Beginner’s Primer on Business Intelligence
Business Intelligence is a set of process, architecture, technologies that converts the raw data into meaningful information which helps any business in making productive decision.
Business Intelligence directly affects the strategic and progressive decisions of any organization by making the use of historical data and facts. It also helps in analysing the data and explains the end users the nature of the business in the form of Reports, Graphs and Dashboards.
The session will cover the following topics:
-What is Business Intelligence
-Where does BI fit within an organization
-Advantages of BI systems
-Various types of BI users
-Various BI Tools
-Careers in BI
-Q & A
Follow Us For More Updates
Facebook Page https://www.facebook.com/CLTConsultin...
LinkedIn Page http://linkedin.com/company/cltcsi
Twitter Page https://twitter.com/CLTCSGLOBAL
YouTube Page https://www.youtube.com/channel/UCzqO...
Instagram Page https://www.instagram.com/clt_consult...
#businessintelligence #bi #bitool #oraclebi #otbi #oracle #cltconsulting
This document discusses an iconic San Diego tech company that focuses on online advertising. It introduces the founders Chern Lee and Valentino Vaschetto and their expertise in areas like search optimization, coding, and web development. The company's strategy involves leveraging marketing, data analysis, product development and other areas to better understand customers, increase awareness, and customize coding technologies for clients. By combining data-driven marketing strategies with analysis and new approaches, the company aims to provide personalized solutions and results for clients. Background is provided on founder Chern Lee's early career and success in online marketing and growing several companies specializing in areas like lead generation and ecommerce. The vision is to bring marketing and technology together in innovative ways while nurturing
Description
Organizations often struggle to successfully integrate analytics into their business. While this is always a challenging task, there are several approaches analytics teams can take to maximize their impact. This talk provides actionable recommendations to help analytics teams succeed and become essential to their company.
Here a few of the specific topics covered:
*How a small analytics team should prioritize projects to maximize impact. New analytics teams are often under-resourced and un-specialized. Focusing on a specific area makes it easier for the team to become experts and add value.
*Strategies to engage with and become essential to business stakeholders. Too often, analytics teams and the business stakeholders they serve work in completely separate workflows. Building strong relationships with business stakeholders is critical for an analytics team's success.
*Methods to increase the overall analytics literacy of your company. Evangelizing the benefits of analytics paves the way for future success.
*Building a network of analytics-minded people. Outside of the official analytics team, companies often have many other employees interested in learning about and leveraging this topic. Building a network of these people can be extremely beneficial.
Speaker
Jai Bansal, Aetna, Senior Data Scientist
Events-Based Research (EBR) is a method of collecting market research data from corporate events by surveying attendees. EBR allows companies to measure their return on investment from events, understand if they are reaching their target audience, and gain meaningful feedback from participants. Surveys are completed by attendees during events when they have time and no distractions, resulting in response rates usually above 80%. Data collected from multiple events through EBR can provide valuable insights through comparative analysis and help identify new trends, establish benchmarks, and unveil new opportunities.
The document discusses challenges with hiring data scientists and suggests alternative approaches. It recommends empowering small cross-functional data-oriented teams explicitly tasked with delivering measurable business benefits. This builds internal data capabilities rather than just hiring expertise. It also stresses the importance of making data science a cultural value throughout the organization so that all employees understand basic principles and practices of data science.
"Simplify Your Analytics Strategy" by Narendra MulaniSai Sandeep MN
Companies can get stuck trying to analyze all possible analytics opportunities instead of focusing on what matters most to customers, stakeholders, and employees. This document provides steps to simplify an analytics strategy and generate useful insights. It recommends accelerating data through a hybrid data platform and emerging technologies. A path to insight is unique for each organization and involves various elements like goals, data types and sources. Simplifying an analytics strategy can provide managerial insights through discovery-based or hypothesis-based approaches depending on whether the problem or solution is known.
The document provides recommendations for simplifying an analytics strategy in 3 key steps:
1) Accelerate data processing to enable fast insights and outcomes.
2) Delegate analytical work to technologies like business intelligence, data discovery, analytics applications, and machine learning to uncover patterns and insights.
3) Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem is known or unknown, with the goal of deriving insights to inform decision-making.
Beginner’s Primer on Business Intelligence
Business Intelligence is a set of process, architecture, technologies that converts the raw data into meaningful information which helps any business in making productive decision.
Business Intelligence directly affects the strategic and progressive decisions of any organization by making the use of historical data and facts. It also helps in analysing the data and explains the end users the nature of the business in the form of Reports, Graphs and Dashboards.
The session will cover the following topics:
-What is Business Intelligence
-Where does BI fit within an organization
-Advantages of BI systems
-Various types of BI users
-Various BI Tools
-Careers in BI
-Q & A
Follow Us For More Updates
Facebook Page https://www.facebook.com/CLTConsultin...
LinkedIn Page http://linkedin.com/company/cltcsi
Twitter Page https://twitter.com/CLTCSGLOBAL
YouTube Page https://www.youtube.com/channel/UCzqO...
Instagram Page https://www.instagram.com/clt_consult...
#businessintelligence #bi #bitool #oraclebi #otbi #oracle #cltconsulting
This document discusses an iconic San Diego tech company that focuses on online advertising. It introduces the founders Chern Lee and Valentino Vaschetto and their expertise in areas like search optimization, coding, and web development. The company's strategy involves leveraging marketing, data analysis, product development and other areas to better understand customers, increase awareness, and customize coding technologies for clients. By combining data-driven marketing strategies with analysis and new approaches, the company aims to provide personalized solutions and results for clients. Background is provided on founder Chern Lee's early career and success in online marketing and growing several companies specializing in areas like lead generation and ecommerce. The vision is to bring marketing and technology together in innovative ways while nurturing
Description
Organizations often struggle to successfully integrate analytics into their business. While this is always a challenging task, there are several approaches analytics teams can take to maximize their impact. This talk provides actionable recommendations to help analytics teams succeed and become essential to their company.
Here a few of the specific topics covered:
*How a small analytics team should prioritize projects to maximize impact. New analytics teams are often under-resourced and un-specialized. Focusing on a specific area makes it easier for the team to become experts and add value.
*Strategies to engage with and become essential to business stakeholders. Too often, analytics teams and the business stakeholders they serve work in completely separate workflows. Building strong relationships with business stakeholders is critical for an analytics team's success.
*Methods to increase the overall analytics literacy of your company. Evangelizing the benefits of analytics paves the way for future success.
*Building a network of analytics-minded people. Outside of the official analytics team, companies often have many other employees interested in learning about and leveraging this topic. Building a network of these people can be extremely beneficial.
Speaker
Jai Bansal, Aetna, Senior Data Scientist
Events-Based Research (EBR) is a method of collecting market research data from corporate events by surveying attendees. EBR allows companies to measure their return on investment from events, understand if they are reaching their target audience, and gain meaningful feedback from participants. Surveys are completed by attendees during events when they have time and no distractions, resulting in response rates usually above 80%. Data collected from multiple events through EBR can provide valuable insights through comparative analysis and help identify new trends, establish benchmarks, and unveil new opportunities.
The document discusses challenges with hiring data scientists and suggests alternative approaches. It recommends empowering small cross-functional data-oriented teams explicitly tasked with delivering measurable business benefits. This builds internal data capabilities rather than just hiring expertise. It also stresses the importance of making data science a cultural value throughout the organization so that all employees understand basic principles and practices of data science.
"Simplify Your Analytics Strategy" by Narendra MulaniSai Sandeep MN
Companies can get stuck trying to analyze all possible analytics opportunities instead of focusing on what matters most to customers, stakeholders, and employees. This document provides steps to simplify an analytics strategy and generate useful insights. It recommends accelerating data through a hybrid data platform and emerging technologies. A path to insight is unique for each organization and involves various elements like goals, data types and sources. Simplifying an analytics strategy can provide managerial insights through discovery-based or hypothesis-based approaches depending on whether the problem or solution is known.
The document provides recommendations for simplifying an analytics strategy in 3 key steps:
1) Accelerate data processing to enable fast insights and outcomes.
2) Delegate analytical work to technologies like business intelligence, data discovery, analytics applications, and machine learning to uncover patterns and insights.
3) Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem is known or unknown, with the goal of deriving insights to inform decision-making.
The document discusses how businesses can overcome complexity and confusion with analytics by pursuing a simpler path to insights. It recommends creating a hybrid data environment to accelerate data delivery and insights. Businesses should delegate analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. Each company's path to insights is unique, so they can take either a problem-focused or discovery-based approach depending on their needs. Uncovering opportunities requires making data-driven decisions once insights are found.
The document provides steps for companies to simplify their analytics strategy. It recommends companies accelerate data, use next-gen BI and data visualization, perform data discovery, leverage analytics applications, and adopt machine learning. Companies can take either a hypothesis-based approach for known problems or discovery-based for unknown solutions. The key is to focus on high-value problems and place action behind uncovered insights.
The document provides steps to simplify analytics strategies for businesses. It recommends accelerating data through hybrid technologies, using next-gen business intelligence and data visualization, conducting data discovery, leveraging analytics applications, and adopting machine learning. It also advises taking either a hypothesis-based approach for known problems or discovery-based approach for unknown solutions to generate insights and outcomes. The goal is to make analytics easier to use and deliver tangible business benefits.
This document provides tips for simplifying an analytics strategy. It recommends accelerating data by creating a hybrid data platform. It also suggests delegating work to analytics technologies like interactive BI tools. Additionally, it advises using data discovery techniques to uncover patterns and find opportunities. Industry-specific applications and machine learning can also simplify advanced analytics. Developing an data-driven culture and talent is important for ensuring an effective analytics strategy.
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
The document discusses how the Analytics Translator role can help organizations become more AI-driven by bridging the gap between business and technology. The Analytics Translator collects and prioritizes ideas, develops business cases for AI solutions, guides the solution development process, and drives adoption. Characteristics of a good Analytics Translator include understanding both business and AI, taking ownership, and operating at the intersection of UX, technology, and business. Developing this role is important for companies to successfully create impact and value from data and AI.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
The document discusses data mining and data warehousing. It describes data mining as a technique that enables companies to discover patterns and relationships in data with a high degree of accuracy. Typical tasks for data mining include predicting customer responses, identifying opportunities for cross-selling products, and detecting fraud. The document also discusses why companies build marketing data warehouses - to more efficiently and profitably serve customers by integrating customer data from various sources and analyzing purchase histories. Key considerations for ensuring success include having the right support team, quantifying benefits, and prioritizing deliverables in a phased approach.
The document discusses simplifying analytics strategies. It recommends that companies focus on analyzing what is important for customers, stakeholders, and employees rather than trying to analyze all possible data. It also suggests using next-gen business intelligence, data visualization, data discovery techniques, and machine learning to gain fast insights from data and uncover real business opportunities. The key is recognizing that each path to data-driven insights is unique and placing action behind the insights.
This document discusses creating a data-driven culture. It states that a data-driven organization acquires, processes, and leverages data in a timely manner to create efficiencies, iterate products, and navigate competition. It recommends focusing on process, people, platform, and products through metrics, iterative development, data access for staff, centralized data storage, and choosing tools for users. Initial tactics include talking to stakeholders, defining key metrics and entities, and finding easy wins to share results.
The document outlines the main components of a business intelligence system used by McDonald's. It discusses how McDonald's collects various data sources, including in-store traffic and customer interactions. This data is stored in data warehouses and used for business intelligence analysis. The business intelligence methodology includes data exploration through querying and reporting, data mining to extract knowledge, optimization models to determine the best solutions, and final decision making by choosing alternatives. McDonald's has become more data-driven to make decisions that save time and money.
This document discusses how analytics can provide competitive advantages for businesses. It explains that analytics involves translating customer, market, and company data into actions through a combination of knowledge, skills, and technology to support faster and better decision making. The document outlines several types of insights that can be gained, such as competitive insights, market insights, and business insights. It then provides examples of initial analytics projects that can be undertaken, including sales forecasting, price analysis, brand and reputation tracking, and gathering customer insights. Finally, it describes the services that AnalitiQs provides to help organizations successfully introduce and improve their analytics capabilities.
Competitive Intelligence is critical to any company striving to make their product deliver the value customers expect. As a Product Manager, you want to keep a holistic view of both your client’s opinion, desire and thoughts on new development, combined with a perspective on what your competition will be up to next. After all, the competition has smart Product Managers who may have discovered an important service or product angle those customers are going to want.
With social media in a mature phase, can’t you just rely on Twitter Feeds, news aggregators, and information from the odd LinkedIn Group? Is that enough? What else should you be doing to stay ahead of you competitors?
Join our guest speaker, Zena Applebaum, of Bennet Jones LLP, for a practical discussion about where to look for competitive intelligence (ethically), how to collect it, the questions you should be asking, who to ask, and how best to use the intel once you have it
The document discusses developing a content strategy through conducting research on target audiences. It emphasizes the importance of understanding consumer needs and pain points when creating buyer personas and blueprints to guide content creation. Both online and offline methods for research are outlined, including surveys, interviews, databases and events. The results should be analyzed and incorporated into the overarching content strategy, marketing plan and guide production of content that meets audience needs.
The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Society Consulting CEO Chad Richeson provides a synopsis of why analytics matters to customer experience, and how to make a greater impact on your business with a disciplined analytics process.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Businesses are challenged by complex analytics that can generate confusion instead of insights. Companies should simplify their analytics strategy by focusing on the most important data first to uncover real business opportunities and achieve desired outcomes. They can do this by creating a hybrid data environment that enables fast data delivery and real-time analytics, speeding up decision making. Delegating analytics work to technologies like next-gen business intelligence and data visualization can help turn data into useful insights without needing to analyze everything possible.
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.
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.
- - -
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/
The document discusses how businesses can overcome complexity and confusion with analytics by pursuing a simpler path to insights. It recommends creating a hybrid data environment to accelerate data delivery and insights. Businesses should delegate analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. Each company's path to insights is unique, so they can take either a problem-focused or discovery-based approach depending on their needs. Uncovering opportunities requires making data-driven decisions once insights are found.
The document provides steps for companies to simplify their analytics strategy. It recommends companies accelerate data, use next-gen BI and data visualization, perform data discovery, leverage analytics applications, and adopt machine learning. Companies can take either a hypothesis-based approach for known problems or discovery-based for unknown solutions. The key is to focus on high-value problems and place action behind uncovered insights.
The document provides steps to simplify analytics strategies for businesses. It recommends accelerating data through hybrid technologies, using next-gen business intelligence and data visualization, conducting data discovery, leveraging analytics applications, and adopting machine learning. It also advises taking either a hypothesis-based approach for known problems or discovery-based approach for unknown solutions to generate insights and outcomes. The goal is to make analytics easier to use and deliver tangible business benefits.
This document provides tips for simplifying an analytics strategy. It recommends accelerating data by creating a hybrid data platform. It also suggests delegating work to analytics technologies like interactive BI tools. Additionally, it advises using data discovery techniques to uncover patterns and find opportunities. Industry-specific applications and machine learning can also simplify advanced analytics. Developing an data-driven culture and talent is important for ensuring an effective analytics strategy.
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
The document discusses how the Analytics Translator role can help organizations become more AI-driven by bridging the gap between business and technology. The Analytics Translator collects and prioritizes ideas, develops business cases for AI solutions, guides the solution development process, and drives adoption. Characteristics of a good Analytics Translator include understanding both business and AI, taking ownership, and operating at the intersection of UX, technology, and business. Developing this role is important for companies to successfully create impact and value from data and AI.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
The document discusses data mining and data warehousing. It describes data mining as a technique that enables companies to discover patterns and relationships in data with a high degree of accuracy. Typical tasks for data mining include predicting customer responses, identifying opportunities for cross-selling products, and detecting fraud. The document also discusses why companies build marketing data warehouses - to more efficiently and profitably serve customers by integrating customer data from various sources and analyzing purchase histories. Key considerations for ensuring success include having the right support team, quantifying benefits, and prioritizing deliverables in a phased approach.
The document discusses simplifying analytics strategies. It recommends that companies focus on analyzing what is important for customers, stakeholders, and employees rather than trying to analyze all possible data. It also suggests using next-gen business intelligence, data visualization, data discovery techniques, and machine learning to gain fast insights from data and uncover real business opportunities. The key is recognizing that each path to data-driven insights is unique and placing action behind the insights.
This document discusses creating a data-driven culture. It states that a data-driven organization acquires, processes, and leverages data in a timely manner to create efficiencies, iterate products, and navigate competition. It recommends focusing on process, people, platform, and products through metrics, iterative development, data access for staff, centralized data storage, and choosing tools for users. Initial tactics include talking to stakeholders, defining key metrics and entities, and finding easy wins to share results.
The document outlines the main components of a business intelligence system used by McDonald's. It discusses how McDonald's collects various data sources, including in-store traffic and customer interactions. This data is stored in data warehouses and used for business intelligence analysis. The business intelligence methodology includes data exploration through querying and reporting, data mining to extract knowledge, optimization models to determine the best solutions, and final decision making by choosing alternatives. McDonald's has become more data-driven to make decisions that save time and money.
This document discusses how analytics can provide competitive advantages for businesses. It explains that analytics involves translating customer, market, and company data into actions through a combination of knowledge, skills, and technology to support faster and better decision making. The document outlines several types of insights that can be gained, such as competitive insights, market insights, and business insights. It then provides examples of initial analytics projects that can be undertaken, including sales forecasting, price analysis, brand and reputation tracking, and gathering customer insights. Finally, it describes the services that AnalitiQs provides to help organizations successfully introduce and improve their analytics capabilities.
Competitive Intelligence is critical to any company striving to make their product deliver the value customers expect. As a Product Manager, you want to keep a holistic view of both your client’s opinion, desire and thoughts on new development, combined with a perspective on what your competition will be up to next. After all, the competition has smart Product Managers who may have discovered an important service or product angle those customers are going to want.
With social media in a mature phase, can’t you just rely on Twitter Feeds, news aggregators, and information from the odd LinkedIn Group? Is that enough? What else should you be doing to stay ahead of you competitors?
Join our guest speaker, Zena Applebaum, of Bennet Jones LLP, for a practical discussion about where to look for competitive intelligence (ethically), how to collect it, the questions you should be asking, who to ask, and how best to use the intel once you have it
The document discusses developing a content strategy through conducting research on target audiences. It emphasizes the importance of understanding consumer needs and pain points when creating buyer personas and blueprints to guide content creation. Both online and offline methods for research are outlined, including surveys, interviews, databases and events. The results should be analyzed and incorporated into the overarching content strategy, marketing plan and guide production of content that meets audience needs.
The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
Companies should simplify their analytics strategies by focusing on discovering real business opportunities and outcomes for customers, stakeholders, and employees. They can do this by creating a hybrid data environment that enables fast data movement and using techniques like next-gen business intelligence, data discovery, analytics applications, and machine learning to delegate work to analytics technologies. The optimal path depends on a company's goals, culture, and existing technologies, but generally involves either testing known solutions or taking a discovery-based approach to find patterns for known problem areas. The highest value problems should be addressed first using the most appropriate approach.
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate.
Companies can get stuck trying to analyze all that’s possible and all that they could do through analytics, when they should be taking that next step of recognizing what’s important and what they should be doing — for their customers, stakeholders, and employees.
Discovering real business opportunities and achieving desired outcomes can be elusive.
Society Consulting CEO Chad Richeson provides a synopsis of why analytics matters to customer experience, and how to make a greater impact on your business with a disciplined analytics process.
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Businesses are challenged by complex analytics that can generate confusion instead of insights. Companies should simplify their analytics strategy by focusing on the most important data first to uncover real business opportunities and achieve desired outcomes. They can do this by creating a hybrid data environment that enables fast data delivery and real-time analytics, speeding up decision making. Delegating analytics work to technologies like next-gen business intelligence and data visualization can help turn data into useful insights without needing to analyze everything possible.
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.
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.
- - -
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/
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.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
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.
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Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
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4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
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2. • Companies can get stuck trying to
analyze all that’s possible and all that they could
do through analytics, when they should be
taking that next step of recognizing what’s
important and what they should be doing for
their customers, stakeholders, and
employees. Discovering real business
opportunities and achieving desired outcomes
can be elusive.
7. • The path to insight doesn’t come in one
single form.
• There are many different elements in
play, and they are always changing
business goals, technologies, data types
data sources, and then some are in a
state of flux.
• No matter what combination of culture
and technology exists for a business,
each path to analytics insight should be
individually paved with an outcome
driven mindset.
• Once insights are uncovered, the next
step is for the business, of course, to
make the data driven decisions that
place action behind the data.