Business analytics uses data to help organizations make better decisions and craft business strategies. As companies generate vast amounts of data, there is a need for professionals with data analysis skills. Leading companies are using analytics not just to improve operations but launch new business models. While some industries and digital natives have captured opportunities, much potential value from analytics remains untapped, especially in manufacturing, healthcare, and the public sector. For companies to succeed in an increasingly data-driven world, analytics must be incorporated strategically and supported by the right talent, processes, and infrastructure.
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.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
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.
Business analytics can be useful in personal decision making, like choosing a chair for the home. Factors like price, style, comfort, and reviews can be analyzed to identify the best option. Customer ratings and comments give insights into other people's experiences. This helps make an informed choice accounting for preferences and needs. Analytics provides objectivity that improves decision quality compared to relying only on subjective opinions.
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
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.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
Sit717 enterprise business intelligence 2019 t2 copy1NellutlaKishore
This document discusses data mining techniques and business intelligence. It begins with an introduction to different data mining techniques like clustering, statistical analysis, visualization, classification, neural networks, rules, and decision trees. It then provides more detail on statistical techniques, explaining that they help analyze large datasets. The document evaluates how big data and business intelligence are related, concluding that while they are different concepts, they need to work together to effectively analyze data and make smart business decisions. Big data provides the large datasets, while business intelligence extracts useful information from those datasets.
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.
Business analytics can be useful in personal decision making, like choosing a chair for the home. Factors like price, style, comfort, and reviews can be analyzed to identify the best option. Customer ratings and comments give insights into other people's experiences. This helps make an informed choice accounting for preferences and needs. Analytics provides objectivity that improves decision quality compared to relying only on subjective opinions.
1. The document discusses shifts in analytics and big data, including that the majority of organizations now realize returns on analytics investments within a year, and that while customer focus remains important, organizations are increasingly using data and analytics to improve operations.
2. It also notes that many organizations are transforming processes by integrating digital capabilities, and that the value driver for big data has shifted from volume to velocity - the ability to quickly move from data to action.
3. Speed is now the key differentiator, as data-driven organizations with capabilities for broad, fast analytics usage and agile technical infrastructure are creating significant business impacts.
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
Data Science - Part I - Sustaining Predictive Analytics CapabilitiesDerek Kane
This is the first lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Workforce Analytics-Big Data in Talent Development_2016 05Rob Abbanat
1) The document discusses how workforce analytics uses big data approaches to improve talent management and recruiting. It outlines a 5-step process for implementing workforce analytics: clarifying the problem, determining metrics, gathering data, analyzing the data, and presenting results visually.
2) Most companies are still only reporting workforce analytics data, while few are able to forecast or simulate results. Examples are given of how some companies have used workforce analytics to optimize retention, promotions, and talent acquisition strategies.
3) The meeting discussed how workforce analytics can help move companies from decisions based on hunches to data-driven models, showing clearer links between talent expenditures and organizational performance.
IBM observed that the world is becoming increasingly instrumented, interconnected, and intelligent. In response, IBM developed its Smarter Planet initiative to help organizations benefit from a smarter world. Through over 20,000 engagements, IBM has found that big data and analytics can improve outcomes for individuals, organizations, and society. Organizations that implement big data and analytics are differentiating themselves and outperforming competitors by gaining new insights. To fully benefit, organizations must build an analytics culture, be proactive about privacy/security/governance, and invest in a big data/analytics platform that can handle all types of data and analytics.
1) The document discusses how big data and analytics can provide businesses with competitive advantages by generating insights from vast amounts of data to improve business outcomes.
2) It outlines three key steps for a successful big data and analytics implementation: building an analytics-driven culture, prioritizing privacy/security/governance, and investing in an integrated analytics platform.
3) Examples are provided of how various organizations have used big data and analytics to optimize operations, acquire and retain customers, manage risk, improve social services, and more.
Better business outcomes with Big Data AnalyticsBillington K
1) The document discusses how big data and analytics can provide businesses with competitive advantages by generating insights from vast amounts of data to improve business outcomes.
2) It outlines three key steps for a successful big data and analytics implementation: building an analytics-driven culture, prioritizing privacy/security/governance, and investing in an integrated analytics platform.
3) Examples are provided of how various organizations have used big data and analytics to optimize operations, acquire and retain customers, manage risk, improve social services, and more.
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
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 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
The document discusses the rise of prescriptive analytics and its importance. Prescriptive analytics provides recommendations on what actions companies should take based on insights generated from descriptive and predictive analytics. It uses optimization and simulation algorithms to find solutions and recommend actions. There is high demand for prescriptive analytics as it allows companies to take quick actions based on data instead of just analyzing past data. The document then provides examples of industries using prescriptive analytics like oil and gas to optimize fracking and healthcare to improve facilities and reduce costs.
The Future of Analytics: Predict, Optimize, SucceedUncodemy
In today's data-driven world, the importance of analytics cannot be overstated. Businesses across industries are realizing the power of harnessing data to gain valuable insights, make informed decisions, and drive growth.
1. Data science involves applying scientific methods and processes to extract knowledge and insights from data. It includes techniques like machine learning, statistical analysis, and data visualization.
2. Data science has many applications in fields like marketing, healthcare, banking, and government. It helps with tasks like demand forecasting, fraud detection, personalized recommendations, and policymaking.
3. The key characteristics of data science include business understanding, intuition, curiosity, and skills in areas like machine learning algorithms, statistics, programming, and communication. Data scientists help organizations make better decisions using data-driven insights.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
Business intelligence and IT governance are increasingly important for modern businesses. Business intelligence involves collecting and analyzing large amounts of data to help businesses make better decisions. It has evolved from early attempts by businesses to understand their own information and markets. Modern business intelligence utilizes tools like dashboards, scorecards, and data warehouses. IT governance ensures that business and IT strategies are aligned and that information technology supports business objectives. Business intelligence 2.0 takes analysis a step further by enabling more interactive and flexible analysis of both structured and unstructured data.
The document discusses how companies can fully harness the power of data analytics. It provides two key insights: 1) Companies must choose the right data, build predictive models, and transform capabilities. 2) They should develop business-relevant analytics, embed analytics in simple tools, and develop big data skills. The insights emphasize upgrading managerial analytics skills so decision-making is data-driven. Acting on these insights can help Indian managers lead a successful digital transformation.
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
This document discusses network security and provides an overview of common security threats and countermeasures. It defines security, explains why security is needed to protect information and resources, and identifies entities that are vulnerable to attacks. It then describes several common security attacks such as firewalls, intrusion detection systems, denial of service attacks, TCP attacks, packet sniffing, and social engineering. For each threat, it outlines associated countermeasures to mitigate risks and improve security.
Logistics and Managing Transportion.pptxcalf_ville86
Transportation is the backbone of logistics and accounts for 40-50% of total logistics costs. It facilitates the movement of goods and connects production facilities. The key modes of transportation are roadways, railways, waterways, airways, and pipelines. Choosing the right mode or combination depends on factors like type of goods, distance, costs etc. Effective transportation requires applying principles like economy of scale and distance to reduce costs. Containerization, network design, and route planning techniques further optimize the transportation system.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Workforce Analytics-Big Data in Talent Development_2016 05Rob Abbanat
1) The document discusses how workforce analytics uses big data approaches to improve talent management and recruiting. It outlines a 5-step process for implementing workforce analytics: clarifying the problem, determining metrics, gathering data, analyzing the data, and presenting results visually.
2) Most companies are still only reporting workforce analytics data, while few are able to forecast or simulate results. Examples are given of how some companies have used workforce analytics to optimize retention, promotions, and talent acquisition strategies.
3) The meeting discussed how workforce analytics can help move companies from decisions based on hunches to data-driven models, showing clearer links between talent expenditures and organizational performance.
IBM observed that the world is becoming increasingly instrumented, interconnected, and intelligent. In response, IBM developed its Smarter Planet initiative to help organizations benefit from a smarter world. Through over 20,000 engagements, IBM has found that big data and analytics can improve outcomes for individuals, organizations, and society. Organizations that implement big data and analytics are differentiating themselves and outperforming competitors by gaining new insights. To fully benefit, organizations must build an analytics culture, be proactive about privacy/security/governance, and invest in a big data/analytics platform that can handle all types of data and analytics.
1) The document discusses how big data and analytics can provide businesses with competitive advantages by generating insights from vast amounts of data to improve business outcomes.
2) It outlines three key steps for a successful big data and analytics implementation: building an analytics-driven culture, prioritizing privacy/security/governance, and investing in an integrated analytics platform.
3) Examples are provided of how various organizations have used big data and analytics to optimize operations, acquire and retain customers, manage risk, improve social services, and more.
Better business outcomes with Big Data AnalyticsBillington K
1) The document discusses how big data and analytics can provide businesses with competitive advantages by generating insights from vast amounts of data to improve business outcomes.
2) It outlines three key steps for a successful big data and analytics implementation: building an analytics-driven culture, prioritizing privacy/security/governance, and investing in an integrated analytics platform.
3) Examples are provided of how various organizations have used big data and analytics to optimize operations, acquire and retain customers, manage risk, improve social services, and more.
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
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 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
The document discusses the rise of prescriptive analytics and its importance. Prescriptive analytics provides recommendations on what actions companies should take based on insights generated from descriptive and predictive analytics. It uses optimization and simulation algorithms to find solutions and recommend actions. There is high demand for prescriptive analytics as it allows companies to take quick actions based on data instead of just analyzing past data. The document then provides examples of industries using prescriptive analytics like oil and gas to optimize fracking and healthcare to improve facilities and reduce costs.
The Future of Analytics: Predict, Optimize, SucceedUncodemy
In today's data-driven world, the importance of analytics cannot be overstated. Businesses across industries are realizing the power of harnessing data to gain valuable insights, make informed decisions, and drive growth.
1. Data science involves applying scientific methods and processes to extract knowledge and insights from data. It includes techniques like machine learning, statistical analysis, and data visualization.
2. Data science has many applications in fields like marketing, healthcare, banking, and government. It helps with tasks like demand forecasting, fraud detection, personalized recommendations, and policymaking.
3. The key characteristics of data science include business understanding, intuition, curiosity, and skills in areas like machine learning algorithms, statistics, programming, and communication. Data scientists help organizations make better decisions using data-driven insights.
The new ‘A and B’ of the Finance Function: Analytics and Big Data - -Evolutio...Balaji Venkat Chellam Iyer
Published in 2013, this White Paper discusses how the finance function would evolve with the combined forces of Big Data and Analytics and the levers that could help catalyze the change and has drawn upon the Global Trend Study conducted by Tata Consultancy Services (TCS) on how companies were investing in Big Data and deriving returns from it.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
Business intelligence and IT governance are increasingly important for modern businesses. Business intelligence involves collecting and analyzing large amounts of data to help businesses make better decisions. It has evolved from early attempts by businesses to understand their own information and markets. Modern business intelligence utilizes tools like dashboards, scorecards, and data warehouses. IT governance ensures that business and IT strategies are aligned and that information technology supports business objectives. Business intelligence 2.0 takes analysis a step further by enabling more interactive and flexible analysis of both structured and unstructured data.
The document discusses how companies can fully harness the power of data analytics. It provides two key insights: 1) Companies must choose the right data, build predictive models, and transform capabilities. 2) They should develop business-relevant analytics, embed analytics in simple tools, and develop big data skills. The insights emphasize upgrading managerial analytics skills so decision-making is data-driven. Acting on these insights can help Indian managers lead a successful digital transformation.
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
This document discusses network security and provides an overview of common security threats and countermeasures. It defines security, explains why security is needed to protect information and resources, and identifies entities that are vulnerable to attacks. It then describes several common security attacks such as firewalls, intrusion detection systems, denial of service attacks, TCP attacks, packet sniffing, and social engineering. For each threat, it outlines associated countermeasures to mitigate risks and improve security.
Logistics and Managing Transportion.pptxcalf_ville86
Transportation is the backbone of logistics and accounts for 40-50% of total logistics costs. It facilitates the movement of goods and connects production facilities. The key modes of transportation are roadways, railways, waterways, airways, and pipelines. Choosing the right mode or combination depends on factors like type of goods, distance, costs etc. Effective transportation requires applying principles like economy of scale and distance to reduce costs. Containerization, network design, and route planning techniques further optimize the transportation system.
Lesson 3 - Enterprise System Architecture.pptxcalf_ville86
This document discusses enterprise systems architecture and ERP systems. It examines the modules of ERP systems like finance, HR and sales. It describes common ERP architectures including three-tier architectures that separate data, application and presentation layers. The document also discusses service-oriented architectures, cloud computing and the implications of architecture decisions for management.
This document summarizes the key aspects of the Kimball Lifecycle approach to data warehousing. It describes the main phases including planning, requirements definition, dimensional modeling, ETL design, application development, deployment, maintenance, and growth. It explains the parallel tracks of technology, data, and business intelligence applications. Dimensional modeling concepts like star schemas and snowflake schemas are also defined.
This document provides an overview of application software and discusses several common types, including word processing, spreadsheet, database, and presentation graphics software. It describes key concepts for each type of application software, such as how to create and format documents in word processing and spreadsheet programs, organize data in databases using tables and queries, and design electronic slide shows using presentation graphics software. The document also covers general topics like software ownership rights, installed versus cloud-based software, and common commands found in many application programs.
Lesson 2 - The Internet, the Web, and Electronic Commerce.pptxcalf_ville86
This document provides an overview of the Internet, the World Wide Web, and electronic commerce. It discusses the origins and evolution of the Internet and Web. It describes how to access the Web using Internet service providers and browsers. It also covers various Internet applications and technologies like email, social media, search tools, e-commerce models, cloud computing, and the Internet of Things. The document aims to explain how individuals and businesses can effectively use Internet resources.
The document discusses database management systems (DBMS). It defines DBMS as software that collects, organizes, and provides access to data. The key components of a DBMS are hardware, software, data, procedures, and database access language. Normalization is also discussed as the process of organizing data into tables to avoid data redundancy and ambiguity. The goals of normalization include dividing tables, eliminating duplicated data, and defining relationships between tables.
Lesson 1 - Introduction to Enterprise Systems for Management.pdfcalf_ville86
The document provides an introduction to enterprise resource planning (ERP) systems. It discusses how ERP systems evolved from early inventory management and materials requirement planning systems used in the 1960s-1980s. ERP systems integrate core business functions such as accounting, finance, marketing, and human resources into a single system. The document outlines the components, architecture, benefits and limitations of ERP systems. It explains how ERP systems improve information sharing, standardize processes, and increase an organization's agility compared to earlier disconnected legacy systems.
Lessoon 1 - Information Technology, The Internet and You.pptxcalf_ville86
This document provides an overview of information technology concepts including:
- The parts of an information system are people, procedures, software, hardware, data, and the Internet.
- There are two main types of software: system software which manages computer resources, and application software which users directly interact with like word processors and browsers.
- Computers range from supercomputers to mainframes to personal computers (PCs) like desktops, laptops, tablets, smartphones, and wearables.
- Personal computer hardware includes the system unit containing the processor and memory, input devices like keyboards, output devices like monitors, storage devices like hard disks, and communication devices like modems.
- Data is stored electronically in
This document provides an overview of data warehousing and dimensional modeling concepts. It defines key terms like data warehouse and data mart. It explores reasons for data warehousing like the need for an integrated company-wide view of information. It describes common data warehouse architectures and components of the star schema model. It also discusses topics like slowly changing dimensions, data visualization, and data mining.
Definition of requirements for each project phases.pdfcalf_ville86
The document discusses the five key phases of project management: initiation, planning, execution, monitoring and control, and closure. It provides details on typical activities and objectives for each phase, including developing a project initiation document, creating a project plan and schedule, implementing the planned project activities, monitoring progress, and closing out the project upon completion.
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A data mart is a smaller subset of data from a data warehouse that is tailored to a specific business unit or function. It provides faster access to relevant data than searching an entire data warehouse. There are three main types of data marts - dependent, which get data from a data warehouse; independent, which access data directly from sources; and hybrid, which integrate multiple data sources. Data marts use either a star or snowflake schema to logically structure the data in dimension and fact tables for analysis. Implementing a data mart involves designing it, constructing the logical and physical structures, transferring data using ETL tools, configuring access, and ongoing management.
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3. Business analytics is a powerful tool in today’s
marketplace that can be used to make
decisions and craft business strategies. Across
industries, organizations generate vast amounts
of data which, in turn, has heightened the need
for professionals who are data literate and
know how to interpret and analyze that
information.
4. According to a study by MicroStrategy,
companies worldwide are using data to:
•Improve efficiency and productivity (64
percent)
•Achieve more effective decision-making (56
percent)
•Drive better financial performance (51
percent)
5. The research also shows that 65 percent of global enterprises plan to
increase analytics spending.
In light of these market trends, gaining an in-depth understanding of
business analytics can be a way to advance your career and make
better decisions in the workplace.
“Using data analytics is a very effective way to have influence in an
organization,” said Harvard Business School Professor Jan
Hammond, who teaches the online course Business Analytics, in
a previous interview. “If you’re able to go into a meeting and other
people have opinions, but you have data to support your arguments
and your recommendations, you’re going to be influential.”
Before diving into the benefits of data analysis, it’s important to
understand what the term “business analytics” means.
6. Business analytics is the process of using quantitative methods to derive
meaning from data to make informed business decisions.
Business Analytics may be defined as refining past or present business
data using modern technologies. They are used to build sophisticated
models for driving future growth. A general Business Analytics process may
include Data Collection, Data Mining, Sequence Identification, Text Mining,
Forecasting, Predictive Analytics, Optimization, and Data Visualization.
Every business today produces a considerable amount of data in a specific
way. Business Analytics now are leveraging the benefits of statistical
methods and technologies to analyze their past data. This is used to
uncover new insights to help them make a strategic decision for the future.
7. Business Intelligence, a subset of the Business Analytics field, plays
an essential role in utilizing various tools and techniques such
as machine learning and artificial intelligence technologies to predict
and implement insights into daily operations.
Thus, Business Analytics brings together fields of business
management, and computing to get actionable insights. These
values and inputs are then used to remodel business procedures to
generate more efficiency and build a productive system.
After going through What is Business Analytics, let us understand
more about its evolution.
8. Evolution of Business Analytics
Technologies have been used as a measure to improve business
efficiency since the beginning. Automation has played a considerable
role in managing and performing multiple tasks for large
organizations. The unprecedented rise of the internet and information
technology has further boosted the performance of businesses.
With advancement today, we have Business Analytics tools that
utilize past and present data to give businesses the right direction for
their future.
As we now have a stronghold on What is Business Analytics, let us
next look into the types of business analytics techniques.
9. Types of Business Analytics Techniques
Business analytics techniques can be segmented in the following
four ways:
1.Descriptive Analytics: This technique describes the past or present
situation of the organization's activities.
2.Diagnostic Analytics: This technique discovers factors or reasons
for past or current performance.
3.Predictive Analytics: This technique predicts figures and results
using a combination of business analytics tools.
4.Prescriptive Analytics: This technique recommends specific
solutions for businesses to drive their growth forward.
10. A complete business analytics life cycle
starts from raw data received from the
devices or services, then collecting data in
an unstructured type, then processing and
analyzing data to draw actionable insights.
These are then integrated into business
procedures to deliver better outcomes for
the future.
11. Business Analytics Applications
Business Analytics is now systematically
integrated across several applications in
the field of supply chain management,
customer relationship management,
financial management, human resources,
manufacturing, and even build smart
strategies for sports too.
12. Importance of Business Analytics
•Business analytics can transform raw data into more
valuable inputs to leverage this information in decision
making.
•With Business Analytics tools, we can have a more
profound understanding of primary and secondary data
emerging from their activities. This helps businesses refine
their procedures further and be more productive.
•To stay competitive, companies need to be ahead of their
peers and have all the latest toolsets to assist their decision
making in improving efficiency as well as generating more
profits.
13. The Scope of Business Analytics
Business Analytics has been applied to a wide variety of
applications. Descriptive analytics is thoroughly used by businesses
to understand the market position in the current scenarios.
Meanwhile, predictive and prescriptive analytics are used to find
more reliable measures for businesses to propel their growth in a
competitive environment.
In the last decade, business analytics is among the leading career
choices for professionals with high earning potential and assisting
businesses to drive growth with actionable inputs.
We have understood well about what Business Analytics is, let us
next understand its benefits.
14. The Benefits of Business Analytics
To club in one phrase: Business Analytics brings actionable insights
for businesses. However, here are the main benefits of Business
Analytics:
1.Improve operational efficiency through their daily activities.
2.Assist businesses to understand their customers more precisely.
3.Business uses data visualization to offer projections for future
outcomes.
4.These insights help in decision making and planning for the future.
5.Business analytics measures performance and drives growth.
6.Discover hidden trends, generate leads, and scale business in the
right direction.
15. Difference Between Business Intelligence and Business
Analytics
Business Intelligence(BI) uses the past and present to
identify trends and patterns in the organizational
procedures, while Business Analytics determines the
reasons and factors that led to present situations. Business
Intelligence focuses mainly on descriptive analysis, while
Business Analytics deals with predictive analysis. BI tools
are part of Business Analytics that helps understand the
Business Analytics process better.
16. A Career in Business Analytics
The role of Business Analytics professionals may change accordingly to meet
organizational goals and objectives. Several individual profiles are closely
associated with business analytics when dealing with data.
In this competitive age, business analytics has revolutionized the procedures to
discover intelligent insights and gain more profits using their existing methods
only. Business Analytics Techniques also help organizations personalize
customers with more optimized services and even include their feedback to
create more profitable products. Large organizations today are now competing to
stay top in the markets by utilizing practical business analytics tools.
Several business analytics tools are available in the market that offers specific
solutions to match requirements. Professionals might need business analytics
skills, like understanding and expertise of statistics or SQL to manage them.
17. The age of analytics: Competing in a data-driven world
18. Big data’s potential just keeps
growing. Taking full advantage
means companies must incorporate
analytics into their strategic vision
and use it to make better, faster
decisions.
19. Is big data all hype? To the contrary: earlier research
may have given only a partial view of the ultimate
impact. A new report from the McKinsey Global Institute
(MGI), The age of analytics: Competing in a data-driven
world, suggests that the range of applications and
opportunities has grown and will continue to expand.
Given rapid technological advances, the question for
companies now is how to integrate new capabilities into
their operations and strategies—and position
themselves in a world where analytics can upend entire
industries.
20. A 2011 MGI report highlighted the transformational
potential of big data. Five years later, we remain
convinced that this potential has not been oversold. In
fact, the convergence of several technology trends is
accelerating progress. The volume of data continues to
double every three years as information pours in from
digital platforms, wireless sensors, virtual-reality
applications, and billions of mobile phones. Data-storage
capacity has increased, while its cost has plummeted.
Data scientists now have unprecedented computing
power at their disposal, and they are devising algorithms
that are ever more sophisticated.
21. Earlier, we estimated the potential for big data and
analytics to create value in five specific domains.
Revisiting them today shows uneven progress and a great
deal of that value still on the table (exhibit). The greatest
advances have occurred in location-based services and in
US retail, both areas with competitors that are digital
natives. In contrast, manufacturing, the EU public sector,
and healthcare have captured less than 30 percent of the
potential value we highlighted five years ago. And new
opportunities have arisen since 2011, further widening the
gap between the leaders and laggards.
22.
23. Leading companies are using their capabilities not only
to improve their core operations but also to launch
entirely new business models. The network effects of
digital platforms are creating a winner-take-most
situation in some markets. The leading firms have
remarkably deep analytical talent taking on various
problems—and they are actively looking for ways to
enter other industries. These companies can take
advantage of their scale and data insights to add new
business lines, and those expansions are
increasingly blurring traditional sector boundaries.
24. Where digital natives were built for analytics, legacy
companies have to do the hard work of overhauling
or changing existing systems. Adapting to an era of
data-driven decision making is not always a simple
proposition. Some companies have invested heavily
in technology but have not yet changed their
organizations so they can make the most of these
investments. Many are struggling to develop the
talent, business processes, and organizational
muscle to capture real value from analytics.
25. The first challenge is incorporating data and analytics
into a core strategic vision. The next step is developing
the right business processes and building capabilities,
including both data infrastructure and talent. It is not
enough simply to layer powerful technology systems on
top of existing business operations. All these aspects of
transformation need to come together to realize the full
potential of data and analytics. The challenges
incumbents face in pulling this off are precisely why
much of the value we highlighted in 2011 is still
unclaimed.
26. The urgency for incumbents is growing, since leaders are staking out
large advantages, and hesitating increases the risk of being
disrupted. Disruption is already happening, and it takes multiple
forms. Introducing new types of data sets (“orthogonal data”) can
confer a competitive advantage, for instance, while massive
integration capabilities can break through organizational silos,
enabling new insights and models. Hyperscale digital platforms can
match buyers and sellers in real time, transforming inefficient
markets. Granular data can be used to personalize products and
services—including, most intriguingly, healthcare. New analytical
techniques can fuel discovery and innovation. Above all, businesses
no longer have to go on gut instinct; they can use data and analytics
to make faster decisions and more accurate forecasts supported by
a mountain of evidence.
27. The next generation of tools could unleash
even bigger changes. New machine-
learning and deep-learning capabilities
have an enormous variety of applications
that stretch into many sectors of the
economy. Systems enabled by machine
learning can provide customer service,
manage logistics, analyze medical records,
or even write news stories.
28. These technologies could generate productivity
gains and an improved quality of life, but they
carry the risk of causing job losses and
dislocations. Previous MGI research found
that 45 percent of work activities could be
automated using current technologies; some
80 percent of that is attributable to existing
machine-learning capabilities. Breakthroughs in
natural-language processing could expand that
impact.
29. Data and analytics are already shaking up
multiple industries, and the effects will only
become more pronounced as adoption reaches
critical mass—and as machines gain
unprecedented capabilities to solve problems
and understand language. Organizations that
can harness these capabilities effectively will
be able to create significant value and
differentiate themselves, while others will find
themselves increasingly at a disadvantage.