The big-data explosion is driving a shift away from gut-based decision making and marketing in particular is feeling the pressure to embrace new data driven capabilities.
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
Making advanced analytics work for youYogesh Kumar
Big data and analytics is becoming increasingly important for corporations. Many top companies like Google and Amazon use business models fueled by big data to gain a competitive advantage. Research also shows companies that effectively use big data have productivity and profitability rates 5-6% higher than peers. However, some leaders are still cautious about using big data, due to inability to fully utilize existing analytics programs or prior experiences not meeting expectations. To successfully use big data, organizations need three key capabilities: collecting and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models can improve decision making.
These are my insights on the article "Making Advanced Analytics Work for You" by Dominic Barton and David Court. This is an assignment, part of data analytics internship
This document discusses the importance of business transformation from just managing data to achieving digital agility. It notes that digital disruption is happening more rapidly as new trends emerge and start-ups create disruptive business models. To succeed, businesses need to shift power to customers, change business operations models, and drive organizational change. Data-driven insights and analytics are also key drivers, and the volume of data is exploding. To win, businesses need organizational agility like start-ups through a digital vision and the ability to change and innovate quickly.
Big data analytics can provide valuable insights for small and medium enterprises (SMEs). While SMEs have less data than larger corporations, analytics can determine relevance within available data and help SMEs make strategic decisions, improve operations, enable faster analysis, and gain competitive advantages. Cloud computing provides a cost-effective way for SMEs to access big data tools without large upfront investments. Choosing the right analytics framework and solutions tailored for SMEs' needs, along with proper resources and organizational alignment, are keys to success with big data projects.
Big data has transformed many businesses, though some companies remain wary of investing heavily in it. To successfully use analytics, companies must follow three steps: source and manage multiple data sources, build advanced analytics models to predict and optimize outcomes, and transform the organization so data and models guide better decisions. Specifically, companies should identify usable existing data and new sources, develop simple yet powerful analytics tools, and train employees to make data-driven decisions. When done right, analytics can optimize performance if grounded in business needs and practical data relationships.
The document discusses how marketers struggle with using big data effectively to make decisions. While data-driven decision making is important, many marketers rely too heavily on gut feelings instead of properly analyzing and interpreting data. The best marketers are able to filter out noise by focusing on higher-level goals, being comfortable with ambiguity, and asking strategic questions of the data. To improve, marketing leaders should constantly reiterate business goals, teach marketers to center decisions around data, and help them avoid common mistakes in data interpretation.
Making advanced analytics work for you.
Big data and analytics have rocketed to the top of the corporate agenda. Executives look with admiration at how Google, Amazon, and others have eclipsed competitors with powerful new business models that derive from an ability to exploit data....
Making advanced analytics work for youYogesh Kumar
Big data and analytics is becoming increasingly important for corporations. Many top companies like Google and Amazon use business models fueled by big data to gain a competitive advantage. Research also shows companies that effectively use big data have productivity and profitability rates 5-6% higher than peers. However, some leaders are still cautious about using big data, due to inability to fully utilize existing analytics programs or prior experiences not meeting expectations. To successfully use big data, organizations need three key capabilities: collecting and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models can improve decision making.
These are my insights on the article "Making Advanced Analytics Work for You" by Dominic Barton and David Court. This is an assignment, part of data analytics internship
This document discusses the importance of business transformation from just managing data to achieving digital agility. It notes that digital disruption is happening more rapidly as new trends emerge and start-ups create disruptive business models. To succeed, businesses need to shift power to customers, change business operations models, and drive organizational change. Data-driven insights and analytics are also key drivers, and the volume of data is exploding. To win, businesses need organizational agility like start-ups through a digital vision and the ability to change and innovate quickly.
Big data analytics can provide valuable insights for small and medium enterprises (SMEs). While SMEs have less data than larger corporations, analytics can determine relevance within available data and help SMEs make strategic decisions, improve operations, enable faster analysis, and gain competitive advantages. Cloud computing provides a cost-effective way for SMEs to access big data tools without large upfront investments. Choosing the right analytics framework and solutions tailored for SMEs' needs, along with proper resources and organizational alignment, are keys to success with big data projects.
Big data has transformed many businesses, though some companies remain wary of investing heavily in it. To successfully use analytics, companies must follow three steps: source and manage multiple data sources, build advanced analytics models to predict and optimize outcomes, and transform the organization so data and models guide better decisions. Specifically, companies should identify usable existing data and new sources, develop simple yet powerful analytics tools, and train employees to make data-driven decisions. When done right, analytics can optimize performance if grounded in business needs and practical data relationships.
The document discusses how marketers struggle with using big data effectively to make decisions. While data-driven decision making is important, many marketers rely too heavily on gut feelings instead of properly analyzing and interpreting data. The best marketers are able to filter out noise by focusing on higher-level goals, being comfortable with ambiguity, and asking strategic questions of the data. To improve, marketing leaders should constantly reiterate business goals, teach marketers to center decisions around data, and help them avoid common mistakes in data interpretation.
What do we do with all this big data- A ted talk Vyshnavi Veluri
Big data provides insights if analyzed properly, but static facts alone are not enough. Big data should continue expanding with deeper data in multiple metrics to ensure precision and relevance. While big data can manipulate numbers for business, it also has applications improving humanity, health, communities and the environment. Managers must carefully select analysts who can make meaningful insights from large amounts of irrelevant data, prioritizing critical thinking. For managers in India, continually analyzing feedback from big data can help tailor products and services to keep customers happy while accounting for the country's diversity in dynamic facts.
Making advanced analytics work for youRahul Chawla
This document outlines how companies can benefit from advanced analytics and big data. It discusses 3 key steps: 1) choosing the right data sources, 2) building models to predict and optimize business outcomes, and 3) transforming company capabilities. Specifically, it emphasizes sourcing data creatively to solve problems, using new technologies to access data, focusing models on business opportunities, developing analytics that can be used, embedding analytics in frontline tools, and upgrading analytical skills. The document argues that with the right approach, big data can significantly boost company performance and competitive advantage.
Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
Oceans of big data: Take the plunge or wade in slowly?Deloitte Canada
In a recent study, Deloitte identified some of the hurdles that keep organizations from making greater use of business analytics. These include poor technology infrastructure, the quality and amount of data being collected and leadership that may not support or even understand the use of analytics.
This presentation defines big data, explains why you should care about big data, and suggests when big data should be used. The potential of big data is immense, but it can also become an expensive distraction. Once you remove constraints on the size, type, source and complexity of useful data, you can ask the ‘crunchy’ questions that are critical to the success of your business.
How can you analyze data in fragile and conflict affected states? What happens if you ignore the analytics and move on gut feeling? Read more about the three key steps for better data analytics in difficult places.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
#MITXData "Leveraging Data and Analytics for Your Marketing Strategy" present...MITX
-Jesse Harriott, Ph.D., Chief Analytics Officer, Constant Contact
-Dave Krupinksi, Co-Founder & Chief Technology Officer, Care.com
You may remember the days before the Web, social media, mobile, and Big Data. Instinct was a prized business characteristic and it, rather than data, drove many corporate marketing decisions.Companies now say that they are "data-driven" and only make quantitative marketing decisions. But these same companies are also overwhelmed by the sheer volume of data at their disposal and how to best analyze it to shape critical marketing questions. The issue today is not the lack of data, but rather how to prioritize, access, and use data in real time so it has the greatest impact on your business.
During this opening keynote, two top analytic leaders from major brands, Constant Contact and Care.com, will share best practices and proven strategies for incorporating analytics into your marketing strategy. Join Jesse Harriott, Chief Analytics Officer at Constant Contact, and Dave Krupinski, Co-founder and Chief Technology Officer at Care.com, as they discuss strategies to leverage data and analytics tools to inform marketing decisions and realize substantial ROI.
Analysis of stop searching for that elusive data scientist by michael schrageDarpan Deoghare
The document summarizes an analysis of the shortage of data scientists. It notes that while large organizations try to hire expensive experts, more prudent enterprises are taking alternate approaches like empowering small cross-functional data teams. These teams should be charged with delivering measurable benefits quickly by building data capabilities rather than infrastructure. The document advises managers to consider metrics that prevent organizational downfalls and to help employees understand data science principles to create a context for future hiring, rather than trying to find a perfect data scientist.
Stop searching for that elusive data scientistYogita Bansal
Companies are increasingly seeking data scientists to drive data-based decision making, but there is a lack of qualified candidates. To address this, companies should build effective teams by coordinating existing resources, promoting a data-focused culture, and encouraging all members to contribute insights from available data. Even small groups can draw meaningful conclusions and make informed decisions by maximizing their current capabilities.
This document discusses the role of information systems in organizations and opportunities for their use. It outlines objectives like understanding a company's value chain and competing globally. Information systems can play roles in e-procurement, e-marketing, e-sales, e-learning, and e-supply chains. For companies like NDPL, information systems can help compete with stockists, enable modern retailing, meet sales team expectations, and improve operational efficiency, though challenges implementing information systems exist. The document poses assignment questions about Gowlings' international expansion plans.
The document discusses making advanced analytics work for companies. It provides guidance on choosing the right data, building models that predict and optimize business outcomes, and transforming a company's capabilities. It emphasizes starting with identifying a business opportunity and determining how analytics can improve performance. While big data can solve companies' problems, organizational change is needed to fully exploit analytics capabilities. Research shows companies using big data and analytics have 5-6% higher productivity and profitability than their peers.
From Insight to Impact (Chicago Summit - Keynote)Open Analytics
This document discusses five critical pillars for the success of analytics and data science projects: 1) align with corporate strategy, 2) ignite stakeholder engagement, 3) sharpen team focus, 4) drive change management, and 5) recruit key talent. It provides guidance on each pillar, such as prioritizing analytics opportunities by their impact and horizon, understanding stakeholder incentives, avoiding "zombie" projects, enabling experiments to drive change, and pre-screening talent for technical skills and culture fit. Following these pillars can help organizations improve analytics project success rates and better compete through data-driven insights.
Making the Most of Big Data Through Technology and Organizational DesignJason Wilson
This document discusses how increased technology and data availability has led to the rise of "Big Data". It presents findings from interviews and surveys with business leaders on their relationship with data, investment in technology, and organizational empowerment. The key findings are that a minimum investment in technology is needed to make data-driven decisions, increased investment leads to more data insights, and organizational design has a strong impact on the ability to make data-driven decisions. The major barrier to fully utilizing data is organizational structure, not lack of technology.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
The big-data explosion is driving a shift away from gut-based decision making. Marketing, in particular, is feeling the pressure to embrace new data-driven customer intelligence capabilities.
Marketers working 70-80 hours a week is not a great thing to hear.
But the requirement for them to have such a large amount of work time causes problems in the data selection and filtering.
Hence many marketers flunk the big data test
Big Data - Bridging Technology and HumansMark Laurance
The document discusses big data and how organizations can leverage it. It defines big data and notes the rapid growth in data. It outlines five ways big data can create value for organizations, including making information more transparent and usable, improving performance through data collection, narrow customer segmentation, improved decision making, and better product development. The document also warns of a potential shortage of analytics talent as organizations seek to take advantage of big data.
What do we do with all this big data- A ted talk Vyshnavi Veluri
Big data provides insights if analyzed properly, but static facts alone are not enough. Big data should continue expanding with deeper data in multiple metrics to ensure precision and relevance. While big data can manipulate numbers for business, it also has applications improving humanity, health, communities and the environment. Managers must carefully select analysts who can make meaningful insights from large amounts of irrelevant data, prioritizing critical thinking. For managers in India, continually analyzing feedback from big data can help tailor products and services to keep customers happy while accounting for the country's diversity in dynamic facts.
Making advanced analytics work for youRahul Chawla
This document outlines how companies can benefit from advanced analytics and big data. It discusses 3 key steps: 1) choosing the right data sources, 2) building models to predict and optimize business outcomes, and 3) transforming company capabilities. Specifically, it emphasizes sourcing data creatively to solve problems, using new technologies to access data, focusing models on business opportunities, developing analytics that can be used, embedding analytics in frontline tools, and upgrading analytical skills. The document argues that with the right approach, big data can significantly boost company performance and competitive advantage.
Making Advanced Analytics Work for You by Dominic Barton and David Court MohitGupta714
The document discusses how advanced analytics can provide competitive advantages but many companies are unsure how to implement them effectively. It identifies that fully exploiting analytics requires the ability to identify and manage multiple data sources, build advanced analytics models, and adapt the organization. Companies must have a clear strategy for using data to compete and the right technology architecture. Managers need to focus on sourcing data, building models, transforming culture with flexibility and promoting creativity around new data sources.
Oceans of big data: Take the plunge or wade in slowly?Deloitte Canada
In a recent study, Deloitte identified some of the hurdles that keep organizations from making greater use of business analytics. These include poor technology infrastructure, the quality and amount of data being collected and leadership that may not support or even understand the use of analytics.
This presentation defines big data, explains why you should care about big data, and suggests when big data should be used. The potential of big data is immense, but it can also become an expensive distraction. Once you remove constraints on the size, type, source and complexity of useful data, you can ask the ‘crunchy’ questions that are critical to the success of your business.
How can you analyze data in fragile and conflict affected states? What happens if you ignore the analytics and move on gut feeling? Read more about the three key steps for better data analytics in difficult places.
Data Analytics with Managerial Applications InternshipJahanvi Khedwal
This document discusses how companies can make advanced analytics work for them. It identifies three key capabilities: 1) Choosing the right data, including both internal and external sources, and asking how available data can help key decisions. 2) Building predictive models that optimize outcomes simply, focusing on the least complex model that improves performance. 3) Transforming company capabilities by embedding analytics in tools for front-line use and making analytics central to daily operations.
#MITXData "Leveraging Data and Analytics for Your Marketing Strategy" present...MITX
-Jesse Harriott, Ph.D., Chief Analytics Officer, Constant Contact
-Dave Krupinksi, Co-Founder & Chief Technology Officer, Care.com
You may remember the days before the Web, social media, mobile, and Big Data. Instinct was a prized business characteristic and it, rather than data, drove many corporate marketing decisions.Companies now say that they are "data-driven" and only make quantitative marketing decisions. But these same companies are also overwhelmed by the sheer volume of data at their disposal and how to best analyze it to shape critical marketing questions. The issue today is not the lack of data, but rather how to prioritize, access, and use data in real time so it has the greatest impact on your business.
During this opening keynote, two top analytic leaders from major brands, Constant Contact and Care.com, will share best practices and proven strategies for incorporating analytics into your marketing strategy. Join Jesse Harriott, Chief Analytics Officer at Constant Contact, and Dave Krupinski, Co-founder and Chief Technology Officer at Care.com, as they discuss strategies to leverage data and analytics tools to inform marketing decisions and realize substantial ROI.
Analysis of stop searching for that elusive data scientist by michael schrageDarpan Deoghare
The document summarizes an analysis of the shortage of data scientists. It notes that while large organizations try to hire expensive experts, more prudent enterprises are taking alternate approaches like empowering small cross-functional data teams. These teams should be charged with delivering measurable benefits quickly by building data capabilities rather than infrastructure. The document advises managers to consider metrics that prevent organizational downfalls and to help employees understand data science principles to create a context for future hiring, rather than trying to find a perfect data scientist.
Stop searching for that elusive data scientistYogita Bansal
Companies are increasingly seeking data scientists to drive data-based decision making, but there is a lack of qualified candidates. To address this, companies should build effective teams by coordinating existing resources, promoting a data-focused culture, and encouraging all members to contribute insights from available data. Even small groups can draw meaningful conclusions and make informed decisions by maximizing their current capabilities.
This document discusses the role of information systems in organizations and opportunities for their use. It outlines objectives like understanding a company's value chain and competing globally. Information systems can play roles in e-procurement, e-marketing, e-sales, e-learning, and e-supply chains. For companies like NDPL, information systems can help compete with stockists, enable modern retailing, meet sales team expectations, and improve operational efficiency, though challenges implementing information systems exist. The document poses assignment questions about Gowlings' international expansion plans.
The document discusses making advanced analytics work for companies. It provides guidance on choosing the right data, building models that predict and optimize business outcomes, and transforming a company's capabilities. It emphasizes starting with identifying a business opportunity and determining how analytics can improve performance. While big data can solve companies' problems, organizational change is needed to fully exploit analytics capabilities. Research shows companies using big data and analytics have 5-6% higher productivity and profitability than their peers.
From Insight to Impact (Chicago Summit - Keynote)Open Analytics
This document discusses five critical pillars for the success of analytics and data science projects: 1) align with corporate strategy, 2) ignite stakeholder engagement, 3) sharpen team focus, 4) drive change management, and 5) recruit key talent. It provides guidance on each pillar, such as prioritizing analytics opportunities by their impact and horizon, understanding stakeholder incentives, avoiding "zombie" projects, enabling experiments to drive change, and pre-screening talent for technical skills and culture fit. Following these pillars can help organizations improve analytics project success rates and better compete through data-driven insights.
Making the Most of Big Data Through Technology and Organizational DesignJason Wilson
This document discusses how increased technology and data availability has led to the rise of "Big Data". It presents findings from interviews and surveys with business leaders on their relationship with data, investment in technology, and organizational empowerment. The key findings are that a minimum investment in technology is needed to make data-driven decisions, increased investment leads to more data insights, and organizational design has a strong impact on the ability to make data-driven decisions. The major barrier to fully utilizing data is organizational structure, not lack of technology.
Deep Neural Networks (DNN), or simply Deep Learning (DL), took Artificial Intelligence (AI) by storm and have infiltrated into business at an unprecedented rate. Access to vast amounts of data, recently made available by the Big Data revolution, extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges.
The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. However, incorporating this technology in established business is far from obvious: cultural inertia in organizations, lack of transparency in most DL models and the complexity in training these models are some of the issues that will be addressed.
This document discusses how companies can make advanced analytics work for them. It notes that while big data is attracting investment, most companies are unsure how to implement it. It recommends that companies 1) choose the right data sources, 2) build models that predict and optimize business outcomes, and 3) transform their capabilities to develop analytics that managers understand and can use daily. The key is aligning analytics with business goals and processes rather than just focusing on data itself.
According to research, companies that effectively use big data and analytics show 5-6% higher productivity and profitability than their peers. To do this requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models improve decisions. The document provides recommendations for choosing usable data sources, establishing supportive IT infrastructure, building models to optimize business outcomes, and transforming company capabilities to develop and utilize analytics. Overall, developing capabilities around big data may become a key competitive advantage.
The big-data explosion is driving a shift away from gut-based decision making. Marketing, in particular, is feeling the pressure to embrace new data-driven customer intelligence capabilities.
Marketers working 70-80 hours a week is not a great thing to hear.
But the requirement for them to have such a large amount of work time causes problems in the data selection and filtering.
Hence many marketers flunk the big data test
Big Data - Bridging Technology and HumansMark Laurance
The document discusses big data and how organizations can leverage it. It defines big data and notes the rapid growth in data. It outlines five ways big data can create value for organizations, including making information more transparent and usable, improving performance through data collection, narrow customer segmentation, improved decision making, and better product development. The document also warns of a potential shortage of analytics talent as organizations seek to take advantage of big data.
The document discusses handling and processing big data. It begins by defining big data and explaining why it is important for companies to analyze big data. It then discusses several techniques for handling big data, including establishing goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. The document also covers preprocessing big data by cleaning, integrating, reducing, and discretizing data. It provides a case study of preprocessing government agency data and discusses advanced tools and techniques for working with big data.
The presentation includes the introduction to the topic, the various dimensions of big data, its evolution from big data 1.0 to bid data 3.0 and its impact on various industries, uses as well as the challenges it faces. The concluding slide gives a brief on the future of big data.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
This whitepaper aims to assist Chief Data Officers in promoting a data-driven culture at their
organization, helping them lead the enterprise on a digital transformation journey backed by
analytical insights.
The Future Of Big Data In Business – 4 Emerging Trends In 2022.pptxArpitGautam20
Here are some exciting trends that will dictate the future of Big Data in Business in 2022. Read on to know about these exciting developments. https://arsr.tech/the-future-of-big-data-in-business-4-emerging-trends-in-2022/
This document discusses securing big data as it travels and is analyzed. It outlines some of the key challenges organizations face with big data including increasing volumes of data from various sources, managing data privacy, and optimizing return on investment from big data analytics. Effective data governance is important for managing data as an asset and meeting regulatory compliance. However, many companies struggle with data governance due to short-term priorities and political issues. An iterative approach focusing on specific data sets can help companies start seeing results more quickly from data governance.
Business use cases highlight how big data can drive organizations towards tangible results. These use cases are practical points of reference that emphasize why (and how) investing in big data is worthwhile.
Operationalize analytics through modern data strategyNagarro
This document discusses the need for companies to operationalize analytics through a modern data strategy. It outlines key drivers of innovation like customers, competitors and regulators that necessitate such a strategy. It then discusses challenges of existing systems related to data volume, structure and regulations. The document proposes a modern data architecture with three pillars - people, process and technology. It provides an example framework for an enterprise data strategy and references Nagarro's capabilities in big data and analytics.
The document discusses big data analytics and related topics. It covers the evolution of technology, an overview of big data analytics including the 5 V's (volume, variety, velocity, value, and veracity). It also discusses research topics in big data, tools and software, literature surveys on various big data studies, identified research gaps, and a proposed activity chart and bibliography. The document provides a comprehensive overview of big data analytics, key concepts, potential research areas, and literature in the field.
Companies from across sectors are experiencing exponential growth in data as social interactions, rich media and a variety of devices generate new content. A tidal wave... of digital data is getting created through emails, instant messaging, survey videos, images, RFID tags, web text, blogs, geo-location devices, collaboration platforms like Twitter and Facebook, and so many other sources.
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
Big data provides opportunities for businesses to gain insights from large, diverse, and rapidly changing data. Traditional business intelligence tools answer some but not all key questions, while big data technologies can potentially answer all questions by analyzing structured and unstructured data. Opportunities exist in using big data to personalize offers, predict customer behavior, and optimize digital marketing campaigns. Machine learning algorithms like logistic regression and clustering can help businesses leverage big data to improve customer targeting and increase sales.
Stop the madness - Never doubt the quality of BI again using Data GovernanceMary Levins, PMP
Does this sound familiar? "Are you sure those numbers are right?" "Why are your numbers different than theirs?"
We've all heard it and had that gut wrenching feeling of doubt that comes with uncertainty around the quality of the numbers.
Stop the madness! Presented in Dunwoody on April 18 by industry leading expert Mary Levins who discusseses what it takes to successfully take control of your data using the Data Governance Framework. This framework is proven to improve the quality of your BI solutions.
Mary is the founder of Sierra Creek Consulting
This document provides an overview of handling and processing big data. It begins with defining big data and its key characteristics of volume, velocity, and variety. It then discusses several ways to effectively handle big data, such as outlining goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. Metadata is also important for big data handling and processing. The document outlines the different types of metadata and closes by discussing technologies commonly used for big data processing like Hadoop, MapReduce, and Hive.
Business intelligence (BI) involves strategies and technologies used to analyze business data and present information to support decision-making. Big data refers to extremely large datasets that require advanced analytics to derive insights. BI technologies provide historical, current, and predictive views of business operations through reporting, analytics, and data mining. While BI helps with reporting, budgeting, forecasting, and promotions, it can be costly and expose information to risks. Big data allows for detecting fraud, gaining competitive insights, and improving customer service and profits through real-time analysis, but poses logistical and privacy challenges.
The document discusses data warehousing, data mining, and business intelligence. It defines each topic and explains their key processes and purposes. Data warehousing involves collecting, storing, and managing large amounts of data from different sources for analysis and decision making. Data mining analyzes large datasets to identify patterns and relationships for informed decisions. Business intelligence provides technologies and methods to analyze business data for insights, performance improvement, and informed decision making.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Big data refers to the
ever-increasing volume,
velocity, variety,
variability and complexity
of information. Data expansion over the years
Big Data
3. Application of Big Data
• Optimizing Machine and Device Performance
• Understanding and Targeting Customers
• Understanding and Optimizing Business Processes
• Personal Quantification and Performance Optimization
• Financial Trading
4. How Marketing and data work!
Marketing optimization/performance.
Customer engagement.
Customer retention and loyalty.
KEY FEATURES
6. • Most Organizations rely too much on gut
In today’s volatile business
environment, judgment built from
past experience is increasingly
unreliable. With consumer behaviors
in flux, once-valid assumptions can
quickly become outdated.
8. • Dangerously distracted by data
Sometimes, one can blindly follow data without applying the
human intellect. This, leads to higher risk of taking wrong
decisions.
10. Focus on Goal and filter out the noise
Three key qualities:
Comfort with ambiguity
Ability to ask strategic questions based on data
Narrow focus on higher-order goals.
11.
12. Indian Managers Globally
CHALLENGES
• Globalization of Economy
• Corporate restructuring
• Newer organizational designs
• Emphasis on knowledge management
13.
14. Relevance to an Indian Manager
A manager working in some MNC can be
mislead with data. It is important to
consider that if managers get better access
to raw numbers and big data keeps
growing, the importance of the filtering
ability will only intensify, and a well well-
guided team environment will
only help in filtering out the noise, which
will further help them to achieve their goals.
15. Data Facts
• The amount of data generated
in two days is almost as much
as all the data before 2003.
• Harnessing Big Data could reduce Health Care costs by 8%.
• In 1985, it cost $100,000 to store a gigabyte of data. It cost 5 cents
in 2013.
• Today’s data centers occupy an area of land equal in size to almost
6,000 football fields.