Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
This document discusses how self-service analytics is becoming a new competitive advantage. It notes that business users now demand more user-friendly, self-service features for analytics without relying as much on expensive IT resources. The document advocates for giving a broader range of users from operational workers to customers access to analytics tools. It presents Information Builders' strategy of deploying self-service business intelligence applications called InfoApps across different roles to generate insights, drive revenue and profitability, and transform data into business value.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
Business is running ever faster—generating, collecting and using increas-ing volumes of data about every aspect of the interactions between sup-pliers, manufacturers, retailers and customers. Within these mountains of data are seams of gold—patterns of behavior that can be interpreted, classified and analyzed to allow predictions of real value. Which treat-ment is likely to be most effective for this patient? What can we offer that this particular customer is more likely to buy? Can we identify if that transaction is fraudulent before the sale is closed?
Analytics in Financial Services: Keynote Presentation for TDWI and NY Tech Co...Fitzgerald Analytics, Inc.
Keynote Presentation Given in New York City on March 30th, at a joint event of The Data Warehousing Institute (TDWI) and the New York Technology Council. This keynote presentation by Jaime Fitzgerald focused on "Bridging the Gap" between business goals in the data and analytic enablers of achieving these goals.
What we do; predictive and prescriptive analyticsWeibull AS
Prescriptive Analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.
Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.
Prescriptive analytics will also tell what probably will happen, but in addition: when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.
This document discusses how self-service analytics is becoming a new competitive advantage. It notes that business users now demand more user-friendly, self-service features for analytics without relying as much on expensive IT resources. The document advocates for giving a broader range of users from operational workers to customers access to analytics tools. It presents Information Builders' strategy of deploying self-service business intelligence applications called InfoApps across different roles to generate insights, drive revenue and profitability, and transform data into business value.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
This document discusses moving from business intelligence to predictive analytics. It introduces predictive analytics and how they can automatically discover patterns in data to predict trends or future behavior. Predictive analytics turn uncertainty about the future into usable probabilities. The document also discusses how predictive analytics can be applied in operations through decision management, which is a proven approach to deploy and apply predictive analytics at decision points.
This document discusses incorporating business experiments and statistical testing into strategy development. It begins by defining designed experiments as a way to treat the business environment like a controlled experimental setting by randomly assigning strategies to customers. The document then provides examples of experiments State Farm has conducted, from simple banner testing to more sophisticated multivariate testing. It also outlines typical evolution in testing practices and provides strategies for launching testing programs, including assembling the right team, scope, and controls. Finally, it addresses common arguments against testing like regulation, complexity, and organizational tradition and provides rebuttals.
Predictive and prescriptive analytics: Transform the finance function with gr...Grant Thornton LLP
As all businesses continue to collect, store and analyze more data than ever before, they face growing data challenges to support decision-making. Those who can leverage predictive and prescriptive analytics will differentiate themselves in the marketplace and gain a competitive advantage. In this report by Financial Executives Research Foundation Inc. and Grant Thornton LLP, we highlight insights from in-depth interviews with senior-level executives. These organizations use advanced analytics in their businesses to gain significant profit improvements. See more at - http://gt-us.co/1vv2KU9
- The document discusses Enova, a global online lender operating under multiple brands in six countries. It has over 1,100 employees and has extended over $15 billion in credit to more than 4 million customers worldwide.
- Enova uses advanced analytics at every stage of the customer experience from application to funding to servicing loans. Their analytics team of over 50 people uses massive datasets to power real-time decisioning and improve customer outcomes.
- Examples provided show how analytics have reduced page load times and improved conversion rates, enabled better customer offers using new data sources, reduced customer friction through verification improvements, and intercepted stalled applications. Lessons focus on deeply understanding customers, investing in analytics infrastructure, memorializing work,
The document discusses business analytics and decision making. It defines key concepts like data warehousing, data mining, business intelligence, descriptive analytics, predictive analytics, and prescriptive analytics. It explains how these concepts are used to extract insights from data to support decision making in organizations. Examples of how different types of analytics can be applied in a retail context are provided.
The document provides an overview of business intelligence (BI) including definitions, typical architectures, and key concepts. It describes how data is extracted from operational systems via ETL processes and loaded into data warehouses to support OLAP and business analytics. Different data modeling approaches are covered, including star schemas, snowflake schemas, and fact constellations. Dimensional modeling techniques are outlined to transform enterprise data models into structures optimized for analysis and reporting.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
The document discusses using analytics to drive business actions and decisions. It covers topics like visual data discovery, predictive analytics, optimization, alerts and automation across batch, real-time and streaming analytics. Case studies are presented in areas like retail banking, industrial equipment management and customer analytics. Both quantitative and marketing analytics are discussed as well as how they can be used together to gain business insights.
Business Partner Product Enablement Roadmap, IBM Predictive AnalyticsArrow ECS UK
This document provides an overview of IBM's predictive analytics products and capabilities. It discusses IBM SPSS products like Statistics, Modeler, Data Collection, Text Analytics for Surveys, and Analytic Server. It explains what each product does, such as build predictive models, analyze structured and unstructured data, deploy analytics, and more. The document also highlights the strengths of the IBM predictive analytics portfolio in areas like customer analytics, operational analytics, threat and fraud analytics, and decision management.
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
Recently, Oracle and Accenture polled some 200 CFOs and senior finance executives about
their strategies for improving the management reporting process. More than a third—41%— said selecting the right analytics tools and technologies was their top concern.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
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.
Financial analytics can help companies answer important strategic questions by providing forward-looking insights from large amounts of internal and external data. It moves beyond traditional financial reporting to help shape business strategy and improve real-time decision making. By analyzing risks, processes, investments, customer profitability, and other factors, financial analytics helps optimize profits and predict future impacts on key business drivers and stock price. It arms finance leaders with visual tools to understand complex information and partner more strategically with other areas of the business.
Business intelligence systems are also unable to deal with market volatiles. Infosys' business analytics offerings provide the processes, tools and expertise to extract the most from information investments description.
Material for the 26 Oct 2015 lecture I held for Aalto University business students. The lecture focuses on the high level topics in analytics and Big Data that are either central to the subject or just highly visible in the media.
The main messages of the lecture are:
- The purpose of analytics and of the data analyst is to solve business problems
- Big Data brings over some very special traits to doing analytics that don't exist when working working with smaller datasets. Understanding these traits is a must for successful analytics.
- Deploying analytics is more dependent on humans than on technology
- Data and analytics are nowadays significant assets to many companies. Therefore they need their own strategy and need to be managed just like any other business critical assets.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
Jim Cobb Containers Inspectors CertificateJim Cobb
Jim Cobb has successfully completed the Institute of International Container Lessors' marine cargo container inspector certification examination. He is certified as a container inspector from January 1, 2015 to December 31, 2019 based on passing the exam on October 11, 2014. The president of the Institute of International Container Lessors has issued this inspector's certification.
O Dropbox é um serviço gratuito que permite armazenar e sincronizar arquivos entre dispositivos. Os arquivos salvos na pasta Dropbox são automaticamente atualizados em todos os computadores e celulares do usuário, permitindo acessá-los de qualquer lugar. O Dropbox oferece opções de compartilhamento de arquivos e pastas com outros usuários.
Amigo sublime que deu-me o Senhor por guardião, valei-me!...Te rogo atuação direta em todos os momentos de minha vida, para que as dificuldades não pesem excessivamente sobre mim e assim eu deixe de percebê-lo ao meu lado!...
This document discusses incorporating business experiments and statistical testing into strategy development. It begins by defining designed experiments as a way to treat the business environment like a controlled experimental setting by randomly assigning strategies to customers. The document then provides examples of experiments State Farm has conducted, from simple banner testing to more sophisticated multivariate testing. It also outlines typical evolution in testing practices and provides strategies for launching testing programs, including assembling the right team, scope, and controls. Finally, it addresses common arguments against testing like regulation, complexity, and organizational tradition and provides rebuttals.
Predictive and prescriptive analytics: Transform the finance function with gr...Grant Thornton LLP
As all businesses continue to collect, store and analyze more data than ever before, they face growing data challenges to support decision-making. Those who can leverage predictive and prescriptive analytics will differentiate themselves in the marketplace and gain a competitive advantage. In this report by Financial Executives Research Foundation Inc. and Grant Thornton LLP, we highlight insights from in-depth interviews with senior-level executives. These organizations use advanced analytics in their businesses to gain significant profit improvements. See more at - http://gt-us.co/1vv2KU9
- The document discusses Enova, a global online lender operating under multiple brands in six countries. It has over 1,100 employees and has extended over $15 billion in credit to more than 4 million customers worldwide.
- Enova uses advanced analytics at every stage of the customer experience from application to funding to servicing loans. Their analytics team of over 50 people uses massive datasets to power real-time decisioning and improve customer outcomes.
- Examples provided show how analytics have reduced page load times and improved conversion rates, enabled better customer offers using new data sources, reduced customer friction through verification improvements, and intercepted stalled applications. Lessons focus on deeply understanding customers, investing in analytics infrastructure, memorializing work,
The document discusses business analytics and decision making. It defines key concepts like data warehousing, data mining, business intelligence, descriptive analytics, predictive analytics, and prescriptive analytics. It explains how these concepts are used to extract insights from data to support decision making in organizations. Examples of how different types of analytics can be applied in a retail context are provided.
The document provides an overview of business intelligence (BI) including definitions, typical architectures, and key concepts. It describes how data is extracted from operational systems via ETL processes and loaded into data warehouses to support OLAP and business analytics. Different data modeling approaches are covered, including star schemas, snowflake schemas, and fact constellations. Dimensional modeling techniques are outlined to transform enterprise data models into structures optimized for analysis and reporting.
Predictive analytics uses statistical techniques and business intelligence technologies to uncover relationships within large datasets to predict future behaviors or outcomes. While predictive analytics can provide benefits like reducing customer churn or improving marketing campaign response rates, it is not widely used due to complexity, underestimating value, high software costs, and reliance on good quality data. The document outlines best practices for predictive analytics including focusing on data management, expecting incremental improvements over time, measuring impact using business metrics, and gaining executive sponsorship for projects.
The document discusses using analytics to drive business actions and decisions. It covers topics like visual data discovery, predictive analytics, optimization, alerts and automation across batch, real-time and streaming analytics. Case studies are presented in areas like retail banking, industrial equipment management and customer analytics. Both quantitative and marketing analytics are discussed as well as how they can be used together to gain business insights.
Business Partner Product Enablement Roadmap, IBM Predictive AnalyticsArrow ECS UK
This document provides an overview of IBM's predictive analytics products and capabilities. It discusses IBM SPSS products like Statistics, Modeler, Data Collection, Text Analytics for Surveys, and Analytic Server. It explains what each product does, such as build predictive models, analyze structured and unstructured data, deploy analytics, and more. The document also highlights the strengths of the IBM predictive analytics portfolio in areas like customer analytics, operational analytics, threat and fraud analytics, and decision management.
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
Highlights of the Business Analytics seminar by Gary Cokins from October 21, 2014 presentation with Illinois CPA Society.
Gary Cokins is an internationally recognized expert, speaker, and author in performance improvement systems and cost management.
http://www.GaryCokins.com
Recently, Oracle and Accenture polled some 200 CFOs and senior finance executives about
their strategies for improving the management reporting process. More than a third—41%— said selecting the right analytics tools and technologies was their top concern.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
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.
Financial analytics can help companies answer important strategic questions by providing forward-looking insights from large amounts of internal and external data. It moves beyond traditional financial reporting to help shape business strategy and improve real-time decision making. By analyzing risks, processes, investments, customer profitability, and other factors, financial analytics helps optimize profits and predict future impacts on key business drivers and stock price. It arms finance leaders with visual tools to understand complex information and partner more strategically with other areas of the business.
Business intelligence systems are also unable to deal with market volatiles. Infosys' business analytics offerings provide the processes, tools and expertise to extract the most from information investments description.
Material for the 26 Oct 2015 lecture I held for Aalto University business students. The lecture focuses on the high level topics in analytics and Big Data that are either central to the subject or just highly visible in the media.
The main messages of the lecture are:
- The purpose of analytics and of the data analyst is to solve business problems
- Big Data brings over some very special traits to doing analytics that don't exist when working working with smaller datasets. Understanding these traits is a must for successful analytics.
- Deploying analytics is more dependent on humans than on technology
- Data and analytics are nowadays significant assets to many companies. Therefore they need their own strategy and need to be managed just like any other business critical assets.
In this presentation Juan M. Huerta talks about big data adoption process at Citi, realising the technical value of big data and global solutions. Huerta goes on to talk about following a hybrid approach, and the future of analytics, expensive algorithms applied to large datasets. With Citi using these approaches in hopes of getting even wider global recognition.
Pi cube banking on predictive analytics151Cole Capital
Predictive analytics can help banks in several key areas:
1) Predictive models can analyze customer data to better understand customers, identify new customers, estimate lifetime value, maximize spending, and reduce attrition.
2) Risk management models can assess default risk, optimize lending policies, and proactively restructure loans to manage credit risk.
3) Revenue models can help target marketing, make customized offers, and increase sales and loyalty by anticipating customer needs.
Jim Cobb Containers Inspectors CertificateJim Cobb
Jim Cobb has successfully completed the Institute of International Container Lessors' marine cargo container inspector certification examination. He is certified as a container inspector from January 1, 2015 to December 31, 2019 based on passing the exam on October 11, 2014. The president of the Institute of International Container Lessors has issued this inspector's certification.
O Dropbox é um serviço gratuito que permite armazenar e sincronizar arquivos entre dispositivos. Os arquivos salvos na pasta Dropbox são automaticamente atualizados em todos os computadores e celulares do usuário, permitindo acessá-los de qualquer lugar. O Dropbox oferece opções de compartilhamento de arquivos e pastas com outros usuários.
Amigo sublime que deu-me o Senhor por guardião, valei-me!...Te rogo atuação direta em todos os momentos de minha vida, para que as dificuldades não pesem excessivamente sobre mim e assim eu deixe de percebê-lo ao meu lado!...
El Festival de Navidad en el Cerro de Santa Cruz se celebrará del 15 al 24 de diciembre. Habrá presentaciones musicales, ferias artesanales, misas y procesiones religiosas. Se espera que miles de personas visiten el cerro durante el festival para celebrar la temporada navideña.
The document provides information about copper mould plates from Imex International, an ISO 9001:2008 certified company located in Bhilai, Chhattisgarh, India. It discusses the material properties and parameters of various copper materials - CuCrZr, CuAg, and CuNiBe - that are suitable for use in copper mould plates. It also provides details on nickel, cobalt, and nickel-cobalt coating parameters that can be applied to the copper mould plates to improve properties like tensile strength, adhesion, stress resistance, and hardness. Potential customers are invited to contact the company for more details on copper mould plates.
O documento discute o caráter educativo da dor. A dor pode ter diferentes aspectos como expiação, auxílio ou evolução e serve para despertar o espírito, corrigir imperfeições e promover o progresso. Embora dolorosa, a dor é benéfica pois esculpe a alma e a torna mais forte e pura.
Fiscal policy consists of measures related to central and local government revenue and expenditure. Monetary policy consists of the measures which affect the supply of money and credit and the rate of interest. Changes in the supply of money and in the rate of interest are generally closely interrelated in the sense that, other things being equal, an increase in the supply of money and credit is likely to produce a fall in the rate of interest, and vice versa. A broader definition of monetary policy adopted here includes also measures taken to change the exchange rate.
Los egipcios antiguos vivían a lo largo del río Nilo en Egipto, donde se dedicaban principalmente a la agricultura. Construyeron impresionantes pirámides y templos dedicados a sus muchos dioses representados con cabezas de animales. La religión jugó un papel importante en su sociedad, y dejaron atrás obras de arte y escritura que proporcionaron información sobre su avanzada civilización.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
The document discusses the growth of data and how analyzing and monetizing data can create business opportunities. It describes four main ways companies can provide commercial services from monetized data: direct marketing, predictive analytics, benchmarks and indexes, and gamification. The document also discusses how partners can help clients with spend analytics and the benefits of using a cloud-based platform versus legacy systems.
We are very excited to share our first edition of our OpenInsight. This is the first of a regular series of updates which will include insights covering extending the value of data within your business, tips and tricks on the use of the RAPid platform and generic articles on business analytics.
Business analytics can help organizations make better decisions by applying analytical techniques to business problems. While many organizations collect large amounts of data, few systematically analyze this data to improve decision making. Common approaches used by organizations to enhance decisions include analytics, testing hypotheses with data, and improving data quality. Business analytics frameworks provide tools to leverage more information for strategic and operational decisions.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Data mining involves finding patterns in large datasets that can help improve business outcomes. It uses computer processes and statistical techniques to analyze datasets too large for manual review. The CRISP process standardizes the data mining workflow into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Key modeling techniques include supervised learning methods like decision trees and regression as well as unsupervised clustering. Ongoing evaluation and refinement of models is important as data and business needs change over time.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
The document provides an introduction to the e-book which discusses how advanced analytics and big data are transforming businesses. It notes that the amount of data in the world is doubling every two years and analytics on this data is growing. New platforms and technologies now make it possible to economically process huge datasets and lower the cost and increase the speed of analysis.
The e-book contains essays from data analytics experts organized into five sections: business change, technology platforms, industry examples, research, and marketing. The technology platforms section focuses on tools that make advanced analytics affordable for organizations of all sizes. The introduction aims to provide insights into how analytics are evolving across different fields and industries through these expert perspectives.
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
The document discusses rethinking supply chain analytics. It notes that traditional supply chains rely on structured data and Excel, but new forms of analytics using technologies like blockchain and cognitive computing will disrupt current approaches within 5 years. Barriers to adopting new analytics include a lack of alignment between business and IT teams and a need to re-skill employees. The document recommends testing new technologies through agile sprints, embracing disparate data through schema-on-read, and placing analytics at the core of digital transformations.
Traditional approaches to handling disruptive change like big data analytics, such as resisting change or protecting existing business models, are ineffective in today's digital economy. By rapidly processing vast amounts of structured and unstructured data using big data tools, businesses can test new strategies faster through analytical sandboxes to better meet customer demands. Superfast in-memory computing is transforming industries by enabling new data-driven business models in areas like transportation. The ability to analyze unprecedented types and volumes of data in real time using tools like Apache Hadoop and Spark makes it possible to build more accurate predictive models and realize future gains.
The document discusses how modern data analytics are transforming business. It introduces the topic and explains that data is doubling every two years and analytics are becoming more valuable. The rest of the document is organized into five sections that will discuss topics like how analytics are changing business models, new technology platforms, industry examples, research, and marketing. The introduction of each section provides a brief overview of what essays in that section will cover. The overall goal is to provide insights from different perspectives on how analytics are rapidly evolving and playing an increasingly important role.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Big Data Analytics for Predicting Consumer BehaviourIRJET Journal
This document discusses using big data analytics and machine learning techniques to predict consumer behavior and sales trends. It begins with an introduction to consumer behavior and an overview of how analyzing customer data can provide insights. The document then discusses using data mining methods on customer data to build predictive models for tasks like sales forecasting. It proposes using a combination of random forest and linear regression algorithms on a dataset from various stores. The implementation section outlines the steps, including data preprocessing, feature extraction, applying algorithms to the data, comparing results and building the best predictive model. The goal is to determine the most accurate approach for understanding customer behavior and how they will respond in different situations.
The vast pool of data is a goldmine for all. However, only the information that gives an accurate picture can serve the purpose. As such, learn about the different ways to enhance the success of data scraping like never before. Data extraction is the process of gathering relevant information from different sources. The objective is to standardize it to have structured data. This data can then get used to performing queries or analytics calculations. Businesses today rely on different forms of data to run their enterprises. If the information collected is accessible and accurate, it can transform into valuable intelligence.
Only extracting data is not enough for any enterprise, but having relevant and accurate data is necessary too. As such, here are some of the ways in which the success of web extraction can get improved.
With the addition of a large amount of data in the internet world every day, the importance of web extraction is increasing. Today, several companies offer customized web scraping tools to users. It has helped in faster data gathering from the internet. These then get arranged into understandable information.
As such, web scraping has reduced human efforts, which is time-consuming. Collecting data now does not require manually visiting each website. It has aided companies in making informed decisions. Indeed, the future of web scraping is bright and will become more prominent for different businesses with time.
With the growth of the internet and companies' dependence on data and information, the future of web scraping is full of new adventures and successes. With a data-driven approach, enterprises can improve their services and offer, giving better output and grabbing customers' attention over time.
Employees can perform to the highest standards up to certain limits that should get set at the behest. Overloading them with too much data or unreasonable deadlines can lead to errors. However, an automated extraction system eliminates the potential risk of human errors. It also helps reduce potential biases and provides faster results.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
Similar to Integrating Analytics into the Operational Fabric of Your Business (20)
This IBM Redpaper provides a brief overview of OpenStack and a basic familiarity of its usage with the IBM XIV Storage System Gen3. The illustration scenario that is presented uses the OpenStack Folsom release implementation IaaS with Ubuntu Linux servers and the IBM Storage Driver for OpenStack. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit http://bit.ly/KWh5Dx to 'Follow' the official Twitter handle of IBM India Smarter Computing.
Learn how all flash needs end to end Storage efficiency. For more information on IBM FlashSystem, visit http://ibm.co/10KodHl.
Visit http://bit.ly/KWh5Dx to 'Follow' the official Twitter handle of IBM India Smarter Computing.
Learn about vSphere Storage API for Array Integration on the IBM Storwize family. IBM Storwize V7000 Unified combines the block storage capabilities of Storwize V7000 with file storage capabilities into a single system for greater ease of management and efficiency. For more information on IBM Storage Systems, visit http://ibm.co/LIg7gk.
Visit http://bit.ly/KWh5Dx to 'Follow' the official Twitter handle of IBM India Smarter Computing.
Learn about IBM FlashSystem 840 and its complete product specification in this Redbook. FlashSystem 840 provides scalable performance for the most demanding enterprise class applications. IBM FlashSystem 840 accelerates response times with IBM MicroLatency to enable faster decision making. For more information on IBM FlashSystem, visit http://ibm.co/10KodHl.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Learn about the IBM System x3250 M5,.The x3250 M5 offers the following energy-efficiency features to save energy, reduce operational costs, increase energy availability, and contribute to a green environment, energy-efficient planar components help lower operational costs. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210746104/IBM-System-x3250-M5
This Redbook talks about the product specification of IBM NeXtScale nx360 M4. The NeXtScale nx360 M4 server provides a dense, flexible solution with a low total cost of ownership (TCO). The half-wide, dual-socket NeXtScale nx360 M4 server is designed for data centers that require high performance but are constrained by floor space. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210745680/IBM-NeXtScale-nx360-M4
The IBM System x3650 M4 HD is a (1) 2-socket 2U rack-optimized server that supports up to 32 internal drives and features an innovative design for optimal performance, uptime, and dense storage. It offers (2) excellent reliability, availability, and serviceability for improved business environments. The server is (3) designed for easy deployment, integration, service, and management.
Here are the product specification for IBM System x3300 M4. This product can be managed remotely.The x3300 M4 server contains IBM IMM2, which provides advanced service-processor control, monitoring, and an alerting function. The IMM2 lights LEDs to help you diagnose the problem, records the error in the event log, and alerts you to the problem. For more information on System x, visit http://ibm.co/Q7m3iQ.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Learn about IBM System x iDataPlex dx360 M4. IBM System x iDataPlex is an innovative data center solution that maximizes performance and optimizes energy and space efficiency. The iDataPlex solution provides customers with outstanding energy and cooling efficiency, multi-rack level manageability, complete flexibility in configuration, and minimal deployment effort. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210744055/IBM-System-x-iDataPlex-dx360-M4
The IBM System x3500 M4 server provides powerful and scalable performance for business applications in an energy efficient tower or rack design. It features the latest Intel Xeon E5-2600 v2 or E5-2600 processors with up to 24 cores, 768GB RAM, 32 hard drives, and 8 PCIe slots. Comprehensive systems management tools and redundant components help ensure high availability, while its small footprint and 80 Plus Platinum power supplies reduce data center costs.
Learn about system specification for IBM System x3550 M4. The x3550 M4 offers numerous features to boost performance, improve scalability, and reduce costs. Improves productivity by offering superior system performance with up to 12-core processors, up to 30 MB of L3 cache, and up to two 8 GT/s QPI interconnect links. For more information on System x, visit http://ibm.co/Q7m3iQ.
Learn about IBM System x3650 M4. The x3650 M4 is an outstanding 2U two-socket business-critical server, offering improved performance and pay-as-you grow flexibility along with new features that improve server management capability. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210741926/IBM-System-x3650-M4
Learn about the product specification of IBM System x3500 M3. System x3500 M3 has an energy-efficient design which works in conjunction with the IMM to govern fan rotation based on the readings that it delivers. This saves money under normal conditions because the fans do not have to spin at high speed. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210741626/IBM-System-x3500-M3
Learn about IBM System x3400 M3. The x3400 M3 offers numerous features to boost performance and reduce costs, x3400 M3 has the ability to grow with your application requirements with these features. Powerful systems management features simplify local and remote management of the x3400 M3. For more information on System x, visit http://ibm.co/Q7m3iQ.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Learn about IBM System 3250 M3 which is a single-socket server that offers new levels of performance and flexibility
to help you respond quickly to changing business demands. Cost-effective and compact, it is well suited to small to mid-sized businesses, as well as large enterprises. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210740347/IBM-System-x3250-M3
Learn about IBM System x3200 M3 and its specifications. The System x3200 M3 features easy installation and management with a rich set of options for hard disk drives and memory. The efficient design helps to save energy and provide a better work environment with less heat and noise. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210739508/IBM-System-x3200-M3
Learn about the configuration of IBM PowerVC. IBM PowerVC is built on OpenStack that controls large pools of server, storage, and networking resources throughout a data center. IBM Power Virtualization Center provides security services that support a secure environment. Installation requires just 20 minutes to get a virtual machine up and running. For more information on Power Systems, visit http://ibm.co/Lx6hfc.
Visit http://on.fb.me/LT4gdu to 'Like' the official Facebook page of IBM India Smarter Computing.
Learn about Ibm POWER7 Virtualization Performance. PowerVM Lx86 is a cross-platform virtualization solution that enables the running of a wide range of x86 Linux applications on Power Systems platforms within a Linux on Power partition without modifications or recompilation of the workloads. For more information on Power Systems, visit http://ibm.co/Lx6hfc.
http://www.scribd.com/doc/210734237/A-Comparison-of-PowerVM-and-Vmware-Virtualization-Performance
This reference architecture document describes deploying the VMware vCloud Enterprise Suite on the IBM PureFlex System hardware platform. Key points:
- The vCloud Suite software provides components for managing and delivering cloud services, while the IBM PureFlex System provides an integrated hardware platform in a single chassis.
- The reference architecture focuses on installing the vCloud Suite management components as virtual machines on an ESXi host to manage consumer resources.
- The IBM PureFlex System provides servers, networking, and storage in a single chassis that can then be easily scaled out. This standardized deployment accelerates provisioning of cloud infrastructure.
- Deployment considerations cover systems management using IBM Flex System Manager, server, networking, storage configurations
Learn how x6: The sixth generation of EXA Technology is fast, agile and Resilient for Emerging Workloads from Alex Yost. Vice President, IBM PureSystems and System x
IBM Systems and Technology Group. x6 drives cloud and big data for enterprises by achieving insight faster thereby outperforming competitors. For more information on System x, visit http://ibm.co/Q7m3iQ.
http://www.scribd.com/doc/210715795/X6-The-sixth-generation-of-EXA-Technology
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Integrating Analytics into the Operational Fabric of Your Business
1. Integrating Analytics into the
Operational Fabric of Your Business
A combined platform for optimizing analytics and operations
April 2012
A White Paper by
Dr. Barry Devlin, 9sight Consulting
barry@9sight.com
Business is running ever faster—generating, collecting and using increas-
ing volumes of data about every aspect of the interactions between sup-
pliers, manufacturers, retailers and customers. Within these mountains of
data are seams of gold—patterns of behavior that can be interpreted,
classified and analyzed to allow predictions of real value. Which treat-
ment is likely to be most effective for this patient? What can we offer that
this particular customer is more likely to buy? Can we identify if that
transaction is fraudulent before the sale is closed?
To these questions and more, operational analytics—the combination of
deep data analysis and transaction processing systems—has an answer.
This paper describes what operational analytics is and what it offers to the
business. We explore its relationship to business intelligence (BI) and see
how traditional data warehouse architectures struggle to support it. Now,
the combination of advanced hardware and software technologies provide
the opportunity to create a new integrated platform delivering powerful
operational analytics within the existing IT fabric of the enterprise.
With the IBM DB2 Analytics Accelerator, a new hardware/software offer-
ing on System z, the power of the massively parallel processing (MPP) IBM
Netezza is closely integrated with the mainframe and accessed directly and
transparently via DB2 on z/OS. The IBM DB2 Analytics Accelerator brings
enormous query performance gains to analytic queries and enables direct
integration with operational processes.
This integrated environment also enables distributed data marts to be re-
turned to the mainframe environment, enabling significant reductions in
data management and total ownership costs.
Contents
2 Operational analytics—
diamonds in the detail,
magic in the moment
5 Data warehousing and
the evolution of species
7 An integrated platform
for OLTP and
operational analytics
11 Business benefits and
architectural advantages
13 Conclusions