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.
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.
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.
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.
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.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
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.
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.
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.
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.
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.
Business Analytics, "Second Edition teaches the fundamental concepts of the emerging field of business analytics and provides vital tools in understanding how data analysis works in today s organizations. Students will learn to apply basic business analytics principles, communicate with analytics professionals, and effectively use and interpret analytic models to make better business decisions. Included access to commercial grade analytics software gives students real-world experience and career-focused value. Author James Evans takes a balanced, holistic approach and looks at business analytics from descriptive, and predictive perspectives.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
The document summarizes insights from an article on simplifying analytics strategies. It discusses two main insights:
1) Steps to simplify analytics strategies including accelerating data through data platforms, next-gen business intelligence to visualize data, using data discovery techniques, analytics applications, and machine learning.
2) Two approaches to pave the path to analytics insight with an outcome-driven mindset: a hypothesis-based approach for known problems and a discovery-based approach for unknown solutions.
The document then discusses how these insights are relevant for managers in India, noting that some businesses are challenged by analytics complexity and it's important to focus on deriving insights from important data that add value for customers, stakeholders, and employees.
The document discusses how businesses can simplify their analytics strategy to generate insights that lead to real outcomes. It recommends that companies accelerate data through emerging technologies to speed up insight generation and business outcomes. Next-gen business intelligence can help companies improve decision making by presenting data in a visually appealing way to enable data-driven opportunities. The document also discusses how applications and machine learning can simplify advanced analytics to put power in the hands of business users to make data-driven decisions. It emphasizes that each company's path to analytics insight is unique and should have an outcome-driven mindset.
1. The document discusses simplifying a company's analytics strategy to enable insight-driven decisions that add value. It recommends accelerating data, using business intelligence and data visualization tools, data discovery, analytics applications, and machine learning.
2. It notes that the path to data-driven insights is unique for each dataset as business goals, technologies, data types, and sources are constantly changing. Companies need a new analytics approach each time to maximize benefits from data.
3. Two approaches are outlined: using existing solutions for known problems like customer segmentation; and discovery-based approaches to find predictive patterns for unknown problems. The goal is to uncover insights and then make data-driven decisions.
Business analytics uses tools like data science, artificial intelligence, and information technology to analyze data and add value to companies. Historically, businesses made decisions based on opinions rather than data analysis. Business analytics aims to increase decision making efficiency through data mining. It begins with understanding a business's context and goals. Technology is used to capture, record, and automate actions from analytical models. Data science determines the best mathematical models and machine learning algorithms to solve problems. Descriptive analytics describes what happened, predictive analytics predicts what could happen, and prescriptive analytics recommends actions.
This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
This document discusses how companies can make advanced analytics work for them. It recommends that companies (1) choose the right data by focusing on specific business problems and considering new sources, (2) build models that predict and optimize business outcomes through hypothesis-led modeling and simple models, and (3) transform capabilities by developing business-relevant analytics, embedding analytics in tools, and building skills. The document also notes challenges like modeling errors, behavioral inertia, and weak analytical skills, and advises that companies act now on targeted efforts rather than overhauls.
The document discusses simplifying analytics strategies for businesses. It recommends that companies pursue a simpler path to gaining insights from data by accelerating data processing, delegating analytic work to technologies like BI and machine learning, and recognizing that different problems require different analytic approaches. Recognizing insights and making data-driven decisions can provide competitive advantages for companies.
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Recently, Oracle and Accenture polled some 200 CFOs and senior finance executives about
their strategies for improving the management reporting process. More than a third—41%— said selecting the right analytics tools and technologies was their top concern.
Optimizely building your_data_dna_e_booktthhciciedeng
This document provides guidance on how to build a company's data DNA by establishing key metrics, gathering both quantitative and qualitative data, and using that information to optimize business performance through experimentation and A/B testing. It emphasizes the importance of identifying a single "guiding light" metric that defines business goals and can be used to prioritize optimization efforts. The document also outlines how to map customer journeys and core conversion funnels in order to determine high-value areas of a website or product to test. It recommends using qualitative user research to identify major roadblocks or weaknesses before developing hypotheses for A/B tests aimed at improving conversion rates and the guiding metric.
Poster presetation for "Using Big Data for Marketing Analytics"Touseef Ahmed
The document discusses how marketing analytics uses procedures and technologies to assess the success of marketing initiatives by estimating performance metrics. It aims to determine how marketing analytics benefits from big data and its implications for Ireland's retail sector. Specifically, it investigates how big data can help existing market analytic processes and how the Irish retail market can benefit from integrating big data-driven market analytics.
The document discusses big data analytics and provides tips for organizations looking to implement big data initiatives. It notes that while organizations have large amounts of customer, sales, and other operational data, most are not effectively analyzing and extracting insights from this data. The value is in using analytics to uncover hidden patterns and correlations to help businesses make better decisions. However, most companies currently take a slow, manual approach to data compilation and analysis. The document recommends that organizations consider big data as a business solution rather than just an IT problem. It suggests taking a journey approach, focusing on insights over data, using proven analytics tools, and delivering early business value from big data projects in order to justify further investment.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid data platform and emerging technologies. This allows for fast data, insights, and outcomes. Examples show how next-gen BI and data visualization, data discovery, and machine learning can delegate work to analytics technologies to more easily uncover patterns and opportunities. The document emphasizes that each path to insights is unique and may involve hypothesis-based or discovery-based approaches.
Business analytics can help organizations make better decisions by applying analytical techniques to business problems. While many organizations collect large amounts of data, few systematically analyze this data to improve decision making. Common approaches used by organizations to enhance decisions include analytics, testing hypotheses with data, and improving data quality. Business analytics frameworks provide tools to leverage more information for strategic and operational decisions.
Carlos Navarro discusses improving analytical platforms to empower employees and better serve customers. He states that big data needs to be analyzed and presented in meaningful and actionable ways. Empowering employees requires generating and distributing actionable insights from data. Visual analytics can turn data into insights in an interactive, graphical manner. The presentation provides examples of visualizing customer satisfaction data to generate insights such as satisfaction trends, positive and negative feedback, and underperforming teams.
This document discusses how machine learning can help companies move beyond basic targeting and accelerate sales. It provides examples of how machine learning has helped companies improve product propensity modeling to identify prospects likely to need their products, and response propensity modeling to ensure marketing dollars are spent on prospects most likely to respond. The document also outlines considerations for applying machine learning, such as the need for quality input data and human intervention to determine the right algorithms and data sources.
The document summarizes two key insights from an article on simplifying analytics strategies:
1. There are methods to simplify complex analytics strategies, such as accelerating data processing, delegating work to analytics technologies like business intelligence and data visualization, data discovery, analytics applications, and machine learning.
2. Companies can take either a hypothesis-based or discovery-based approach to solve business problems depending on how much is known about the problem and potential solutions. A hypothesis-based approach is best for known problems with known solutions, while discovery-based is best for known problems with unknown solutions.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid technology environment. This allows for fast delivery of analytics to improve service quality. It also recommends delegating work to analytics technologies like business intelligence and data visualization to more easily uncover patterns. Different analytic techniques like data discovery, applications, and machine learning can further help companies gain insights from their data in a simplified manner. The path to insights is unique for each company based on their goals, data, and technologies.
How to Leverage the Power of Data Analytics in Sales?Shaily Shah
Data is the DNA behind the robust analytics and insights supporting modern organizations to recognize new products, determine how to serve customers better, and enhance operational efficiencies.
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.
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.
The document summarizes insights from an article on simplifying analytics strategies. It discusses two main insights:
1) Steps to simplify analytics strategies including accelerating data through data platforms, next-gen business intelligence to visualize data, using data discovery techniques, analytics applications, and machine learning.
2) Two approaches to pave the path to analytics insight with an outcome-driven mindset: a hypothesis-based approach for known problems and a discovery-based approach for unknown solutions.
The document then discusses how these insights are relevant for managers in India, noting that some businesses are challenged by analytics complexity and it's important to focus on deriving insights from important data that add value for customers, stakeholders, and employees.
The document discusses how businesses can simplify their analytics strategy to generate insights that lead to real outcomes. It recommends that companies accelerate data through emerging technologies to speed up insight generation and business outcomes. Next-gen business intelligence can help companies improve decision making by presenting data in a visually appealing way to enable data-driven opportunities. The document also discusses how applications and machine learning can simplify advanced analytics to put power in the hands of business users to make data-driven decisions. It emphasizes that each company's path to analytics insight is unique and should have an outcome-driven mindset.
1. The document discusses simplifying a company's analytics strategy to enable insight-driven decisions that add value. It recommends accelerating data, using business intelligence and data visualization tools, data discovery, analytics applications, and machine learning.
2. It notes that the path to data-driven insights is unique for each dataset as business goals, technologies, data types, and sources are constantly changing. Companies need a new analytics approach each time to maximize benefits from data.
3. Two approaches are outlined: using existing solutions for known problems like customer segmentation; and discovery-based approaches to find predictive patterns for unknown problems. The goal is to uncover insights and then make data-driven decisions.
Business analytics uses tools like data science, artificial intelligence, and information technology to analyze data and add value to companies. Historically, businesses made decisions based on opinions rather than data analysis. Business analytics aims to increase decision making efficiency through data mining. It begins with understanding a business's context and goals. Technology is used to capture, record, and automate actions from analytical models. Data science determines the best mathematical models and machine learning algorithms to solve problems. Descriptive analytics describes what happened, predictive analytics predicts what could happen, and prescriptive analytics recommends actions.
This document describes a data analytics internship under Prof. Sameer Mathur at IIM Lucknow. The internship involves developing an analytics strategy to drive healthcare transformation. Key responsibilities include analyzing articles to identify important insights and explaining their relevance to managers in India. Two important insights from the article are discussed. The first is ways to simplify an analytics strategy, such as accelerating data and delegating work to technologies like business intelligence and machine learning. The second insight describes two approaches - hypothesis-based and discovery-based - for solving business problems depending on the nature and known solutions. The insights are relevant to managers in India as they can help uncover patterns, rely on business intelligence, and use analytics to handle inventories.
This document discusses how companies can make advanced analytics work for them. It recommends that companies (1) choose the right data by focusing on specific business problems and considering new sources, (2) build models that predict and optimize business outcomes through hypothesis-led modeling and simple models, and (3) transform capabilities by developing business-relevant analytics, embedding analytics in tools, and building skills. The document also notes challenges like modeling errors, behavioral inertia, and weak analytical skills, and advises that companies act now on targeted efforts rather than overhauls.
The document discusses simplifying analytics strategies for businesses. It recommends that companies pursue a simpler path to gaining insights from data by accelerating data processing, delegating analytic work to technologies like BI and machine learning, and recognizing that different problems require different analytic approaches. Recognizing insights and making data-driven decisions can provide competitive advantages for companies.
The document discusses several key challenges in adopting predictive analytics in healthcare:
1) Lack of quality data due to incomplete, inconsistent, or non-standardized data from different sources.
2) Difficulty incorporating analytics into clinical workflows and ensuring usability for clinicians.
3) Privacy concerns around sharing and integrating patient data from different organizations.
4) Need for interdisciplinary teams including data scientists, clinicians, and other stakeholders to design effective predictive solutions.
Recently, Oracle and Accenture polled some 200 CFOs and senior finance executives about
their strategies for improving the management reporting process. More than a third—41%— said selecting the right analytics tools and technologies was their top concern.
Optimizely building your_data_dna_e_booktthhciciedeng
This document provides guidance on how to build a company's data DNA by establishing key metrics, gathering both quantitative and qualitative data, and using that information to optimize business performance through experimentation and A/B testing. It emphasizes the importance of identifying a single "guiding light" metric that defines business goals and can be used to prioritize optimization efforts. The document also outlines how to map customer journeys and core conversion funnels in order to determine high-value areas of a website or product to test. It recommends using qualitative user research to identify major roadblocks or weaknesses before developing hypotheses for A/B tests aimed at improving conversion rates and the guiding metric.
Poster presetation for "Using Big Data for Marketing Analytics"Touseef Ahmed
The document discusses how marketing analytics uses procedures and technologies to assess the success of marketing initiatives by estimating performance metrics. It aims to determine how marketing analytics benefits from big data and its implications for Ireland's retail sector. Specifically, it investigates how big data can help existing market analytic processes and how the Irish retail market can benefit from integrating big data-driven market analytics.
The document discusses big data analytics and provides tips for organizations looking to implement big data initiatives. It notes that while organizations have large amounts of customer, sales, and other operational data, most are not effectively analyzing and extracting insights from this data. The value is in using analytics to uncover hidden patterns and correlations to help businesses make better decisions. However, most companies currently take a slow, manual approach to data compilation and analysis. The document recommends that organizations consider big data as a business solution rather than just an IT problem. It suggests taking a journey approach, focusing on insights over data, using proven analytics tools, and delivering early business value from big data projects in order to justify further investment.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid data platform and emerging technologies. This allows for fast data, insights, and outcomes. Examples show how next-gen BI and data visualization, data discovery, and machine learning can delegate work to analytics technologies to more easily uncover patterns and opportunities. The document emphasizes that each path to insights is unique and may involve hypothesis-based or discovery-based approaches.
Business analytics can help organizations make better decisions by applying analytical techniques to business problems. While many organizations collect large amounts of data, few systematically analyze this data to improve decision making. Common approaches used by organizations to enhance decisions include analytics, testing hypotheses with data, and improving data quality. Business analytics frameworks provide tools to leverage more information for strategic and operational decisions.
Carlos Navarro discusses improving analytical platforms to empower employees and better serve customers. He states that big data needs to be analyzed and presented in meaningful and actionable ways. Empowering employees requires generating and distributing actionable insights from data. Visual analytics can turn data into insights in an interactive, graphical manner. The presentation provides examples of visualizing customer satisfaction data to generate insights such as satisfaction trends, positive and negative feedback, and underperforming teams.
This document discusses how machine learning can help companies move beyond basic targeting and accelerate sales. It provides examples of how machine learning has helped companies improve product propensity modeling to identify prospects likely to need their products, and response propensity modeling to ensure marketing dollars are spent on prospects most likely to respond. The document also outlines considerations for applying machine learning, such as the need for quality input data and human intervention to determine the right algorithms and data sources.
The document summarizes two key insights from an article on simplifying analytics strategies:
1. There are methods to simplify complex analytics strategies, such as accelerating data processing, delegating work to analytics technologies like business intelligence and data visualization, data discovery, analytics applications, and machine learning.
2. Companies can take either a hypothesis-based or discovery-based approach to solve business problems depending on how much is known about the problem and potential solutions. A hypothesis-based approach is best for known problems with known solutions, while discovery-based is best for known problems with unknown solutions.
This document discusses simplifying analytics strategies. It recommends pursuing a simpler path to insights by accelerating data through a hybrid technology environment. This allows for fast delivery of analytics to improve service quality. It also recommends delegating work to analytics technologies like business intelligence and data visualization to more easily uncover patterns. Different analytic techniques like data discovery, applications, and machine learning can further help companies gain insights from their data in a simplified manner. The path to insights is unique for each company based on their goals, data, and technologies.
How to Leverage the Power of Data Analytics in Sales?Shaily Shah
Data is the DNA behind the robust analytics and insights supporting modern organizations to recognize new products, determine how to serve customers better, and enhance operational efficiencies.
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.
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.
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.
The document provides tips for simplifying an analytics strategy. It recommends accelerating data processing to enable fast insights and outcomes. Companies should delegate analytics 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 an outcome-driven approach for known problems or a discovery-based approach to find patterns for unknown solutions. The ultimate goal is to uncover insights from data and make data-driven decisions.
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.
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 presentation is based on the article Simplify Your Analytics Strategy by Narendra Mulani.I have made this presentation
as a part of my data internship course
This document discusses how companies can simplify their analytics strategies. It recommends that companies accelerate data access to gain insights more quickly, delegate analytics work to technologies, and use next-gen business intelligence and data visualization to present data insights visually. The document also suggests using applications, machine learning, and data discovery techniques to simplify advanced analytics and uncover new opportunities from data. The overall message is that companies can gain data-driven insights more easily by focusing on outcomes, leveraging technologies, and having an adaptive analytics approach.
"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.
This document discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, complexity and confusion can make realizing benefits difficult. To overcome this, companies should pursue a simpler path to gain insights from their data in order to make decisions that add value. This involves accelerating data access, data discovery, analytics applications, and machine learning. Companies can take either a hypothesis-based or discovery-based approach depending on whether the business problem and solution are known or unknown.
Companies should pursue a simpler path to uncover insights from their data by creating a data supply chain using a hybrid of technologies. They should focus on next-gen business intelligence and data visualization to improve decision making, as well as data discovery techniques to uncover patterns. Machine learning can also be used to produce predictions and remove human elements from modeling. A company's analytics journey also depends on its culture.
Lecture 1.13 & 1.14 &1.15_Business Profiles in Big Data.pptxRATISHKUMAR32
The presentation contain the business profiles in big data analytics. through this ppt user can learn about the different case studies such as facebook and walmart. This ppt contain the information and seven characteristics that are required to learn the basics of big data.
Simplify Your Analytics Strategy" by Narendra MulaniMohitGupta714
This document discusses simplifying analytics strategies. It recommends that companies focus on important analytics that benefit customers, stakeholders, and employees rather than trying to analyze all possible data. It suggests accelerating data by creating a hybrid data environment for faster movement and consumption of increasing data. Analytical technologies like next-gen business intelligence, data discovery, machine learning, and analytics applications can help delegate work and simplify advanced analytics for business users. Companies can take known problem/known solution or known problem/unknown solution approaches depending on the business problem.
The document discusses how companies can make advanced analytics work for them. It provides several guides for managers, including identifying the right data sources, building simple analytics models focused on business goals, and developing tools everyone can understand. While acquiring big data is important, companies must transform their culture and capabilities to develop business-relevant analytics that can optimize outcomes. Executing analytics properly requires a flexible approach and cultural shift within the organization.
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
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
2) Legacy systems and complex analytics platforms also impede effective data use. Modular solutions that integrate with existing systems and empower self-service are recommended.
3) The document promotes the Statistica software as addressing these challenges through its ease of use, integration capabilities, and support for big data analytics.
202406 - Cape Town Snowflake User Group - LLM & RAG.pdfDouglas Day
Content from the July 2024 Cape Town Snowflake User Group focusing on Large Language Model (LLM) functions in Snowflake Cortex. Topics include:
Prompt Engineering.
Vector Data Types and Vector Functions.
Implementing a Retrieval
Augmented Generation (RAG) Solution within Snowflake
Dive into the details of how to leverage these advanced features without leaving the Snowflake environment.
Optimizing Feldera: Integrating Advanced UDFs and Enhanced SQL Functionality ...mparmparousiskostas
This report explores our contributions to the Feldera Continuous Analytics Platform, aimed at enhancing its real-time data processing capabilities. Our primary advancements include the integration of advanced User-Defined Functions (UDFs) and the enhancement of SQL functionality. Specifically, we introduced Rust-based UDFs for high-performance data transformations and extended SQL to support inline table queries and aggregate functions within INSERT INTO statements. These developments significantly improve Feldera’s ability to handle complex data manipulations and transformations, making it a more versatile and powerful tool for real-time analytics. Through these enhancements, Feldera is now better equipped to support sophisticated continuous data processing needs, enabling users to execute complex analytics with greater efficiency and flexibility.
Difference in Differences - Does Strict Speed Limit Restrictions Reduce Road ...ThinkInnovation
Objective
To identify the impact of speed limit restrictions in different constituencies over the years with the help of DID technique to conclude whether having strict speed limit restrictions can help to reduce the increasing number of road accidents on weekends.
Context*
Generally, on weekends people tend to spend time with their family and friends and go for outings, parties, shopping, etc. which results in an increased number of vehicles and crowds on the roads.
Over the years a rapid increase in road casualties was observed on weekends by the Government.
In the year 2005, the Government wanted to identify the impact of road safety laws, especially the speed limit restrictions in different states with the help of government records for the past 10 years (1995-2004), the objective was to introduce/revive road safety laws accordingly for all the states to reduce the increasing number of road casualties on weekends
* The Speed limit restriction can be observed before 2000 year as well, but the strict speed limit restriction rule was implemented from 2000 year to understand the impact
Strategies
Observe the Difference in Differences between ‘year’ >= 2000 & ‘year’ <2000
Observe the outcome from multiple linear regression by considering all the independent variables & the interaction term
Interview Methods - Marital and Family Therapy and Counselling - Psychology S...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
3. 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.
4. 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
5. To overcome this, companies should
pursue a simpler path to uncovering the
insight in their data and making insight-
driven decisions that add value.
6. Following are steps that we have seen
work in a number of companies to
simplify their analytics strategy and
generate insight that leads to real
outcomes:
7. Accelerate the data
• Fast data = fast insight = fast outcomes.
• Liberate and accelerate data by creating a
data supply chain built on a hybrid
technology environment — a data service
platform combined with emerging big data
technologies.
8.
9. Next-Gen Business Intelligence (BI)
and data visualization.
•At its core, next-gen business
intelligence is bringing data and
analytics to life to help companies
improve and optimize their decision-
making and organizational
performance.
10. Data discovery
•Data discovery can take place
alongside outcome-specific data
projects. Through the use of data
discovery techniques, companies can
test and play with their data to
uncover data patterns that aren’t
clearly evident.
11.
12. Analytics applications
•Applications can simplify
advanced analytics as
they put the power of
analytics easily and
elegantly into the hands
of the business user to
make data-driven
business decisions.
13.
14. Machine learning and cognitive
computing
•Machine learning is an evolution of
analytics that removes much of the
human element from the data
modeling process to produce
predictions of customer behavior and
enterprise performance.
15. •Recognize that each path to data
insight is unique. 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.
16. • Another main component of a company’s
analytics journey depends on the company’s
culture itself.
• 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.
• To do this, companies can take two approaches
depending on the nature of the business
problem.
17. • First, for a known problem with a known
solution, such as customer segmentation and
propensity modeling for targeted marketing
campaigns, the company could take a
hypothesis-based approach by starting with
the outcome (e.g. cross-sell/up-sell to
existing customers), pilot and test the solution
with a control group and then scale broadly
across the customer base.
18. • Second, for a known problem area, but with an
unknown solution, the company could take a
discovery-based approach to look for patterns in
the data to find interesting correlations that may
be predictive—for instance, a bank found that the
speed at which fields were filled out on its online
forms was highly correlated with fraudulent
behavior.
19. •When determining which
problem to address,
companies should first
focus on the one that can
offer the highest value,
then it can choose a
hypothesis-based or
discovery-based
approach based on the
degree of institutional
knowledge it has to solve
that kind of problem.
20. • 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. It is possible to uncover
the business opportunities in your data and
increase data equity, simply.
21. RECAP
• Companies get stuck trying to analyze all that’s
possible.
• Following are steps that we have worked in a number
of companies to simplify their analytics strategy:
1. Accelerate the data
2. Next-Gen Business Intelligence (BI) and data
visualization.
3. Data discovery
4. Analytics applications
5. Machine learning and cognitive computing
• Companies can take two approaches
1. For a known problem with a known solution
2. For a known problem area, but with an unknown
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