of data visualization to tell the story of patterns and create useful insights for businesses is also highlighted.
The document discusses how data activation, using a data management platform (DMP), allows marketers to take customer data from various sources and integrate it across marketing channels like websites, CRM, social media, and more in order to personalize experiences for customers and prospects. Data activation provides benefits like improved targeting, personalization, and conversions compared to traditional data management approaches. Key uses of data activation include prospecting, retargeting, lookalike modeling, site personalization, dynamic creative optimization, and CRM messaging.
Marketers are increasingly using data management platforms (DMPs) to power 360-degree analytics by centrally analyzing audience, campaign, and performance data from multiple sources. A DMP allows marketers to aggregate both first-party and third-party data in one place to gain insights. Through 360-degree analytics, marketers can analyze campaign performance across different audiences and channels to improve targeting and marketing ROI. The document provides guidance on using a DMP to engage in 360-degree analytics through data collection, analysis, and optimization of audience targeting strategies.
Hack Your Customer's Journey - Connect the Data You Already Have to Increase ...Luciano Pesci, PhD
The Customer Journey is a perfect framework for organizing your existing data to find insights that drive higher marketing ROI.
Learn how to organize the digital marketing data you already have into a “map” of the customer’s journey, from their initial awareness of your product/service through their consideration and finally to their decision to purchase.
You can watch the accompanying Webinar here: https://youtu.be/DKBr4PTANDA
Using modern intent data to uncover your best oppsClearbit
Intent data unearths leads who are most engaged that you might not even know about yet and gives you the context to get your timing and personalization right.
Learn how you can find and act on high-intent prospects.
Predicting the future of b2b marketing with NexusCyance
How predictive analytics is transforming b2b marketing by squeezing the value from customer data and driving effective marketing targeting and campaign strategies.
The Brihaspati Infotech is a leading ecommerce development company that specializes in custom ecommerce website development using Magento, Drupal, Big Commerce, Woocommerce & Shopify.
The document discusses powering retail analytics through real-time data aggregation and analysis. It notes that demand-based management and delivering customer data through new technology will be key assets for retailers. The solution presented aggregates point of sale data in real-time from various retailers and stores to provide analytics and insights for customer engagement, management, and marketing decisions.
InsideView for Marketing gives you access to accurate company and contact data. Enrich inbound leads for faster lead-qualification and routing, build outbound prospect lists from our extensive database, and clean your existing marketing database. All resulting in more quality leads delivered, higher campaign response rates, and better sales close-win rates.
This document discusses analytics and retail analytics. It defines analytics as discovering patterns in data through statistics, programming, and research. Retail analytics specifically aims to improve customer loyalty and sales. It does this by identifying valuable customers, understanding their preferences, and creating personalized shopping experiences through offers targeted to individual needs. Retailers can gather customer data through in-store and online analytics to gain insights that optimize performance.
Digital marketing has evolved from outbound campaigns to include inbound channels and predictive analytics. As data and devices proliferate, marketers must deal with huge amounts of structured and unstructured data in real-time to trigger personalized campaigns. Emerging technologies like natural language processing, big data analytics, machine learning, and the Internet of Things will enable predictive and prescriptive marketing by detecting events and pushing customized content through various devices. This will mark the beginning of Digital Marketing 2.0 and an "anytime, anywhere" marketing paradigm driven by real-time prediction and recommendations.
1) The document discusses how mid-sized companies can maximize the value of their customer data through data economics and mining big data. It provides questions for companies to assess their use of data and identifies challenges.
2) It recommends finding a data expert who can integrate a company's online and offline customer data and use external sources to better understand customers.
3) Answering the questions can help companies measure how effectively they use data and identify areas for improvement to increase marketing results.
Social media analytics powered by data scienceNavin Manaswi
The document discusses social media analytics using big data and data science. It begins by defining social media and its importance for businesses, as well as big data analytics. It then explains how data science can be leveraged in social media analytics to gain powerful insights through techniques like sentiment analysis, social network analysis, and identifying top influencers. Specific use cases are presented for industries like finance and opportunities discussed for applying these techniques globally. Examples are provided of analyzing social media data for companies like banks and Legoland park.
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
AtScale's marketing team partnered with Metadata to improve their lead generation efforts on social media. Using Metadata's Account Based Marketing managed services, Metadata analyzed AtScale's Salesforce data to generate look-alike audiences and build targeted social media campaigns on LinkedIn and Facebook. This resulted in a 200% increase in marketing qualified leads for AtScale over their previous efforts, with 81% of the leads being a direct match to their target audience. The director of demand generation at AtScale was pleased with the results and cost-effectiveness of the Metadata solution.
How Staples Bridged Analytics with Campaign ExecutionVivastream
Jim Foreman of Staples discussed how the company bridges analytics with campaign execution. Staples generates large amounts of customer data from transactions, surveys, and online behavior. They use exploratory, reactive, and predictive analytics on this data to gain insights. Insights are then applied to outbound and inbound marketing campaigns to improve metrics like reducing customer attrition and increasing online conversion rates. Real-life examples were provided. Foreman emphasized starting with key metrics, testing offers and segments, and continually refining the process to transform data-driven insights into high-performing campaigns.
Organizations can gain valuable insights by consolidating their brand data into a single place and analyzing it on both a macro and micro level. This allows them to determine relationships between data points and use those insights to improve marketing, sales, and the user experience. Automating processes based on triggers and actions identified in the data analysis can help organizations more creatively reach and engage both individual fans and groups of fans.
[Infographic] The Modern Marketing Reality – Marketing Starts with a QuotaLenati
Now Marketing owns a quota to bring the right prospects in the door and deliver sales-ready leads that are more likely to convert. With this new reality of Modern Marketing, marketers are looking at how to measure their business impact with the goal of definitively proving Return on Marketing Investment (ROMI). Content is designed to fuel conversion from anonymous to known leads—first to attract, then with the support of marketing automation, to nurture prospects along their buyer journey. According to Forrester Research, sales is looking to marketing to increase the productivity of customer acquisition by targeting the right accounts and helping sales pursue them—finding smarter ways to prospect and build account-based strategies. With powerful marketing technologies, digital acumen and a little foresight you can get there, but you can’t get there alone.
All the technology in the world can’t help you in your quest to evolve into Modern Marketing unless you mind the golden rules, and in particular, make friends with sales. Sales and marketing now have a common goal to drive revenue. Sales and marketing are now speaking the same language and are motivated to assist each other in attaining their collective goals. The path from anonymous to qualified lead becomes a well-orchestrated dance where marketing goes deeper into driving engagements while sales becomes an active player in cultivating leads. The line between the two disciplines is blurred and both are responsible for bringing the right prospects in the door.
Through precise location analytics, retailers now can monitor the entire path to purchase. With this data, marketers better understand what led to the purchase providing the ability to move beyond the traditional blanketed “campaign” to a year-round interaction based on consumer behavior. Customers “opt-in” by mobile app to receive highly-targeted promotions, information about merchandise they may have “visited” but didn’t purchase, and discounts for major events – based on correlations like visits, dwell and intent – to drive sales like never before.
Data science employs techniques from many fields including mathematics, statistics, computer science, and information science. Data scientists use a scientific approach to analyze big data and identify patterns to predict future outcomes and guide decision making. Examples of data science applications include predicting flu outbreaks, ranking web pages, targeting online ads, and optimizing email campaigns. Data science skills in high demand include SQL, Python, R, machine learning, and statistical analysis.
No Revenue Left Behind: The Triple Threat to Maximizing eCommerce RevenueVe Interactive, US
Suffering from (cart) abandonment issues?
You’re not alone. Approximately $4.6 trillion worth of merchandise was abandoned in online shopping carts in 2016.
Watch this webinar to recover your piece of that $4.6 trillion.
Big Data, Big Thinking: Delight Your CustomersSAP Technology
How can you delight your customers using insight from Big Data?
In this webinar, Bastian Finkel, Director of Strategy and Corporate Development at hybris software and Manuel Sevilla, VP and CTO Global BIM, Capgemini, looked at how Big Data applications, powered by SAP HANA, can fuel real-time retail.
This document discusses how artificial intelligence is impacting marketing. It begins by defining artificial intelligence and how AI uses deep learning and data processing to perform tasks. AI is being used in marketing for data collection, analysis, and customized communications. The document then discusses how consumer behavior has changed with cognitive websites, increased customer loyalty through product recommendations, and changes in buying habits due to voice assistants. Businesses are changing their marketing strategies using AI, such as content-creating chatbots, find-and-replace tools, and asset recommendations. Examples are given of how Starbucks and Nike use AI for personalized experiences. The document concludes by discussing how COVID-19 will encourage more personalized shopping experiences using AI for marketing, targeting, and advertising.
SMBs can really scale and do smart businesses with getting instant/real-time insights from Big Data! Here are the reasons behind why SMBs love Big Data!
How Staples Bridged Analytics with Campaign ExecutionVivastream
Staples uses data analytics and insights to improve their marketing campaigns and reduce customer attrition. They analyze transactional, behavioral, and other customer data to understand customer patterns and predict future behaviors. Staples then uses these insights to execute targeted marketing campaigns through different channels. Their examples showed how predictive modeling of transaction data helped reduce attrition, and qualitative research and multi-channel analysis informed campaigns to increase online conversion rates and attachment rates in baskets.
Real-time Single Customer View
Create a single customer view of your prospects and customers with data from your website, mobile apps, social and phone calls. Use the out of the box dashboards to generate advanced and actionable insights based on your customer data.
Applying Data Science and Analytics in MarketingData Con LA
1) The document discusses applying data science in marketing to create data-driven marketing strategies through techniques like marketing mix modeling, predictive analytics, A/B testing, and attribution modeling.
2) Key benefits of these techniques include optimizing marketing budgets, identifying influential factors, improving customer experience, and tracking success metrics.
3) Building a data-driven marketing organization requires promoting data literacy, investing in technologies, embracing new technologies, and looking at data as an integral part of the business.
Big Data Done Right by Successful OrganizationsEuro IT Group
The document discusses how several large companies like Amazon, Mastercard, Walmart, and Caesars are successfully using big data analytics. It provides examples of how these companies analyze purchasing patterns, social media posts, and other customer data to improve recommendations, inventory management, and personalized customer experiences. The document also outlines how big data can be applied across various industries like retail, healthcare, telecommunications, and more.
Big data can be used in 5 practical ways for marketing and sales:
1. Know your perfect customers through detailed customer data to create targeted segments and campaigns.
2. Improve personalization by analyzing purchase history and preferences to deliver customized recommendations.
3. Analyze pricing models to understand effects on demand and set prices optimally.
4. Balance short and long-term marketing strategies using performance data from various channels.
5. Leverage in-store technologies like beacons and apps to enhance the customer experience.
The document discusses powering retail analytics through real-time data aggregation and analysis. It notes that demand-based management and delivering customer data through new technology will be key assets for retailers. The solution presented aggregates point of sale data in real-time from various retailers and stores to provide analytics and insights for customer engagement, management, and marketing decisions.
InsideView for Marketing gives you access to accurate company and contact data. Enrich inbound leads for faster lead-qualification and routing, build outbound prospect lists from our extensive database, and clean your existing marketing database. All resulting in more quality leads delivered, higher campaign response rates, and better sales close-win rates.
This document discusses analytics and retail analytics. It defines analytics as discovering patterns in data through statistics, programming, and research. Retail analytics specifically aims to improve customer loyalty and sales. It does this by identifying valuable customers, understanding their preferences, and creating personalized shopping experiences through offers targeted to individual needs. Retailers can gather customer data through in-store and online analytics to gain insights that optimize performance.
Digital marketing has evolved from outbound campaigns to include inbound channels and predictive analytics. As data and devices proliferate, marketers must deal with huge amounts of structured and unstructured data in real-time to trigger personalized campaigns. Emerging technologies like natural language processing, big data analytics, machine learning, and the Internet of Things will enable predictive and prescriptive marketing by detecting events and pushing customized content through various devices. This will mark the beginning of Digital Marketing 2.0 and an "anytime, anywhere" marketing paradigm driven by real-time prediction and recommendations.
1) The document discusses how mid-sized companies can maximize the value of their customer data through data economics and mining big data. It provides questions for companies to assess their use of data and identifies challenges.
2) It recommends finding a data expert who can integrate a company's online and offline customer data and use external sources to better understand customers.
3) Answering the questions can help companies measure how effectively they use data and identify areas for improvement to increase marketing results.
Social media analytics powered by data scienceNavin Manaswi
The document discusses social media analytics using big data and data science. It begins by defining social media and its importance for businesses, as well as big data analytics. It then explains how data science can be leveraged in social media analytics to gain powerful insights through techniques like sentiment analysis, social network analysis, and identifying top influencers. Specific use cases are presented for industries like finance and opportunities discussed for applying these techniques globally. Examples are provided of analyzing social media data for companies like banks and Legoland park.
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
AtScale's marketing team partnered with Metadata to improve their lead generation efforts on social media. Using Metadata's Account Based Marketing managed services, Metadata analyzed AtScale's Salesforce data to generate look-alike audiences and build targeted social media campaigns on LinkedIn and Facebook. This resulted in a 200% increase in marketing qualified leads for AtScale over their previous efforts, with 81% of the leads being a direct match to their target audience. The director of demand generation at AtScale was pleased with the results and cost-effectiveness of the Metadata solution.
How Staples Bridged Analytics with Campaign ExecutionVivastream
Jim Foreman of Staples discussed how the company bridges analytics with campaign execution. Staples generates large amounts of customer data from transactions, surveys, and online behavior. They use exploratory, reactive, and predictive analytics on this data to gain insights. Insights are then applied to outbound and inbound marketing campaigns to improve metrics like reducing customer attrition and increasing online conversion rates. Real-life examples were provided. Foreman emphasized starting with key metrics, testing offers and segments, and continually refining the process to transform data-driven insights into high-performing campaigns.
Organizations can gain valuable insights by consolidating their brand data into a single place and analyzing it on both a macro and micro level. This allows them to determine relationships between data points and use those insights to improve marketing, sales, and the user experience. Automating processes based on triggers and actions identified in the data analysis can help organizations more creatively reach and engage both individual fans and groups of fans.
[Infographic] The Modern Marketing Reality – Marketing Starts with a QuotaLenati
Now Marketing owns a quota to bring the right prospects in the door and deliver sales-ready leads that are more likely to convert. With this new reality of Modern Marketing, marketers are looking at how to measure their business impact with the goal of definitively proving Return on Marketing Investment (ROMI). Content is designed to fuel conversion from anonymous to known leads—first to attract, then with the support of marketing automation, to nurture prospects along their buyer journey. According to Forrester Research, sales is looking to marketing to increase the productivity of customer acquisition by targeting the right accounts and helping sales pursue them—finding smarter ways to prospect and build account-based strategies. With powerful marketing technologies, digital acumen and a little foresight you can get there, but you can’t get there alone.
All the technology in the world can’t help you in your quest to evolve into Modern Marketing unless you mind the golden rules, and in particular, make friends with sales. Sales and marketing now have a common goal to drive revenue. Sales and marketing are now speaking the same language and are motivated to assist each other in attaining their collective goals. The path from anonymous to qualified lead becomes a well-orchestrated dance where marketing goes deeper into driving engagements while sales becomes an active player in cultivating leads. The line between the two disciplines is blurred and both are responsible for bringing the right prospects in the door.
Through precise location analytics, retailers now can monitor the entire path to purchase. With this data, marketers better understand what led to the purchase providing the ability to move beyond the traditional blanketed “campaign” to a year-round interaction based on consumer behavior. Customers “opt-in” by mobile app to receive highly-targeted promotions, information about merchandise they may have “visited” but didn’t purchase, and discounts for major events – based on correlations like visits, dwell and intent – to drive sales like never before.
Data science employs techniques from many fields including mathematics, statistics, computer science, and information science. Data scientists use a scientific approach to analyze big data and identify patterns to predict future outcomes and guide decision making. Examples of data science applications include predicting flu outbreaks, ranking web pages, targeting online ads, and optimizing email campaigns. Data science skills in high demand include SQL, Python, R, machine learning, and statistical analysis.
No Revenue Left Behind: The Triple Threat to Maximizing eCommerce RevenueVe Interactive, US
Suffering from (cart) abandonment issues?
You’re not alone. Approximately $4.6 trillion worth of merchandise was abandoned in online shopping carts in 2016.
Watch this webinar to recover your piece of that $4.6 trillion.
Big Data, Big Thinking: Delight Your CustomersSAP Technology
How can you delight your customers using insight from Big Data?
In this webinar, Bastian Finkel, Director of Strategy and Corporate Development at hybris software and Manuel Sevilla, VP and CTO Global BIM, Capgemini, looked at how Big Data applications, powered by SAP HANA, can fuel real-time retail.
This document discusses how artificial intelligence is impacting marketing. It begins by defining artificial intelligence and how AI uses deep learning and data processing to perform tasks. AI is being used in marketing for data collection, analysis, and customized communications. The document then discusses how consumer behavior has changed with cognitive websites, increased customer loyalty through product recommendations, and changes in buying habits due to voice assistants. Businesses are changing their marketing strategies using AI, such as content-creating chatbots, find-and-replace tools, and asset recommendations. Examples are given of how Starbucks and Nike use AI for personalized experiences. The document concludes by discussing how COVID-19 will encourage more personalized shopping experiences using AI for marketing, targeting, and advertising.
SMBs can really scale and do smart businesses with getting instant/real-time insights from Big Data! Here are the reasons behind why SMBs love Big Data!
How Staples Bridged Analytics with Campaign ExecutionVivastream
Staples uses data analytics and insights to improve their marketing campaigns and reduce customer attrition. They analyze transactional, behavioral, and other customer data to understand customer patterns and predict future behaviors. Staples then uses these insights to execute targeted marketing campaigns through different channels. Their examples showed how predictive modeling of transaction data helped reduce attrition, and qualitative research and multi-channel analysis informed campaigns to increase online conversion rates and attachment rates in baskets.
Real-time Single Customer View
Create a single customer view of your prospects and customers with data from your website, mobile apps, social and phone calls. Use the out of the box dashboards to generate advanced and actionable insights based on your customer data.
Applying Data Science and Analytics in MarketingData Con LA
1) The document discusses applying data science in marketing to create data-driven marketing strategies through techniques like marketing mix modeling, predictive analytics, A/B testing, and attribution modeling.
2) Key benefits of these techniques include optimizing marketing budgets, identifying influential factors, improving customer experience, and tracking success metrics.
3) Building a data-driven marketing organization requires promoting data literacy, investing in technologies, embracing new technologies, and looking at data as an integral part of the business.
Big Data Done Right by Successful OrganizationsEuro IT Group
The document discusses how several large companies like Amazon, Mastercard, Walmart, and Caesars are successfully using big data analytics. It provides examples of how these companies analyze purchasing patterns, social media posts, and other customer data to improve recommendations, inventory management, and personalized customer experiences. The document also outlines how big data can be applied across various industries like retail, healthcare, telecommunications, and more.
Big data can be used in 5 practical ways for marketing and sales:
1. Know your perfect customers through detailed customer data to create targeted segments and campaigns.
2. Improve personalization by analyzing purchase history and preferences to deliver customized recommendations.
3. Analyze pricing models to understand effects on demand and set prices optimally.
4. Balance short and long-term marketing strategies using performance data from various channels.
5. Leverage in-store technologies like beacons and apps to enhance the customer experience.
Big data can be used in 5 practical ways for marketing and sales:
1. Know your perfect customers through analyzing available data on customer demographics, spending habits, and behaviors.
2. Improve personalization by analyzing purchase data to provide customized recommendations, coupons, and messages.
3. Create the best pricing models by using data to visualize how price changes affect demand and profit.
4. Optimize marketing across channels by balancing strategies based on past campaign performance data.
5. Enhance the in-store experience with apps, beacons, and loyalty programs tailored from customer data.
Data has the potential to be your most valuable marketing resource. With the right data in hand, you can create scalable, repeatable marketing processes. Learn the essentials of data-driven marketing here.
1. The document outlines strategies for making data actionable, including developing a big data strategy, setting milestones, identifying relevant data sources, and using data to create organizational efficiencies.
2. It emphasizes focusing marketing and data use on engagement, lead generation, and utilization metrics. Data should be used to move from broad approaches like blast marketing to more targeted segmented marketing.
3. Key steps include gathering, organizing, and identifying relevant data, then using data to track activities and outcomes, store results for future use, and continuously tweak marketing based on lessons learned.
First Session - Kickstart Career as Data Analyst presents the definition of data, 5 parameters of big data, why many companies today need data, and different data-related jobs including data engineer, data analyst, and data scientist.
PresentationThe capability of enormous information - or the new .pdfaradhana9856
Presentation
The capability of enormous information - or \"the new oil,\" as a few CIOs and industry
specialists have named it - appears as perpetual as it is subtle. Huge information battles are in
their early stages, with endeavors of all stripes making sense of how to utilize new, old,
unstructured and outer information to make a focused procedure.
Despite the fact that the standard procedures for get-together information and investigating its
value are as yet coming to fruition, organizations know they have to get in the diversion. They
are gathering and mining information on clients, workers, market flow, the climate, and so on,
with instruments going from conventional business insight (BI) frameworks to more trial ones,
for example, geospatial and constant versatile following innovations, online networking
investigation and NoSQL databases.
SearchCIO isn\'t remaining on the sidelines, either. Our Essential Guide on enormous
information incorporates a preliminary for beginning with information social affair and
investigation, true contextual investigations from the CIO and business viewpoints, tips on the
best way to beat hindrances experienced by the huge information pioneers, and expectations on
the following huge information boondocks and what it implies for aggressive methodology.
This aide on the development of huge information is a piece of SearchCIO\'s CIO Briefings
arrangement, which is intended to give IT pioneers vital administration and basic leadership
guidance on opportune themes.
The most effective method to Collect Big Data ?
1 year agoby Ayush1 Comment
The most effective method to Collect Big Data ? : Yes we knoe you would have various inquiries
in your psyche like Collection of Big Data, How organizations gather Big Data, how to gather
information for quantitative research so don\'t stress, in the event that you are here to scan for
these inquiries here then you are on the right website page as here we are going to give you a
complete article on Collection of Big Data techniques quickly.
Astounding Facts about Rise of Big Data Collectection
Consistently buyers make around 11.5 million installments by utilizing Paypal
Consistently, Walmart (chain of rebate retail chains) handles more than 1 million client
exchanges
510 remarks, 293000 status and 136000 overhauls are posted on Facebook consistently
Consistently, ~7000 tweets are made on Twitter
Simply picture the measure of information created if the above details are figured for 24 hours?
Whoa! That is huge.
The term \'Enormous Data\' is ordinarily connected with 4V\'s to be specific, Velocity, Volume,
Variety, Veracity. These 4V\'s appropriately speaks to the genuine way of Big Data. Each \"V\"
has a noteworthy part to play in the presence of Big Data. On the off chance that consolidated,
these 4V paints a wonderful clarification of Big Data which can be comprehended as \" Big Data
as an idea alludes to high speed gathering of information in expansive volumes which radia.
Data & Marketing Analytics Theatre; Long time no see: using predictive modell...TFM&A
The document discusses using predictive modeling to reactivate long lapsed charity donors. It defines long lapsed as those who last donated over 5 years ago. The case study looks at a campaign by Barnardo's, a UK charity helping vulnerable children, to reactivate lapsed donors through direct mail. Barnardo's data was analyzed using a marketing database to identify and score lapsed donors most likely to donate. The campaign aimed to generate cash donations from this reactivated audience.
Mid sized companies dont have sensors and machines spitting out big data, but they have lots of data from traditional sources like CRM, ERP and Billing software. How do you use this. Some techniques, examples and case studies from other industries to get your creatives flowing
The document discusses analytics with big data, describing how businesses are using analytics to gain insights from large datasets. It provides examples of common business questions and the types of analytics that can help answer them, such as forecasting, recommendations, and predictive modeling. The document also introduces Robust Designs, a software company that specializes in business intelligence solutions using their CUBOT product.
Data & Marketing Analytics Theatre; Long time no see: using predictive modell...TFM&A
The document discusses using predictive modeling to reactivate long lapsed charity donors. It defines a long lapsed donor as someone who last donated over 5 years ago. The case study looks at a campaign by Barnardo's, a UK charity helping vulnerable children, to reactivate long lapsed donors through direct mail. Barnardo's data was analyzed using a database marketing tool to identify and score donors likely to give a cash donation. The campaign aimed to warm these lapsed supporters and increase donations to help feed children in need.
HighRoad Solution Session at AUC016-Creating the Insight-Driven Content Marke...HighRoad Solution
Outline the approach undertaken by the American Association of Clinical Chemistry, American Payroll Association & the American Dental Association in transforming their email program by moving to an insight-first driven model.
Effective Business Practices 101 (5/8): Power Your Business With InformationDmitri Tcherbadji
This deck is a part of an eight-day introductory course that I originally designed for the residents of Inle Lake (Nyang Shwe), Myanmar during my volunteer work with Partnership for Change org. This is a basic introductory course for those who wish to start a businesses but aren't sure where to begin or what would be an effective way to run and operate a company geared for Western customers.
This deck is free for anyone to modify and use, but please keep in mind that I do not own copyrights for most of the images on those slides (with some exceptions).
Network Conference LMS Big Data Final 1.24.14LMSmith361
This document discusses how non-profits can leverage big data and analytics to improve fundraising. It begins by providing background on big data, defining it as vast volumes of unstructured and fast-moving data from many sources. It then discusses how big data is being used by large companies like UPS and IBM to optimize operations and make data-driven decisions. While non-profits currently rely mostly on smaller, structured data, the document advocates for creatively using even small amounts of data to personalize communications and engage donors across multiple channels. It outlines strategies non-profits can take to clean up data, understand donor behaviors and relationships, target younger audiences, and optimize fundraising efforts over the long term.
Marketing analytics uses sophisticated data analysis to better understand customers and markets. Companies collect massive amounts of data on customer purchases and behaviors to improve customer experience and gain valuable insights. Effective use of analytics can adjust marketing plans, product placement, and mission statements based on consumer information gathered from social media, loyalty programs, and mobile apps. When companies develop digital strategies that provide value to consumers in exchange for their data, such as making shopping more convenient, both parties can benefit.
How to use the power of data in e-Commerce? Applying the Big Data solutions makes it possible to analyse data in real time. This allows us to use the data not for reports only, but to translate them into action.
Barry Ooi presented on big data analytics in marketing. He discussed what big data is, how it is characterized by volume, variety, velocity and veracity. He provided examples of how companies like Hippo Snacks and Tesco have successfully used big data analytics in marketing. Food Genius was also discussed as an example of a company that collects and integrates restaurant data to provide insights to food marketers.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
3. Robust Data
Who am I? I coordinate data for integrated marketing
packages.
• Track health of packages through various methods
• Internal KPIs
• Google Analytics (Traffic, Content, Audience)
• Hubspot (List and data management)
In short, I am a data super hero. Like Trinity.
5. Robust Data
Data tells a story.
• It tells you how your digital kingdom operates, If
you know what to look for.
• It tells you where people go, what they like to use,
what they like to see.
• And you help build their experience, nudging them
where you think they might like.
6. Robust Data
Definitions & Clarifications
• CRM – Refers to Customer Relationship Management
• Salesforce, Hubspot
• Analytics – Information resulting from systematic
analysis of data
• Google Analytics, Abobe Omniture
7. Robust Data
CRM data
• Hubspot
• Captures user data through form conversion
• Subscription list, gated content, shopping cart
(account set up)
• Cookies users and provides information based on
user interaction
• Once users convert, you collect better data on
user engagement
8. Robust Data
Google Analytics
• Implementation of basic analytics provides wholesale,
anonymous data on users
• How people find you, where they come from, what
they touch, and their journey through your site
• More advanced – Google Tag Manager
• Provides a robust tagging system to track how users
interact with content, campaigns
• eCommerce solutions
9. Robust Data
Real life example
Let’s say your website is like a grocery store.
• What would your analytics tell you?
• What would your CRM tell you?
• Big data v. Small Data
10. Robust Data
Google Analytics – Store data
• How many patrons? When they visit?
• Where your patrons come from: city, state,
country
• How they get to your store: bus, car, walk, Uber
• What aisles they walk down, what products they
look at, what items they buy
• This is BIG data
11. Robust Data
Hubspot – Store data
• Diane lives in Philadelphia, works in digital marketing,
prefers self-checkout
• She is more likely to buy fruits and vegetables on sale than
use coupons for processed food
• She has considered buying Lipton tea but prefers Tazo
English breakfast, will buy generic Splenda
• This is SMALL data
Diane: Buys Tazo tea, Noosa Yogurt, Prefers 18
pack of eggs, spends an average of $65 per week
12. Robust Data
Benefits of bringing these two together:
• Adjust funnels
• Nudge users
• Adjust your overall digital strategy
• Determine your retention rate, comparing New V.
Returning, with recency of visits of users in CRM