This document provides an overview of HR analytics and business analytics. It defines HR analytics as using analytical processes and data to improve employee performance and retention. Business analytics involves collecting, analyzing, and modeling business data to gain insights. The document discusses the evolution of business analytics from operations research during WWII to modern tools like Google Analytics. It also covers the scope, advantages, and challenges of business analytics, as well as its applications in different business domains like finance, e-commerce, and aviation.
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.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses using data mining and market basket analysis techniques to analyze customer purchasing patterns. Market basket analysis examines what products customers frequently purchase together to identify association rules between items. This can help retailers with store layout, promotions, and targeting customers. The document outlines the steps in market basket analysis, including data integration, classification, association rule mining, and visualization tools to analyze customer transactions and identify related products that are commonly purchased together. Examples are given of how association rules have identified that customers often buy shampoo and conditioner or flour and eggs together.
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It allows companies to focus on the most important information to help support business decisions. Data is being collected in enormous quantities, ranging from terabytes to petabytes, from sources like customer transactions, mobile phone usage, health records, and more. Data mining techniques can be used to extract useful insights from this data, such as identifying profitable customer segments, predicting customer churn, detecting fraud, and informing marketing strategies. It provides value by supporting functions like segmentation, targeting, churn reduction, and risk assessment.
This document provides an overview of HR analytics and business analytics. It defines HR analytics as using analytical processes and data to improve employee performance and retention. Business analytics involves collecting, analyzing, and modeling business data to gain insights. The document discusses the evolution of business analytics from operations research during WWII to modern tools like Google Analytics. It also covers the scope, advantages, and challenges of business analytics, as well as its applications in different business domains like finance, e-commerce, and aviation.
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.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Data can exist in many forms and analytics involves finding patterns in data to aid decision making. There are different types of analytics like descriptive, predictive, and prescriptive. Data analysis is the process of inspecting, cleaning, and modeling data to provide business insights. It is used to help organizations make faster and better decisions to reduce costs and improve products and marketing. Advanced analytics uses tools like data mining, location intelligence, and predictive analytics to examine historical data and forecast future behaviors.
This document discusses using data mining and market basket analysis techniques to analyze customer purchasing patterns. Market basket analysis examines what products customers frequently purchase together to identify association rules between items. This can help retailers with store layout, promotions, and targeting customers. The document outlines the steps in market basket analysis, including data integration, classification, association rule mining, and visualization tools to analyze customer transactions and identify related products that are commonly purchased together. Examples are given of how association rules have identified that customers often buy shampoo and conditioner or flour and eggs together.
Data mining is the process of analyzing large amounts of data to discover hidden patterns and relationships. It allows companies to focus on the most important information to help support business decisions. Data is being collected in enormous quantities, ranging from terabytes to petabytes, from sources like customer transactions, mobile phone usage, health records, and more. Data mining techniques can be used to extract useful insights from this data, such as identifying profitable customer segments, predicting customer churn, detecting fraud, and informing marketing strategies. It provides value by supporting functions like segmentation, targeting, churn reduction, and risk assessment.
The document provides information on various aspects of railway planning and engineering. It discusses different types of transportation and railway gauges. It also describes key components of the permanent way including rails, sleepers, ballast and fixtures. Different types of these components are explained along with their requirements and characteristics. The document also covers topics like creep, wear of rails, route alignment survey and different stages of engineering survey.
A very necessary intellectual uplift in fertility work up for practitioners, faculties and students of Ayurveda based on Science Based Evidence Based Ayurveda
breve resumen sobre la historia de la computadora posee:
motivacion para recogo de saberes previos, contenido y cuestionario interactivo donde el estudiante puede confirmar su progreso, conformen avancen de pregunta, en caso de error no avanza de pregunta y sale una diapositiva de error
This booklet was used by Ishwar Agarwal, Gyanu Karki, and Talha Rehman at the IMPACT National Conference 2017 to facilitate a Design Thinking workshop to address a social problem, Obesity. In particular, the attendees designed solutions of their partner's fast food consumption, which is a major cause of obesity. This workshop was adopted from Stanford d.school's gift giving experience: https://dschool.stanford.edu/groups/designresources/wiki/ed894/the_giftgiving_project.html
"A obra Frankenstein é densa, conta a história de um jovem chamado Victor Frankenstein que decide cursar medicina em uma cidade longe de onde morava, em meio aos estudos inúmeras questões, voltadas para a criação, começam o instigar, isso o faz empenhar em diversas pesquisas cujo o campo era gerar vida."
This document provides instructions for account verification at Eagle Aurum, including the documents required, image quality standards, and how to submit verification information via email. Key documents like ID are needed, photos must be clear and legible, and verification materials should be emailed with the account number and ID number in the subject line.
This document discusses various tools and methods for collecting both quantitative and qualitative data. It describes primary and secondary sources, as well as how to evaluate them. Quantitative methods mentioned include census, surveys, and administrative data. Qualitative methods include interviews, focus groups, observation, and case studies. Ethical considerations for data collection and research are also covered, including informed consent and avoiding harm.
Arduino is an open-source hardware platform for building electronics projects. It was developed in 2005 in Italy to provide a simple tool for non-engineers to create digital projects. Popular Arduino boards include the Uno, Leonardo, Mega, and Nano. The Arduino uses an open-source IDE software to write code and upload it to the board, supporting programming languages like Processing. Key components of Arduino boards include a microcontroller, digital and analog pins, flash memory, and operating voltage of 5V.
leewayhertz.com-Data analysis workflow using Scikit-learn.pdfKristiLBurns
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision-making. It involves applying various statistical and analytical techniques to uncover patterns, relationships, and insights from raw data.
Managing marketing information to gain customer insights. MarketingDearMudassir
This document provides an overview of principles of marketing and managing marketing information to gain customer insights. It discusses assessing marketing information needs, marketing research, and analyzing and using market information. Specific topics covered include marketing information systems, assessing marketing information needs, developing and collecting marketing information through research, analyzing the information using tools like CRM, and distributing and using the marketing information.
Data analysis and analytics have become integral to decision-making in various fields.
In this presentation, we'll explore the importance, process, and applications of data analysis and analytics.
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
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.
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
Metrics to Maturity, Intelligence for Innovation: Your Value PropositionCherwell Software
Managing the perception of value is a key strategic initiative that solidifies the business case for further investment in an organization’s service desk. However, metrics are the key to achieving this difficult and challenging proposition. Taking a segmented approach to metrics can bring speed and relevancy to reports and dashboards by empowering the user’s data literacy and the organization’s overall strategic goals. This session will explain how correctly managing metrics for maturity can go hand-in-hand with innovation and value. Status quo BI initiatives will no longer be good enough for IT to maintain its value proposition. The IT organization should manage the user’s perception of value with business intelligence and metrics.
Marketing research involves obtaining information about consumers and customers to help identify opportunities, solve problems, implement plans, and monitor performance for marketers. It answers key questions like what products to offer, where to sell them, how to promote them, and at what price. Marketing research saves companies money by helping anticipate successful products before launch. It is valuable for companies of all sizes, from formal research departments at large companies to hiring outside agencies. A marketing information system regularly generates, stores, analyzes and distributes internal and external marketing data for decision making.
business analytics and its importance, marketing analytics definition and its importance, how marketing analytics helps to run the organization in effective and efficient manner.
Marketing research involves gathering information about consumers and customers to help identify opportunities and solve problems for marketers. It provides data to implement marketing plans and assess performance. The primary goal is obtaining insights into consumer preferences, opinions, behaviors, trends and plans. Marketing research is valuable for companies of all sizes as it can help determine which products to offer, where to sell them, how to promote them and price them, saving companies money by predicting success. Different types of marketing research include attitude, market, media and product research.
The document provides information on various aspects of railway planning and engineering. It discusses different types of transportation and railway gauges. It also describes key components of the permanent way including rails, sleepers, ballast and fixtures. Different types of these components are explained along with their requirements and characteristics. The document also covers topics like creep, wear of rails, route alignment survey and different stages of engineering survey.
A very necessary intellectual uplift in fertility work up for practitioners, faculties and students of Ayurveda based on Science Based Evidence Based Ayurveda
breve resumen sobre la historia de la computadora posee:
motivacion para recogo de saberes previos, contenido y cuestionario interactivo donde el estudiante puede confirmar su progreso, conformen avancen de pregunta, en caso de error no avanza de pregunta y sale una diapositiva de error
This booklet was used by Ishwar Agarwal, Gyanu Karki, and Talha Rehman at the IMPACT National Conference 2017 to facilitate a Design Thinking workshop to address a social problem, Obesity. In particular, the attendees designed solutions of their partner's fast food consumption, which is a major cause of obesity. This workshop was adopted from Stanford d.school's gift giving experience: https://dschool.stanford.edu/groups/designresources/wiki/ed894/the_giftgiving_project.html
"A obra Frankenstein é densa, conta a história de um jovem chamado Victor Frankenstein que decide cursar medicina em uma cidade longe de onde morava, em meio aos estudos inúmeras questões, voltadas para a criação, começam o instigar, isso o faz empenhar em diversas pesquisas cujo o campo era gerar vida."
This document provides instructions for account verification at Eagle Aurum, including the documents required, image quality standards, and how to submit verification information via email. Key documents like ID are needed, photos must be clear and legible, and verification materials should be emailed with the account number and ID number in the subject line.
This document discusses various tools and methods for collecting both quantitative and qualitative data. It describes primary and secondary sources, as well as how to evaluate them. Quantitative methods mentioned include census, surveys, and administrative data. Qualitative methods include interviews, focus groups, observation, and case studies. Ethical considerations for data collection and research are also covered, including informed consent and avoiding harm.
Arduino is an open-source hardware platform for building electronics projects. It was developed in 2005 in Italy to provide a simple tool for non-engineers to create digital projects. Popular Arduino boards include the Uno, Leonardo, Mega, and Nano. The Arduino uses an open-source IDE software to write code and upload it to the board, supporting programming languages like Processing. Key components of Arduino boards include a microcontroller, digital and analog pins, flash memory, and operating voltage of 5V.
leewayhertz.com-Data analysis workflow using Scikit-learn.pdfKristiLBurns
Data analysis is the process of analyzing, cleaning, transforming, and modeling data to uncover useful information and draw conclusions from it to support decision-making. It involves applying various statistical and analytical techniques to uncover patterns, relationships, and insights from raw data.
Managing marketing information to gain customer insights. MarketingDearMudassir
This document provides an overview of principles of marketing and managing marketing information to gain customer insights. It discusses assessing marketing information needs, marketing research, and analyzing and using market information. Specific topics covered include marketing information systems, assessing marketing information needs, developing and collecting marketing information through research, analyzing the information using tools like CRM, and distributing and using the marketing information.
Data analysis and analytics have become integral to decision-making in various fields.
In this presentation, we'll explore the importance, process, and applications of data analysis and analytics.
Data Analytics for Digital Marketing Lecture for Advanced Digital & Social Me...Valters Lauzums
Unlock the power of data analytics with our comprehensive slide deck from the Advanced Digital Strategy MGMT X 466.05 course at UCLAx. This presentation reviews the fundamental concepts and practical applications of data analytics in business and marketing.
Key Topics Covered:
Overview & Concepts: Learn how data analytics uses statistics, predictive modeling, and machine learning to enhance business performance.
Types of Data: Understand the differences between structured and unstructured data, and how to leverage quantitative and qualitative data.
Key Techniques: Explore descriptive, diagnostic, predictive, and prescriptive analytics to transform raw data into actionable insights.
Common Tools: Get acquainted with popular tools like Google Analytics, Google Looker, Adobe Analytics, and HubSpot for effective data tracking and analysis.
Data Analysis Process: Follow a step-by-step guide to collecting, cleaning, modeling, and interpreting data to drive informed decision-making.
Optimizing Campaigns: Learn how to use A/B testing and past campaign performance data to enhance future marketing efforts.
Defining Audiences: Discover how to segment target audiences using demographic data, purchase histories, and online behaviors for more precise marketing strategies.
Advanced Methods: Dive into advanced data analysis techniques like cohort analysis, cluster analysis, sentiment analysis, and regression analysis.
Customer Journey Analytics: Visualize the customer journey and identify key engagement moments to optimize the customer experience.
Data Visualization & Storytelling: Master the art of communicating data insights effectively through visualizations and contextual storytelling.
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.
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
Metrics to Maturity, Intelligence for Innovation: Your Value PropositionCherwell Software
Managing the perception of value is a key strategic initiative that solidifies the business case for further investment in an organization’s service desk. However, metrics are the key to achieving this difficult and challenging proposition. Taking a segmented approach to metrics can bring speed and relevancy to reports and dashboards by empowering the user’s data literacy and the organization’s overall strategic goals. This session will explain how correctly managing metrics for maturity can go hand-in-hand with innovation and value. Status quo BI initiatives will no longer be good enough for IT to maintain its value proposition. The IT organization should manage the user’s perception of value with business intelligence and metrics.
Marketing research involves obtaining information about consumers and customers to help identify opportunities, solve problems, implement plans, and monitor performance for marketers. It answers key questions like what products to offer, where to sell them, how to promote them, and at what price. Marketing research saves companies money by helping anticipate successful products before launch. It is valuable for companies of all sizes, from formal research departments at large companies to hiring outside agencies. A marketing information system regularly generates, stores, analyzes and distributes internal and external marketing data for decision making.
business analytics and its importance, marketing analytics definition and its importance, how marketing analytics helps to run the organization in effective and efficient manner.
Marketing research involves gathering information about consumers and customers to help identify opportunities and solve problems for marketers. It provides data to implement marketing plans and assess performance. The primary goal is obtaining insights into consumer preferences, opinions, behaviors, trends and plans. Marketing research is valuable for companies of all sizes as it can help determine which products to offer, where to sell them, how to promote them and price them, saving companies money by predicting success. Different types of marketing research include attitude, market, media and product research.
Business analytics involves using data, statistical analysis, quantitative methods, and business intelligence to understand and analyze business performance. Key aspects of business analytics include analyzing key performance indicators, common metrics like profitability and market share, and understanding factors that impact performance. Analytics techniques include statistical analysis, machine learning, and data management processes applied to problems like demand forecasting, customer churn prediction, and decision-making. The goal is to generate insights and recommendations to improve business performance and competitive strategies.
The document discusses customer relationship management (CRM) strategies and the use of data in CRM. It describes the C-MAT model for customer management, which involves understanding customer value, behavior and attitudes. It also discusses integrating customer data into CRM strategies using tools like data warehousing and data mining to collect and analyze large amounts of customer data. The document provides examples of how companies can use data mining techniques like correlation, segmentation and propensity analysis to gain insights into customers.
Using established business models as investigative tools and linking them together to enhance their analytical value is proposed in this paper as a method of progressing from strategic situation analysis to competitive advantage. Moreover, internal analyses that result in the identification of distinctive competencies and external investigations that uncover industry key success factors give strategists the means to develop strategies that may achieve competitive advantage.
Data Mining Concepts with Customer Relationship ManagementIJERA Editor
Data mining is important in creating a great experience at e-business. Data mining is the systematic way of extracting information from data. Many of the companies are developing an online internet presence to sell or promote their products and services. Most of the internet users are aware of on-line shopping concepts and techniques to own a product. The e-commerce landscape is the relation between customer relationship management (sales, marketing & support), internet and suppliers.
BUSINESS ANALYTICS, BACKBONE OF ORGANIZATIONS - A LITERATURE REVIEW.pdfAdheer A. Goyal
Business analytics is the process by which businesses use statistical methods and technologies based on historical data in order to attain organizational goals and make profit. Analytics are now regularly used in multiple areas of life. It should come as no surprise that business analytics is one of the fastest growing markets in enterprise software landscape. This article discusses about history and terminology of analytics. There is also a brief discussion about how business analytics gives opportunities not only to large scale and multinational companies but also to small and medium enterprises. In this conceptual paper major types of business analytics i.e., decision analytics, descriptive analytics, predictive analytics and prescriptive analytics are included. We also noted how business analytics can help you in supply chain management, analyze the key performance indicators which further helps in decision making, boost relationship with consumers and improve efficiency in the basis of product data. Then it consists of brief description about advantages and disadvantages of business analytics, difference between business analytics and business intelligence. This paper concludes with challenges in business analytics posed by the big data analytics, data scientists, business organization etc. and thoroughly researched the impact of business analytics on innovation.
Business analytics involves collecting and analyzing data to draw conclusions and identify patterns. It can be used to improve operational efficiency, increase revenues, and gain a competitive advantage. There are four main types of business analytics: descriptive analytics which describes what happened in the past, diagnostic analytics which explains why events occurred, predictive analytics which forecasts what will happen in the future, and prescriptive analytics which recommends actions. The business analytics process includes problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation. Challenges for business analytics include ensuring high quality data from different systems and having storage and processing capabilities that can provide real-time insights.
Marketing intelligence involves gathering and analyzing information about markets, customers, and competitors to inform strategic decision making. It includes insights into market size and trends, customer preferences and loyalty, competitor strategies and investments. Metrics used include financial metrics like profit and customer lifetime value, as well as non-financial metrics like brand awareness and customer satisfaction. Marketing intelligence activities can be aimed at demand generation, branding, customer relationships, shaping markets, and building infrastructure. Companies can improve their marketing intelligence by training salespeople, motivating distributors, networking externally, setting up advisory panels, using government data, buying external information, and collecting online customer feedback.
[MU630] 004. Business Intelligence & Decision SupportAriantoMuditomo
Copyright Notice:
This presentation is prepared by Author for Perbanas Institute as a part of Author Lecture Series. It is to be used for educational and non-commercial purposes only and is not to be changed, altered, or used for any commercial endeavor without the express written permission from Author and/or Perbanas Institute. Appropriate legal action may be taken against any person, organization, or entity attempting to misrepresent, charge, or profit from the educational materials contained here.
Authors are allowed to use their own articles without seeking permission from any person, organization, or entity.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
2. WHAT IS BIG DATA?
Big data analytics is the process of examining large data sets
containing a variety of data types -- i.e., big data - to uncover
hidden patterns, unknown correlations, market trends,
customer preferences and other useful business information.
The analytical findings can lead to more effective marketing,
new revenue opportunities, better customer service, improved
operational efficiency,competitive advantages over rival
organizations and other business benefits.
OR SIMPLY WE
CAN SAY…….
Data that becomes large enough that it
cannot be processed using
conventional methods
4. INDUSTRIAL EXAMPLES OF BIG
DATA!
1. Evaluation of risks
(market/credit)
2. Algorithmic Trading (link in
complex interdependent data)
3. Health Care & disaster recovery
4. Advertisements(allocation of
money and its impact)
5. Consumer sense (study of
behaviour and competitive
advantage)
5. How are we the part of this
Equation???
• Organizational alignment (measurable/directed)
• Decision making &
Consumption of analytics (focus)
• Search for Analysts (talent)
Note: 1.4 to 1.9 lakh unfilled positions of data analytics
experts in US by 2018 and shortage of 15 lakh managers and
analysts who understand and make decisions using big data
6. Please… do
read the book
It contains few uncharted
frontiers for your conquest.
provided your determination
to find one!!!!