What is this ‘Big Data’?
Introduction and Key words
Any secrets behind big data?
The 4V’s of Big Data
Big Data Analytics
What to do with this data?
Usefulness of Business Intelligence (BI)
Big data refers to large volumes of both structured and unstructured data that businesses accumulate on a daily basis. This data has potential business value if analyzed to gain insights that can help strategic decisions and improve operations. While vast amounts of data are created globally each day, only a small percentage is actually analyzed. Analyzing big data, when combined with analytics, allows businesses to reduce costs, make products more tailored to customers, and make smarter, faster decisions. It can also help detect fraud and issues in real-time.
Solving the BI Adoption Challenge With Report Consolidationibi
Check out the slides from a webcast with Rado Kotorov, chief innovation officer at Information Builders, on how to resolve data clutter in your organization with report consolidation.
View the webcast recording at: http://ow.ly/uzPP30alz3J
This document discusses Accenture's approach to data modernization. It outlines key trends in data-driven organizations, including democratizing data, incorporating new data sources, focusing on advanced analytics, adopting big data and hybrid architectures, and changing skills requirements. The document then presents a high-level 9-step approach to agile analytics that engages stakeholders, identifies value opportunities, formulates hypotheses, understands data sources, defines models, prepares data, prototypes and iterates, pilots and executes projects, and delivers actionable insights. It also notes some common challenges organizations face in data transformation, such as unrealistic technology expectations, inadequate delivery approaches, skills gaps, and poor data governance. Finally, it poses questions to help organizations assess their readiness
Check out slides from this 45-minute webcast to see what your organization needs to do to stay on top of the coming technology transformations and gain insight into upcoming trends in analytics.
View the webcast recording at: http://ow.ly/Rnu3307Umb9
Five Critical Success Factors for Embedded Analyticsibi
The document discusses five critical success factors for embedded analytics: 1) Tying embedded analytics to strategic business goals and planning, 2) Focusing on information visibility across the organization, 3) Ensuring better data access, 4) Providing real-time value, and 5) Ensuring constant visibility. It provides details on each success factor and examples of how embedded analytics can help achieve them. The document is presented as a slide deck for a presentation on embedded analytics.
BigInsights BigData Study 2013 - Exec SummaryBigInsights
The document summarizes the findings of a 2013 survey on big data conducted across Asia-Pacific. The key findings include:
- The majority of respondents do not understand the benefits big data could provide or have the skills and resources to pursue big data initiatives.
- However, most business leaders believe big data could help understand customers and business trends better and improve decision making.
- Respondents see potential in mining data from websites, social media, data warehouses for big data solutions.
- Adoption of Hadoop and NoSQL technologies is expected to increase over the next two years.
This document summarizes the key findings of the 2015 Big Data End User Study conducted by BigInsights. The study explored how organizations in the Asia Pacific region are adopting and using big data technologies. It found that data volumes are growing rapidly across industries and organizations are pursuing big data initiatives to drive business benefits like improved customer insights and supply chain optimization. However, challenges remain around integrating diverse data types and delivering big data infrastructure. The report provides insights into how organizations are applying big data analytics, the benefits they expect to achieve, and the challenges they face.
Here are 10 reasons to attend the Information Builders Summit 2017, June 5-8 at the Gaylord Texan in Grapevine, TX.
Learn more and register: http://www.informationbuilders.com/events/summit
Big data refers to large volumes of both structured and unstructured data that businesses accumulate on a daily basis. This data has potential business value if analyzed to gain insights that can help strategic decisions and improve operations. While vast amounts of data are created globally each day, only a small percentage is actually analyzed. Analyzing big data, when combined with analytics, allows businesses to reduce costs, make products more tailored to customers, and make smarter, faster decisions. It can also help detect fraud and issues in real-time.
Solving the BI Adoption Challenge With Report Consolidationibi
Check out the slides from a webcast with Rado Kotorov, chief innovation officer at Information Builders, on how to resolve data clutter in your organization with report consolidation.
View the webcast recording at: http://ow.ly/uzPP30alz3J
This document discusses Accenture's approach to data modernization. It outlines key trends in data-driven organizations, including democratizing data, incorporating new data sources, focusing on advanced analytics, adopting big data and hybrid architectures, and changing skills requirements. The document then presents a high-level 9-step approach to agile analytics that engages stakeholders, identifies value opportunities, formulates hypotheses, understands data sources, defines models, prepares data, prototypes and iterates, pilots and executes projects, and delivers actionable insights. It also notes some common challenges organizations face in data transformation, such as unrealistic technology expectations, inadequate delivery approaches, skills gaps, and poor data governance. Finally, it poses questions to help organizations assess their readiness
Check out slides from this 45-minute webcast to see what your organization needs to do to stay on top of the coming technology transformations and gain insight into upcoming trends in analytics.
View the webcast recording at: http://ow.ly/Rnu3307Umb9
Five Critical Success Factors for Embedded Analyticsibi
The document discusses five critical success factors for embedded analytics: 1) Tying embedded analytics to strategic business goals and planning, 2) Focusing on information visibility across the organization, 3) Ensuring better data access, 4) Providing real-time value, and 5) Ensuring constant visibility. It provides details on each success factor and examples of how embedded analytics can help achieve them. The document is presented as a slide deck for a presentation on embedded analytics.
BigInsights BigData Study 2013 - Exec SummaryBigInsights
The document summarizes the findings of a 2013 survey on big data conducted across Asia-Pacific. The key findings include:
- The majority of respondents do not understand the benefits big data could provide or have the skills and resources to pursue big data initiatives.
- However, most business leaders believe big data could help understand customers and business trends better and improve decision making.
- Respondents see potential in mining data from websites, social media, data warehouses for big data solutions.
- Adoption of Hadoop and NoSQL technologies is expected to increase over the next two years.
This document summarizes the key findings of the 2015 Big Data End User Study conducted by BigInsights. The study explored how organizations in the Asia Pacific region are adopting and using big data technologies. It found that data volumes are growing rapidly across industries and organizations are pursuing big data initiatives to drive business benefits like improved customer insights and supply chain optimization. However, challenges remain around integrating diverse data types and delivering big data infrastructure. The report provides insights into how organizations are applying big data analytics, the benefits they expect to achieve, and the challenges they face.
Here are 10 reasons to attend the Information Builders Summit 2017, June 5-8 at the Gaylord Texan in Grapevine, TX.
Learn more and register: http://www.informationbuilders.com/events/summit
Using Data Strategy Design to Build Data-Driven ProductsDatentreiber
Everyone is talking about Big Data, Deep Learning and Artificial Intelligence. But the reality in some companies looks different, especially when developing new products: (the relevant) data is missing. Without predictive models and recommendation systems cannot be trained and the value is consequently low. This so called cold-start problem is especially concerning startups, since without own data treasure the companies are missing a defendable unique value proposition. Successful startups solve this problem with the help of „Data Traps“ and develop products with „Data Network Effects“. What exactly stands behind these terms and how companies design their own successful and data-driven products, will be demonstrated by Martin Szugat based on samples from his occupation as Data Strategy Consultant.
Winning in Today's Data-Centric Economy (Part 1)Alexander Loth
This document discusses how data and analytics are central to success in today's digital economy. It notes that existing business systems were built for products and transactions, not long-term customer relationships, and data is now everywhere. The document advocates developing a data-centric strategy that uses analytics to extract value from data and wrap data around customers to create business value. It provides examples of how analytics can reduce report creation time and help organizations better understand their data, customers, and make strategic decisions.
Hiring and Developing Analytics Talent in the CPG and Retail Industry - Mohi...Molly Alexander
1. The document discusses how to hire and retain analytics talent in the consumer packaged goods industry. It emphasizes the need for strong analytics leadership to develop a clear talent strategy and define analytics roles.
2. It highlights the importance of "analytic translators" who can communicate between business and technical teams to identify high-impact use cases. It also stresses prioritizing analytic workstreams and building expertise within each.
3. The document provides examples of when to buy versus build analytics capabilities and outlines what data scientists, engineers, and visualizers want in their roles to aid retention. It emphasizes delivering on promises and a culture of innovation.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
1) The document discusses how to strategically deploy big data using an agile analytics approach called "Agile Analytics".
2) Agile Analytics advocates thinking big but starting small by focusing on small, bite-sized use cases that can produce results quickly.
3) It also recommends embedding three roles - an enthusiast project manager, data scientist, and domain expert - to help connect analytics to business value.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
This document discusses the architecture and components of business intelligence (BI). It defines BI as a set of applications used to create, manage, and analyze data warehouses to support the decision making process by answering questions about historical data. Business analytics is defined as using extensive data analysis, statistical modeling, and data mining to provide insights that boost decision making. Some key differences between BI and business analytics are that BI focuses on answering questions about past performance using reporting and dashboards, while business analytics uses advanced quantitative analysis and predictive modeling. The document recommends studying how users analyze data, how results are presented, and how managers implement results to understand how BI can be divided into data analysis and results presentation components in organizations.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Data Driven Strategy Analytics Technology Approach CorporateSlideTeam
This complete deck can be used to present to your team. It has PPT slides on various topics highlighting all the core areas of your business needs. This complete deck focuses on Data Driven Strategy Analytics Technology Approach Corporate and has professionally designed templates with suitable visuals and appropriate content. This deck consists of total of thirteen slides. All the slides are completely customizable for your convenience. You can change the colour, text and font size of these templates. You can add or delete the content if needed. Get access to this professionally designed complete presentation by clicking the download button below. https://bit.ly/3yjusdQ
Robert Piddocke is the Vice President of Channel and Business Development at Concept Searching, a company that provides text analytics solutions to extract concepts and metadata from unstructured information. Concept Searching was founded in 2002 and focuses on managing both structured and unstructured data through automatic concept identification, content tagging, and taxonomy management. It has a client base of Fortune 500 companies and can analyze various data types including documents, audio, video and images.
This document discusses how genomics research and personalized medicine can benefit from agile principles and practices. It provides examples of how the Human Genome Project and next-generation genome sequencing have used rapid, incremental approaches. The document also presents a case study where Tieto helped reduce data preparation time, storage needs, and release cycles for a genomic data warehouse through techniques like data virtualization and cloud computing. Finally, it argues that science and software development share commonalities and that agile transformations allow Tieto to better support fields involving genomics and personalized diagnostics.
From Foundation to Mastery – Building a Mature Analytics Roadmap - Manav MisraMolly Alexander
The document outlines steps to build a mature analytics roadmap for a financial services organization. It discusses:
1) Establishing a leadership team to create an analytics strategy and bridge business needs with data solutions.
2) Developing data products that use analytics to provide value and insights to end users.
3) Implementing a modern data science platform to manage data, run analytics, and deploy models at scale.
4) Implementing data management practices like a data catalog and data lake to break down silos and ensure governance.
5) Fostering a data-driven culture with executive sponsorship of data products and integration with business units.
This document discusses analytics and data-driven product design and decision making. It provides tips for creating good metrics that are understandable, comparative, and meaningful. It warns against common pitfalls like confirmation bias and discusses using controlled experiments and an exploratory approach to find unexpected insights. Iterating products through measurements and changes in production is recommended to build relationships with customers.
JetFerry: Background Info on Business IntelligenceTechFerry
Business intelligence (BI) aims to transform data into meaningful information to support business decisions and growth. BI evolved from manually assembled reports in the pre-1970s to performance management occurring twice a year in the 1990s to today's real-time analytics and predictive insights. BI blends tools, processes, and skills to analyze large volumes of data, allowing effective decisions through access to clean, high-quality data visualization and insights. The future of BI includes enhanced data-driven insights, identifying strategies through natural language queries, and more personalized and real-time intelligent decision automation.
The document is a presentation about big data from a business perspective. It discusses why businesses need big data to make more informed decisions that can increase profits and reduce costs. Specifically, it argues that analyzing more accurate data enabled by big data technologies can lead to more confident decision making and better operational efficiencies. The presentation covers key topics like the four pillars of big data, case studies, challenges, and tools/techniques for working with big data.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
Introduction to syvylyze analytics: Business insights using visual analyticsPaarth Savale
Syvylyze Analytics is a professional services company that uses data visualization tools like Tableau to transform client data into clear and actionable visualizations. They help clients answer business questions and make smarter decisions. Their services include data visualization to create reports and dashboards, business analytics to provide insights, and managed business intelligence as a service where they manage all client reporting and analytics needs. Their goal is to help clients gain faster and deeper insights from their data to support informed decision making.
Strategy session 5 - unlocking the data dividend - andy steerAndy Steer
"A recent study completed by IDC examined the economic benefits accrued to organisations that made basic levels of investment in distinct areas of analytics and data management compared with the benefits accrued by organisations that opted for a broader and more diverse set of investments. The conclusion was that the leading organisations expect to capture in excess of $1.5 trillion more in value from their data and analytics initiatives over the next 4 years. This represents a 60% higher data dividend for the leading organisations.
To achieve these benefits organisations need to embrace the changing reality of the new data driven society and make a break from the beliefs and best practices inherent in traditional Business Intelligence programmes.
During the presentation Andy will expand on the data dividend concept, outline the 4 key investment areas that should be getting your attention and perhaps most importantly, explain how your existing SAP BusinessObjects technology can help you take your share of the estimated £53 billion UK data dividend."
BUSINESS INTELLIGENCE (BI) - Definition, Process, and BenefitDipstrategy
have you known what business intelligence is? the process? and also the benefit for your decision making. business intelligence become completely useful information to be a supporting system for decision making
Using Data Strategy Design to Build Data-Driven ProductsDatentreiber
Everyone is talking about Big Data, Deep Learning and Artificial Intelligence. But the reality in some companies looks different, especially when developing new products: (the relevant) data is missing. Without predictive models and recommendation systems cannot be trained and the value is consequently low. This so called cold-start problem is especially concerning startups, since without own data treasure the companies are missing a defendable unique value proposition. Successful startups solve this problem with the help of „Data Traps“ and develop products with „Data Network Effects“. What exactly stands behind these terms and how companies design their own successful and data-driven products, will be demonstrated by Martin Szugat based on samples from his occupation as Data Strategy Consultant.
Winning in Today's Data-Centric Economy (Part 1)Alexander Loth
This document discusses how data and analytics are central to success in today's digital economy. It notes that existing business systems were built for products and transactions, not long-term customer relationships, and data is now everywhere. The document advocates developing a data-centric strategy that uses analytics to extract value from data and wrap data around customers to create business value. It provides examples of how analytics can reduce report creation time and help organizations better understand their data, customers, and make strategic decisions.
Hiring and Developing Analytics Talent in the CPG and Retail Industry - Mohi...Molly Alexander
1. The document discusses how to hire and retain analytics talent in the consumer packaged goods industry. It emphasizes the need for strong analytics leadership to develop a clear talent strategy and define analytics roles.
2. It highlights the importance of "analytic translators" who can communicate between business and technical teams to identify high-impact use cases. It also stresses prioritizing analytic workstreams and building expertise within each.
3. The document provides examples of when to buy versus build analytics capabilities and outlines what data scientists, engineers, and visualizers want in their roles to aid retention. It emphasizes delivering on promises and a culture of innovation.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
1) The document discusses how to strategically deploy big data using an agile analytics approach called "Agile Analytics".
2) Agile Analytics advocates thinking big but starting small by focusing on small, bite-sized use cases that can produce results quickly.
3) It also recommends embedding three roles - an enthusiast project manager, data scientist, and domain expert - to help connect analytics to business value.
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
Business Analytics & Big Data Trends and Predictions 2014 - 2015Brad Culbert
Brad Culbert, Executive Director of Strategy & Solutions at Bistech, discusses business analytics trends and predictions for 2014-2015. Some key trends include the consumerization of business IT, disruptive force of cloud computing, and shortage of analytic skills. Visual data discovery and story boarding analytics will gain popularity, while decision management and cognitive computing will emerge. Next generation information management will focus on flexible governance for agile self-service data preparation. Cloud analytics adoption will increase significantly with a focus on cloud benefits. Predictive analytics and mobile analytics will see further mainstream adoption.
This document discusses the architecture and components of business intelligence (BI). It defines BI as a set of applications used to create, manage, and analyze data warehouses to support the decision making process by answering questions about historical data. Business analytics is defined as using extensive data analysis, statistical modeling, and data mining to provide insights that boost decision making. Some key differences between BI and business analytics are that BI focuses on answering questions about past performance using reporting and dashboards, while business analytics uses advanced quantitative analysis and predictive modeling. The document recommends studying how users analyze data, how results are presented, and how managers implement results to understand how BI can be divided into data analysis and results presentation components in organizations.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Data Driven Strategy Analytics Technology Approach CorporateSlideTeam
This complete deck can be used to present to your team. It has PPT slides on various topics highlighting all the core areas of your business needs. This complete deck focuses on Data Driven Strategy Analytics Technology Approach Corporate and has professionally designed templates with suitable visuals and appropriate content. This deck consists of total of thirteen slides. All the slides are completely customizable for your convenience. You can change the colour, text and font size of these templates. You can add or delete the content if needed. Get access to this professionally designed complete presentation by clicking the download button below. https://bit.ly/3yjusdQ
Robert Piddocke is the Vice President of Channel and Business Development at Concept Searching, a company that provides text analytics solutions to extract concepts and metadata from unstructured information. Concept Searching was founded in 2002 and focuses on managing both structured and unstructured data through automatic concept identification, content tagging, and taxonomy management. It has a client base of Fortune 500 companies and can analyze various data types including documents, audio, video and images.
This document discusses how genomics research and personalized medicine can benefit from agile principles and practices. It provides examples of how the Human Genome Project and next-generation genome sequencing have used rapid, incremental approaches. The document also presents a case study where Tieto helped reduce data preparation time, storage needs, and release cycles for a genomic data warehouse through techniques like data virtualization and cloud computing. Finally, it argues that science and software development share commonalities and that agile transformations allow Tieto to better support fields involving genomics and personalized diagnostics.
From Foundation to Mastery – Building a Mature Analytics Roadmap - Manav MisraMolly Alexander
The document outlines steps to build a mature analytics roadmap for a financial services organization. It discusses:
1) Establishing a leadership team to create an analytics strategy and bridge business needs with data solutions.
2) Developing data products that use analytics to provide value and insights to end users.
3) Implementing a modern data science platform to manage data, run analytics, and deploy models at scale.
4) Implementing data management practices like a data catalog and data lake to break down silos and ensure governance.
5) Fostering a data-driven culture with executive sponsorship of data products and integration with business units.
This document discusses analytics and data-driven product design and decision making. It provides tips for creating good metrics that are understandable, comparative, and meaningful. It warns against common pitfalls like confirmation bias and discusses using controlled experiments and an exploratory approach to find unexpected insights. Iterating products through measurements and changes in production is recommended to build relationships with customers.
JetFerry: Background Info on Business IntelligenceTechFerry
Business intelligence (BI) aims to transform data into meaningful information to support business decisions and growth. BI evolved from manually assembled reports in the pre-1970s to performance management occurring twice a year in the 1990s to today's real-time analytics and predictive insights. BI blends tools, processes, and skills to analyze large volumes of data, allowing effective decisions through access to clean, high-quality data visualization and insights. The future of BI includes enhanced data-driven insights, identifying strategies through natural language queries, and more personalized and real-time intelligent decision automation.
The document is a presentation about big data from a business perspective. It discusses why businesses need big data to make more informed decisions that can increase profits and reduce costs. Specifically, it argues that analyzing more accurate data enabled by big data technologies can lead to more confident decision making and better operational efficiencies. The presentation covers key topics like the four pillars of big data, case studies, challenges, and tools/techniques for working with big data.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
Introduction to syvylyze analytics: Business insights using visual analyticsPaarth Savale
Syvylyze Analytics is a professional services company that uses data visualization tools like Tableau to transform client data into clear and actionable visualizations. They help clients answer business questions and make smarter decisions. Their services include data visualization to create reports and dashboards, business analytics to provide insights, and managed business intelligence as a service where they manage all client reporting and analytics needs. Their goal is to help clients gain faster and deeper insights from their data to support informed decision making.
Strategy session 5 - unlocking the data dividend - andy steerAndy Steer
"A recent study completed by IDC examined the economic benefits accrued to organisations that made basic levels of investment in distinct areas of analytics and data management compared with the benefits accrued by organisations that opted for a broader and more diverse set of investments. The conclusion was that the leading organisations expect to capture in excess of $1.5 trillion more in value from their data and analytics initiatives over the next 4 years. This represents a 60% higher data dividend for the leading organisations.
To achieve these benefits organisations need to embrace the changing reality of the new data driven society and make a break from the beliefs and best practices inherent in traditional Business Intelligence programmes.
During the presentation Andy will expand on the data dividend concept, outline the 4 key investment areas that should be getting your attention and perhaps most importantly, explain how your existing SAP BusinessObjects technology can help you take your share of the estimated £53 billion UK data dividend."
BUSINESS INTELLIGENCE (BI) - Definition, Process, and BenefitDipstrategy
have you known what business intelligence is? the process? and also the benefit for your decision making. business intelligence become completely useful information to be a supporting system for decision making
Business intelligence (BI) refers to technologies and applications used to analyze data and provide access to information about company operations. It involves collecting data from across the organization, storing it in data warehouses or data marts, and providing tools to access and analyze the data. The goal of BI is to help business users make more informed decisions by providing insights from large amounts of internal and external data. Key aspects of BI include data warehousing, online analytical processing, reporting, dashboards, scorecards, and data mining.
Business intelligence and analytics both refer to maximize the value of your data to make better decisions, ALTEN CAlsoft Labs helps
enterprises accelerate business intelligence by providing the most comprehensive, integrated and easy-to-use reporting and analytics features with its industry specific analytics solutions and best in-class technology.
Business intelligence (BI) uses data about past and present to help companies make better decisions for the future. BI provides timely, accurate insights that are valuable and can be acted upon. It helps companies operate more efficiently and profitably by supporting better strategic and tactical decision making. As BI systems evolve to deliver analytics to mobile devices in near real-time, more companies are using BI to promote a data-driven culture and rational decision making processes.
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
This report is an outcome of research on topic 'Business Intelligence', which is a hot topic now. This research report is prepared for the partial fulfillment of the requirements for 'Current Developments Module' of B.Sc.Computing degree.
It demonstrates details of the Business Intelligence in today's world and explains BI architecture. It also provides detailed analysis on its use in the current business environment.
Panelists from a large company, a small company and a software consulting firm will share insights on how their companies are tackling the arena of Big Data and how to leverage a variety of data sources for strategic decision-making.
This document provides an overview of big data and how it differs from traditional business intelligence (BI). It explains that big data involves bringing computation to the data rather than bringing data to computation. This allows for analysis of large, unstructured data sources like IoT data, social media, and search engines. Big data also offers benefits like fast decision making, additional data dimensions, dynamism, and new business opportunities. The document provides advice on developing a big data strategy including identifying needs and stakeholders, creating standards, and starting small with prototypes before growing capabilities. It emphasizes treating big data as the center of BI initiatives.
Real Life, Strategic BI Strategy for your IT Organizationmayamidmore
This document summarizes key aspects of developing a strategic business intelligence (BI) approach, including fitting BI within an overall IT strategy, implementing BI competency centers and standards, and using BI to improve IT performance. It discusses establishing a BI strategy to determine priority business questions and initiatives. The document also provides examples of strategic BI implementations and outlines stages of BI maturity from an initial, siloed approach to an integrated, strategic enabler of business goals.
This document summarizes the changes in the scope of business intelligence (BI) over recent years. It discusses how BI has evolved from being IT-managed standard reporting to a more self-service, visual, and interactive environment. Key changes highlighted include BI tools now being used and managed by business users, greater flexibility for users to explore and create custom reports, advanced visualizations and interactive dashboards, and the inclusion of more advanced analytics beyond standard SQL. The blurring of lines between reporting and analytics tools and between IT and business user roles is seen as an overall positive development that enables more flexibility, discovery, and insight.
Business intelligence (BI) involves gathering data from various sources, analyzing it to gain insights, and presenting information to help business users make better decisions. BI provides a single, accurate view of data across departments through enterprise-wide reporting, analysis, and decision-making platforms. It leads to fact-based decision making and consistent information. Key benefits of BI include improved measurement, identification of trends and problems, enhanced data visualization and decision making, and the ability to answer important questions about past, present and future business performance.
Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. BA is used by companies committed to data-driven decision-making to gain insights that inform business decisions and can be used to automate and optimize business processes. BA techniques break down into basic business intelligence, which involves collecting and preparing data, and deeper statistical analysis. True data science involves more custom coding and open-ended questions compared to most business analysts.
The document discusses strategies for deriving business value from big data analytics. It emphasizes that collecting large amounts of data is only the first step, and the key is using analytics to find useful insights hidden in the data. It provides guidance on focusing big data initiatives by addressing data accuracy, storage needs, query performance, and scalability when planning projects. Additionally, it discusses how conferences have focused on how to deliver big data analytics to users in a way that connects to real business value.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
2. EVERY 60 SECONDS
204 million emails sent
1.8 million Facebook likes generated
278,000 Tweets sent
200,000 photos uploaded to
Facebook
Around 100 hours of video
uploaded to YouTube
3. I. What is this ‘Big Data’?
Introduction and Key words
II. Any secrets behind big data?
The 4V’s of Big Data
III. Competing with Analytics
Big Data Analytics
IV. What to do with this data?
Usefulness of Business Intelligence (BI)
V. Conclusion
VI. References
CONTENTS
4. INTRODUCTION & KEY WORDS
Data visualization: refers to the approaches and tools
used to visually understand the insights from data to
prove/disprove a hypothesis.
Big Data: huge amount of data, that have not been handled
by the traditional data management systems.
Analytics: knowledge discovery and extracting valuable
trends in data that can be visualized for insights and patterns.
Business Intelligence (BI): technique, tool required to
collect, store, analyse data into valuable information and
benefit from analysing and making efficient business decisions.
6. BACKGROUND
Just lot’s of data!
Or is it?
Unstructured data
Text, video, audio, etc.
Tweets, likes, emails, etc.
Data is the new oil
But renewable!
7. THE FOUR V’S OF BIG DATA
Volume: huge number of transactional data
stored every second, machine and sensor
data, social media, and enterprise data is
being collected and stored.
Velocity: data should be dealt in real time, for
instance streaming data may require to be
executed in real time.
8. THE FOUR V’S OF BIG DATA (CONTINUED)
Variety: data also varies with respect to
structure. Some data are not structured.
Veracity: the trustworthiness (uncertainty of
data) of the data, where the data came from?
9. BIG DATA ANALYTICS
Descriptive: to provide insight into what has
happened?
Diagnostic: services demand, customer
segments, any trend?
Predictive: who need more, helps model
and forecast what might happen?
Prescriptive: seeks to determine the best
solution or outcome among various
choices, given the known parameters.
10. BIG DATA ANALYTICS (CONTINUED)
Reports
Scorecards
Dashboards
Ad hoc analysis
Visualization
12. USEFULNESS OF BI
Optimize business processes
Better decision making on every level in
the organization based on fact
Create better customer experience with
better information
Improve competitiveness
Increase revenues
13. CONCLUSION
Big Data is renewable oil
Remember the 4 V’s of Big Data
Analytics can be Descriptive,
Diagnostic, Predictive or Prescriptive
Business Intelligence is crucial in business
decision making
14. REFERENCES
Big Data Analytics, Advanced Analytics in Oracle Database. An
Oracle White Paper. March 2013.
Tomas Eklund, 4.5.2015. ‘DW Lecture 4: Big Data, ÅAU.
Marko Grobelnik, 8.5.2012. ‘Big Data Tutorial’, [online]. URL:
http://www.planet-
data.eu/sites/default/files/presentations/Big_Data_Tutorial_part4.
pdf
Big Data, What it is & why it matters, [online]. URL:
http://www.sas.com/en_us/insights/big-data/what-is-big-
data.html.
Big Data for Small and Medium-Sized Businesses, [online]. URL:
http://www.ibm.com/midmarket/us/en/article_SmarterAnalytics_
1308.html.
Doug Laney, Claiming Gartner’s Construct for Big Data, [online].
URL: http://blogs.gartner.com/doug-laney/deja-vvvue-others-
claiming-gartners-volume-velocity-variety-construct-for-big-data/.