Business analytics
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Business analytics (BA) refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.
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Analytics
Analytics is the analysis of data to find meaningful patterns and insights to help make better decisions. It uses statistics, computer programming, and operations research to quantify performance. Businesses use analytics to personalize customer experiences, inform decision making, streamline operations, mitigate risks, and enhance security. There are different types of analytics including marketing, web, people, portfolio, risk, digital, and security analytics. Portfolio analysis assesses investments to evaluate risk and return and helps investors manage their portfolios over time based on market conditions.
Business Analytics to solve your Business Problems
Business Analytics helps organizations achieve their core business goals by providing insights into key performance drivers and factors. Analytics can help maximize revenue by understanding revenue growth, customer acquisition/retention, and pricing strategies. It helps minimize operating expenses by optimizing supply chain, inventory, and staffing costs. Analytics maximizes returns on long term assets by evaluating strategic investments, productivity, and technology spend. It also helps manage risks through monitoring strategic, operational, and compliance risks. In this way, business analytics delivers value across an organization's financial, operational and strategic objectives.
Think better using “Descriptive-Prescriptive” Approach
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
Predictive Model
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Predictive project analytics: Will your project be successful?
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
An analysis of Making Advanced Analytics Work for You by Dominic Barton and D...
This document discusses how companies can make advanced analytics work for them. It outlines that fully exploiting data and analytics requires three capabilities: identifying and managing multiple data sources, building advanced analytics models, and transforming the organization so data and models yield better decisions. Two key features are having a clear strategy for how to use data/analytics to compete and deploying the right technology. The document provides three areas of action for organizational change: developing business-relevant analytics, embedding analytics into front-line tools, and developing capabilities to exploit big data. Managers need to understand how information can be used for key decisions and get creative about potential external/new data sources through targeted efforts.
Whitepaper: Predictive Analytics - Looking to the Future
Predictive analytics uses past performance data and modeling techniques to predict future outcomes and trends. This allows companies to evaluate potential strategies and optimize resources before implementing decisions. Descriptive analytics only provides a rear-view perspective on past performance. The document discusses how predictive analytics benefits strategic planning, price optimization, customer relationships, and targeted marketing by facilitating "what if" analyses and data-driven decisions.
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Predictive analytics uses data mining, statistics, modeling, machine learning and artificial intelligence to analyze current and historical facts to make predictions about future or otherwise unknown events. This presentation provides an overview of predictive analytics, including its business applications such as customer retention, risk management and operational optimization. Common predictive analytics methods and tools are also discussed.
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Analytics is the analysis of data to find meaningful patterns and insights to help make better decisions. It uses statistics, computer programming, and operations research to quantify performance. Businesses use analytics to personalize customer experiences, inform decision making, streamline operations, mitigate risks, and enhance security. There are different types of analytics including marketing, web, people, portfolio, risk, digital, and security analytics. Portfolio analysis assesses investments to evaluate risk and return and helps investors manage their portfolios over time based on market conditions.
Business Analytics to solve your Business Problems
Business Analytics helps organizations achieve their core business goals by providing insights into key performance drivers and factors. Analytics can help maximize revenue by understanding revenue growth, customer acquisition/retention, and pricing strategies. It helps minimize operating expenses by optimizing supply chain, inventory, and staffing costs. Analytics maximizes returns on long term assets by evaluating strategic investments, productivity, and technology spend. It also helps manage risks through monitoring strategic, operational, and compliance risks. In this way, business analytics delivers value across an organization's financial, operational and strategic objectives.
Think better using “Descriptive-Prescriptive” Approach
The document outlines an approach called "Descriptive-Prescriptive" for better problem solving. It involves first describing a problem by connecting elements and details to understand it fully ("Analysis"), then prescribing rules and conditions to formulate a solution ("Synthesis"). This approach can be applied to test baselining, strategy formulation, test design, and reporting. Diagrams and examples are provided to illustrate applying description and prescription at different stages. The approach forms the basis of a personal test methodology called HBT, which uses six stages and eight disciplines of thinking.
Predictive Model
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Predictive project analytics: Will your project be successful?
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
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Whitepaper: Predictive Analytics - Looking to the Future
Predictive analytics uses past performance data and modeling techniques to predict future outcomes and trends. This allows companies to evaluate potential strategies and optimize resources before implementing decisions. Descriptive analytics only provides a rear-view perspective on past performance. The document discusses how predictive analytics benefits strategic planning, price optimization, customer relationships, and targeted marketing by facilitating "what if" analyses and data-driven decisions.
Analytics Overview #Predictive Analytics
Predictive analytics uses data mining, statistics, modeling, machine learning and artificial intelligence to analyze current and historical facts to make predictions about future or otherwise unknown events. This presentation provides an overview of predictive analytics, including its business applications such as customer retention, risk management and operational optimization. Common predictive analytics methods and tools are also discussed.
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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.
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The document outlines the three pillars needed for a successful analytics strategy: people, process, and technology. For people, it emphasizes training employees, collaborating across departments, and gaining stakeholder buy-in. For process, it stresses having frameworks for data management, defining governance policies, and standardizing procedures. For technology, it recommends selecting business intelligence tools that integrate with enterprise data sources, provide self-service capabilities, and deliver timely insights. Mastering these three pillars will help maximize value from data and deliver trusted insights.
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Between 60-75% of projects fail to finish successfully. According to research, 25% of projects experience serious quality problems and 40% finish with unclear customer satisfaction, while only 35% are completed successfully. One key factor in ensuring project success is having clear business objectives. Business analysis helps organizations identify business needs and appropriate solutions to address them. It supports organizations in meeting objectives through alternative solution options. Business analysis focuses on the entire product lifecycle through six knowledge areas including planning, requirements elicitation, management and communication, enterprise analysis, requirements analysis, and solution assessment and validation. While not a guarantee of success, business analysis decreases project failure risk.
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This document provides an introduction to business analytics. It defines business analytics as combining data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with information and scenarios to make well-researched decisions. The document discusses the evolution of business analytics and its significance and uses, including making data-driven decisions and improving profitability. It also outlines some challenges of business analytics and describes the scopes of descriptive analysis, predictive analysis, and prescriptive analysis.
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This document discusses using predictive analytics to improve customer retention. It describes how the company Natero helps clients predict churn by extracting features from sales, financial, support, and product usage data and building predictive models. Key steps include labeling accounts based on future potential churn rather than current status, and accounting for situations where churn was avoided. When done correctly with enough data, models can predict churn with over 80% precision and 50% recall. Clients have been able to reduce monthly churn by up to 24% by acting on these predictions through dedicated customer success processes.
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2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
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Business analytics course
Business analytics is the process of using statistical methods and technologies to analyze historical business data in order to gain insights and improve strategic decision making. It helps businesses increase profits, market share, and shareholder returns. Business analytics focuses on developing new understandings of past business performance through continuous data exploration and analysis. The results of business analytics can be used directly for decision making or to drive automated decisions.
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Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
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An introduction to what business analytics actually is and the types of business analytics that are useful for businesses and organisations.
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Business Intelligence is the initial basic step of Business Analytics. It refers to gathering raw and complex data, and converting it into systematic and logical information in a format that is usable by the end user. Copy the link given below and paste it in new browser window to get more information on Business Intelligence And Business Analytics:-
http://www.transtutors.com/homework-help/management/managing-information-technology/business-intelligence-analytics/
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Business analytics software allows companies to analyze large amounts of data and gain insights to improve operations and make smarter decisions. It provides tools to gather, analyze, and visualize data, helping businesses address problems, implement effective strategies, and optimize performance. Using business analytics software, companies can better understand customer behavior, predict outcomes, manage tasks efficiently, and enhance marketing and financial plans to stay competitive.
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The document outlines the three pillars needed for a successful analytics strategy: people, process, and technology. For people, it emphasizes training employees, collaborating across departments, and gaining stakeholder buy-in. For process, it stresses having frameworks for data management, defining governance policies, and standardizing procedures. For technology, it recommends selecting business intelligence tools that integrate with enterprise data sources, provide self-service capabilities, and deliver timely insights. Mastering these three pillars will help maximize value from data and deliver trusted insights.
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Between 60-75% of projects fail to finish successfully. According to research, 25% of projects experience serious quality problems and 40% finish with unclear customer satisfaction, while only 35% are completed successfully. One key factor in ensuring project success is having clear business objectives. Business analysis helps organizations identify business needs and appropriate solutions to address them. It supports organizations in meeting objectives through alternative solution options. Business analysis focuses on the entire product lifecycle through six knowledge areas including planning, requirements elicitation, management and communication, enterprise analysis, requirements analysis, and solution assessment and validation. While not a guarantee of success, business analysis decreases project failure risk.
Business Analytics Overview
This document discusses business analytics. It defines business analytics as using data, statistical and quantitative analysis, explanatory and predictive models to gain insights and support decision-making. The document outlines the typical business analytics process, including understanding the business objectives, assessing the situation, collecting and preparing data, developing analytic models, evaluating and reporting results, and deploying the outcomes. It provides examples of how analytics can be used to drive personalized customer services, optimize people management decisions, and conduct real-time sentiment analysis of social media data for an FMCG company. The document concludes with lessons learned, emphasizing the importance of continuous learning, gaining experience through projects and mentoring, and having confidence in one's abilities.
Introduction to Business Analytics
This document provides an introduction to business analytics. It defines business analytics as combining data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with information and scenarios to make well-researched decisions. The document discusses the evolution of business analytics and its significance and uses, including making data-driven decisions and improving profitability. It also outlines some challenges of business analytics and describes the scopes of descriptive analysis, predictive analysis, and prescriptive analysis.
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This document discusses how companies can fully exploit data and analytics. It notes that advanced analytics can provide benefits like forecasting, enhancing performance, and decision making for businesses and preventive actions, rule making, and decision making for governments. However, managers may be skeptical due to big investments and misunderstanding of data. To fully utilize analytics, companies must identify and combine multiple data sources, build advanced analytics models for prediction and optimization, and transform their organization so data and models yield better decisions. The document provides steps for exploiting data and analytics, including choosing the right data, upgrading IT architecture, building predictive models, and developing business-relevant analytics.
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This document discusses using predictive analytics to improve customer retention. It describes how the company Natero helps clients predict churn by extracting features from sales, financial, support, and product usage data and building predictive models. Key steps include labeling accounts based on future potential churn rather than current status, and accounting for situations where churn was avoided. When done correctly with enough data, models can predict churn with over 80% precision and 50% recall. Clients have been able to reduce monthly churn by up to 24% by acting on these predictions through dedicated customer success processes.
Challenges in adapting predictive analytics
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Swot analysis
The document discusses SWOT analysis, which is a tool used to evaluate the strengths, weaknesses, opportunities, and threats related to a business process, proposed solution, or business entity. A SWOT analysis involves identifying internal strengths and weaknesses as well as external opportunities and threats. It is useful for evaluating viability, prioritizing needs, and guiding development. The results of a SWOT analysis can be used as inputs for decision making, risk assessment, and requirements allocation. The document provides guidelines for conducting a SWOT analysis, including defining the objective, assembling stakeholders, facilitating discussion, and publishing results.
Excel Datamining Addin Beginner
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
Resume_Sharon_S_Hines_Healthcare_IT
Sharon S. Hines is a healthcare IT analyst and technical services professional with over 30 years of experience in various roles including quality consultant, retail business owner, decision analyst, and internet marketing consultant. She has an MBA and BS in applied mathematics and is Epic certified. Hines has extensive experience assisting healthcare organizations with troubleshooting, enhancing, and upgrading their use of Epic software. She brings problem solving skills, process innovation expertise, and an entrepreneurial spirit to her work.
Rapid-Assessment
The document discusses a Rapid Blueprint Assessment that the SupplyChainGroup LLC provides to help companies examine their supply chain operations. The assessment identifies issues in a company's costs, inventory management, processes and technologies. It develops initiatives to address these issues and creates a blueprint and roadmap to implement the initiatives. The methodology involves analyzing the current state, specifying initiatives, developing business cases, quantifying benefits and creating an implementation plan. The assessment helps companies identify and achieve savings and service improvements.
Predictive Analysis PowerPoint Presentation Slides
Gather the required information from the data and predict future outcomes and trends. Use content-ready Predictive Analysis PowerPoint Presentation Slides to forecast future probabilities. Majorly applied in the business field, predictive analysis PPT templates will help you evaluate current data and historical facts to understand customers, products, services, partners, and to identify potential risks and opportunities for an organization. This deck comprises of templates such as research methodology, consumer insights consumption, need for consumer insights, key stats, data collection and processing, consumer insight capabilities, These templates are completely customizable. You can edit the templates as per your need. Change color, text, icon and font size as per your requirement. Add or remove the content, if needed. Get access to the predictive analysis PowerPoint presentation slideshow to predict future outcomes for various business topics such as customer relationship management, health care, collection analytics, fraud detection, risk management, direct marketing, industry applications, etc. Get access to the professionally designed ready-made predictive analysis PowerPoint presentation slides for your business to interpret big data for your benefit. Maintain your demeanour with our Predictive Analysis Powerpoint Presentation Slides. They will help you keep your cool.
Identifying And Prototyping Data Science Use Cases
This document discusses best practices for identifying and prototyping data science use cases within an organization. It recommends establishing a process that involves identifying potential use cases by capturing key stakeholder input, evaluating their impact and priority, and developing prototypes to validate feasibility. The document emphasizes an iterative agile process for data science projects that balances scientific exploration with delivering tangible results. It also covers operationalizing and scaling successful prototypes by addressing infrastructure, process, and organizational challenges.
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Business Intelligence And Business Analytics | Management
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Using business analytics to make smarter business decisions (sadiq shariff10@...
Using business analytics to make smarter business decisions (sadiq shariff10@...
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Business intelligence is a tool that transforms raw data into meaningful information to help businesses make better decisions. It helps managers and executives cut costs, identify opportunities, and improve processes. While the term was coined in 1865, business intelligence has grown more powerful due to increased data collection, storage capacity, and lower storage costs. Companies now use data from various digital sources for business intelligence. It provides benefits like accelerating decision making, optimizing processes, increasing efficiency, and gaining competitive advantages. Common business intelligence software tools are used to analyze historical, current, and predictive data for purposes like performance management and benchmarking against competitors. For successful implementation, companies must have clean data, effective training, clear ROI definitions, and focus on business objectives rather than
Business Analytics
Business analytics refers to using data, statistical methods, and modeling to generate insights and understand business performance in order to continuously improve plans and decisions. It focuses on developing new understandings from data rather than just measuring historical performance. Business analytics makes extensive use of analytical modeling and numerical analysis to drive fact-based management decisions. It can be used as input for both human and automated decision making in industries like healthcare to transform medical data into useful patient information and reporting. The main challenges are integrating diverse data sources and determining the most useful data subsets to analyze.
Business analytics
This document discusses business analytics, including its importance, components, and implementation. Key points:
- Business analytics discovers insights from integrated data to help businesses make better decisions. It converts data to useful information.
- Analytics provides high visibility, competitive advantage, faster decisions, and operational efficiencies that lead to high returns on investment.
- The reference architecture has three layers - data, analytics access, and delivery. It supports historical, current, and predictive analytics across structured and unstructured data sources.
Online Assignment Help
Business analytics leverages statistical analysis and predictive modeling of data to help organizations make informed decisions. It involves collecting, cleaning, analyzing, and visualizing data. This allows organizations to enhance efficiency, gain competitive advantages, and facilitate strategic decision making. Studying business analytics opens opportunities for careers in data analysis, business analysis, data science, marketing analytics, finance, and healthcare. Online assignment help supports learning business analytics by connecting students with experts, providing structured learning materials, and helping with practical skills and projects.
Why Data Analytics is Critical for Business Success.pptx
Data Analysts are professionals who are given the responsibility to look after important business functions such as data visualization, data warehousing, predictive analysis, and so on. Ref Link: https://tinyurl.com/mry3tnnw
Business Intelligence Consultants How to Choose the Right One? - By DataToBiz
Your journey to business intelligence starts here. In this Article By DataToBiz, We explored the necessary steps to create a list of best fits for your company.
Unit 1 pptx.pptx
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.
BA3
The document discusses business monitoring and predictive analytics. It defines key terms like gap analysis and discusses the difference between business analytics and business intelligence. The document also explains that predictive analytics allows companies to understand what is likely to happen by learning from past data. Predictive analytics can provide companies with a unique competitive advantage and maximize return on investment when used to improve business processes. Finally, the document outlines advantages like better decision making and disadvantages like costs and training for implementing business analytics.
Data Analytics
This document discusses different types of data analytics including web, mobile, retail, social media, and unstructured analytics. It defines business analytics as the integration of disparate internal and external data sources to answer forward-looking business questions tied to key objectives. Big data comes from various sources like web behavior and social media, while little data refers to any data not considered big data. Successful analytics requires addressing business challenges, having a strong data foundation, implementing solutions with goals in mind, generating insights, measuring results, sharing knowledge, and innovating approaches. The future of analytics involves every company having a data strategy and using tools to augment internal data. Predictive analytics tells what will happen, while prescriptive analytics tells how to make it
BUSINESS INTELLIGENCE
Business intelligence (BI) refers to techniques used to analyze business data and present it to facilitate decision making. BI technologies provide historical, current, and predictive views of business operations to support better decisions. The major components of BI include applications like reporting, analytics, and dashboards. While BI helps improve productivity, decision making, and results, it also faces disadvantages like data piling, costs, and complexity.
What is analytics
Analytics is the use of data, information technology, statistical analysis, and quantitative methods to help managers gain insights and make better decisions. It involves descriptive analytics which summarize past data, predictive analytics which analyze past data to understand the future, and prescriptive analytics which use optimization techniques to advise on possible outcomes and recommendations. Business analytics is a subset of data analytics that supports business decision making and performance through sequential application of these major analytic components.
How Can Business Analytics Dashboard Help Data Analysts.pdf
The Business analytics dashboard provides a centralized platform that empowers data analysts to explore, analyze, and visualize data more efficiently and intuitively, ultimately driving better decision-making. You can discover how these intuitive Business Intelligence dashboard tools enhance every aspect of data analysis and streamline a data analyst's job. Revolutionize how data analysts function and make the data analysis journey successful by visiting Grow.com.
What Is Business Intelligence's Role In Big Data Analysis
Business intelligence (BI) is a system of tools and methods that aid in strategic planning and informed decision-making. This involves collecting data from internal and external sources, analyzing the data to gain insights, and visualizing insights for decision makers. BI helps organizations understand customer behavior, improve products and efficiency, gain competitive advantages, improve sales and marketing, and gain visibility across the organization. Determining if an organization needs BI involves assessing if the organization has data but no useful information, relies solely on IT for reports, or uses spreadsheets without dedicated BI software. Tracking the right metrics like quantitative vs qualitative, actionable vs vanity, reporting vs exploratory, correlated vs causal, and lagging vs leading metrics helps organizations focus on what
Introduction to Business Data Analytics
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
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Business Intelligence Consultants How to Choose the Right One? - By DataToBiz
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一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
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◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
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◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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Business analytics
- 1. Business Analytics Presented by Sagnik Banerjee
- 2. CONTENTS : What is Business Analytics? Types Importance Where we can use BA? Benefits Challenges Tools
- 3. What is Business Analytics? BUSINESS ANALYTICS IS A FIELD THAT DRIVES PRACTICAL, DATA-DRIVEN CHANGES IN A BUSINESS. IT IS A PRACTICAL APPLICATION OF STATISTICAL ANALYSIS THATFOCUSES ON PROVIDING ACTIONABLE RECOMMENDATIONS. ANALYSTS IN THIS FIELD FOCUS ON HOW TO APPLY THE INSIGHTS THEY DERIVE FROM DATA. THEIR GOAL IS TO DRAW CONCRETE CONCLUSIONS ABOUT A BUSINESS BY ANSWERING SPECIFIC QUESTIONS ABOUT WHY THINGS HAPPENED, WHAT WILL HAPPEN AND WHAT SHOULD BE DONE.
- 4. Types of BA four categories: Anticipatory analytics: Predictive analytics: Descriptive analytics: Prescriptive analytics
- 5. Anticipatory analytics: Building on the foundation of predictive analytics, anticipatorycan identify and adjust predictions based on inflectionpoints such as the acceleration and deceleration ofcertain business behaviors or sudden change in business direction. Anticipatory analytics helps to anticipate the future needs of a business before they show obvious signs in their respective opportunity/riskprofile.
- 6. Predictive analytics: Algorithms use real-worlddata to help businesses understand the most probable outcome of a given action.
- 7. Descriptive analytics: Computingpower utilizescurrent performance metricsto analyze why a specific outcomeoccurred.
- 8. Prescriptive analytics: Analytics applications simulate how variables may affect outcomes, providing guidance on the best course of action.
- 9. Importance Enhance Customer Experience Make Informed Decisions Reduce Employee Turnover Improve Efficiency Identify Frauds Improved Advertising Tackle Problems Better Product Management
- 10. Where we can use BA ? Business analytics has also come to play a pivotal role in the manufacturing sector. It can use the data to offer meaningful insights into inventory management,supply chain management, the performance of targets, and risk mitigation plans. Also, BA tools can help companies scale up the efficiency of their operations. Business analytics is the process of using quantitative methodsto derive meaning from data in order to make informed business decisions.
- 11. Benefits Analytics helps you measurehow much of your mission statement is accomplished Analytics Encourages SmartDecision-Making Analytics Provides Clearer Insights Through DataVisualization Analytics Keep You Updated Analytics Offer Efficiency
- 12. Challenges: Misconception of BA's scopeof work. There are differences between Business Analyst's actual functions and tasks they really should perform. Created specifications do not satisfy the needs of the development team. Changing requirements or businessneeds. Conflicts with stakeholders. Undocumented processes.
- 13. Tools we need to use to become a buisness analyst / data analyst : Power BI Google docs Microsoft Advanced Excel Microsoft SQL Python Tableau MACHINE LEARNING AI
- 14. Business analytics is all about decision-making—not the way it was done in the past, which relied heavily on experience, gut feeling, and intuition, but the one that relies on data/evidence and computational/mathematical/statistical sciences.
- 15. END THANK YOU