The document discusses using IBM Watson Analytics to predict the results of the 2016 March Madness college basketball tournament. It introduces Watson Analytics and describes how to prepare basketball tournament data by combining 15 seasons into a single data set. Predictive analysis is conducted on the data to identify the top predictors of teams that reach the Final Four and championship game. A comparison is shown of how accurately previous seasons were predicted. The analysis predicts that North Carolina will be the 2016 tournament winner.
Watson Analytics - Специалист по обработке данных "в коробке"Irina Podlevskikh
Продукт IBM Watson Analytics предоставляет мощные возможности анализа, доступные практически каждому. Автоматическая подготовка данных, прогнозная аналитика, отчетность, информационные панели, визуализация и функции совместной работы позволят вам полностью взять анализ данных под контроль. Далее вы сможете самостоятельно предпринять необходимые действия, чтобы решить проблему или воспользоваться возможностью, без помощи ИТ-специалистов и специалистов по обработке данных.
Social media represents the pulse of the planet, it can shape our ideas and identify new products and markets, help us identify opportunities for our businesses. The trick is how to tap into that channel. IBM Watson Analytics for Social Media is a cloud-based smart data discovery service which puts advanced analytics, without complexity, right at your fingertips! See how Highlands and Islands can get answers and new insights to inform business decisions.
Introduction to Predictive Analytics with IBM SPSS. Predictive analytics helps organizations use their data to make better decisions by allowing them to draw reliable, data-driven conclusions about current conditions and future events.
Predictive analytics encompasses a variety of techniques such as Statistics, Game theory and Data mining to do this analysis,
and make these predictions.
So by deploying predictive analytics, organizations are addressing their business issues proactively to get the best outcomes.
Demystifying IBM Watson: Uncover the Power of Cognitive SolutionsPerficient, Inc.
Successful organizations recognize that information is a strategic asset, capable of strengthening decision making, improving efficiency, reducing risk, and enhancing customer relationships. With the tremendous surge in the volume and diversity of data, leveraging this information across the entire enterprise is a business imperative that cannot be ignored.
IBM Watson harnesses the power of cognitive exploration, machine learning, and natural language processing to answer your most pressing questions, strengthen decision making, scale expertise, uncover key information in unstructured data, and reveal previously undiscovered data patterns and relationships.
In this SlideShare, we discuss:
Trends in cognitive solutions
Use cases for IBM Watson
Real-world Watson success stories
Getting started on the path to cognitive solutions
Watson Analytics - Специалист по обработке данных "в коробке"Irina Podlevskikh
Продукт IBM Watson Analytics предоставляет мощные возможности анализа, доступные практически каждому. Автоматическая подготовка данных, прогнозная аналитика, отчетность, информационные панели, визуализация и функции совместной работы позволят вам полностью взять анализ данных под контроль. Далее вы сможете самостоятельно предпринять необходимые действия, чтобы решить проблему или воспользоваться возможностью, без помощи ИТ-специалистов и специалистов по обработке данных.
Social media represents the pulse of the planet, it can shape our ideas and identify new products and markets, help us identify opportunities for our businesses. The trick is how to tap into that channel. IBM Watson Analytics for Social Media is a cloud-based smart data discovery service which puts advanced analytics, without complexity, right at your fingertips! See how Highlands and Islands can get answers and new insights to inform business decisions.
Introduction to Predictive Analytics with IBM SPSS. Predictive analytics helps organizations use their data to make better decisions by allowing them to draw reliable, data-driven conclusions about current conditions and future events.
Predictive analytics encompasses a variety of techniques such as Statistics, Game theory and Data mining to do this analysis,
and make these predictions.
So by deploying predictive analytics, organizations are addressing their business issues proactively to get the best outcomes.
Demystifying IBM Watson: Uncover the Power of Cognitive SolutionsPerficient, Inc.
Successful organizations recognize that information is a strategic asset, capable of strengthening decision making, improving efficiency, reducing risk, and enhancing customer relationships. With the tremendous surge in the volume and diversity of data, leveraging this information across the entire enterprise is a business imperative that cannot be ignored.
IBM Watson harnesses the power of cognitive exploration, machine learning, and natural language processing to answer your most pressing questions, strengthen decision making, scale expertise, uncover key information in unstructured data, and reveal previously undiscovered data patterns and relationships.
In this SlideShare, we discuss:
Trends in cognitive solutions
Use cases for IBM Watson
Real-world Watson success stories
Getting started on the path to cognitive solutions
Lightning talk :IBM Content Analytics with Enterprise Search - Wolfgang Junglucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
See and hear how IBM applies Lucence into their commercial software offerings. Hear about experience in development and advantages of this approach.
Explore, analyze and interpret information for better business outcomes
IBM Watson Explorer is a cognitive exploration solution that combines search and content analytics with unique cognitive
computing capabilities to help users find and understand the information they need to work more efficiently and make better, more confident decisions.
To learn more about IBM Watson Explorer visit ibm.biz/watsonexplorer.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. Organizations can then use these insights to strengthen customer relationships, improve service delivery and drive new opportunities. To help guide the processes and activities within a given domain, data scientists and engineers need a foundational methodology that provides a framework for how to proceed with whichever methods or tools they will use to obtain answers and deliver results. In this presentation, we will share data science tips for data engineers.
Join the Data Science Experience: http://ibm.co/data-science
IBM Watson Analytics sets powerful analytics capabilities free so practically anyone can use them. Automated data preparation, predictive analytics, reporting, dashboards, visualization and collaboration capabilities, enable you to take control of your own analysis. You can then take the appropriate action to address a problem or seize an opportunity, all without asking IT or a data expert for help.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Big Data and Analytics: The IBM PerspectiveThe_IPA
Gareth Mitchell-Jones, Associate Partner Big Data & Analytics at IBM, shares his thoughts on the hot topic of Big Data from his unique perspective at an IPA 44 Club event in London. To learn more about The IPA visit www.ipa.co.uk and The 44 Club here http://www.ipa.co.uk/groups/44-club-2
Beyond Keyword Search with IBM Watson Explorer Webinar DeckMC+A
IBM Watson Explorer provides flexible and powerful cognitive search and content analytics that can support a large variety of business use cases. In this Webinar, we discuss moving beyond keyword and federated search provided by products like the Google Search Appliance and getting ready for what’s next.
The Big Picture: Real-time Data is Defining Intelligent OffersCloudera, Inc.
New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Marketers already know this, Ninety-six percent (96%) of organizations believe that email personalization can improve email marketing performance. But where do we get this increasingly personal direction? The answer is likely in your customer data. In order to understand your customer needs contextualized in the moment they feel the need to act you will require a platform that can leverage real-time data. Apache Kudu is a Cloudera component that makes dealing with quickly changing data fast and easy. Companies are leveraging next generation data stores like Kudu to build data applications that deliver smart promotions, real-time offers, and personalized marketing. Join us as we discuss modern approaches to real-time application development and highlight key Cloudera use cases being powered by Cloudera’s operational database.
IBM Watson Jeopardy! white paper which explains Watson’s workload optimised system design based on IBM DeepQA architecture and POWER7® processor-based servers
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Lightning talk :IBM Content Analytics with Enterprise Search - Wolfgang Junglucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
See and hear how IBM applies Lucence into their commercial software offerings. Hear about experience in development and advantages of this approach.
Explore, analyze and interpret information for better business outcomes
IBM Watson Explorer is a cognitive exploration solution that combines search and content analytics with unique cognitive
computing capabilities to help users find and understand the information they need to work more efficiently and make better, more confident decisions.
To learn more about IBM Watson Explorer visit ibm.biz/watsonexplorer.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. Organizations can then use these insights to strengthen customer relationships, improve service delivery and drive new opportunities. To help guide the processes and activities within a given domain, data scientists and engineers need a foundational methodology that provides a framework for how to proceed with whichever methods or tools they will use to obtain answers and deliver results. In this presentation, we will share data science tips for data engineers.
Join the Data Science Experience: http://ibm.co/data-science
IBM Watson Analytics sets powerful analytics capabilities free so practically anyone can use them. Automated data preparation, predictive analytics, reporting, dashboards, visualization and collaboration capabilities, enable you to take control of your own analysis. You can then take the appropriate action to address a problem or seize an opportunity, all without asking IT or a data expert for help.
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
Big Data and Analytics: The IBM PerspectiveThe_IPA
Gareth Mitchell-Jones, Associate Partner Big Data & Analytics at IBM, shares his thoughts on the hot topic of Big Data from his unique perspective at an IPA 44 Club event in London. To learn more about The IPA visit www.ipa.co.uk and The 44 Club here http://www.ipa.co.uk/groups/44-club-2
Beyond Keyword Search with IBM Watson Explorer Webinar DeckMC+A
IBM Watson Explorer provides flexible and powerful cognitive search and content analytics that can support a large variety of business use cases. In this Webinar, we discuss moving beyond keyword and federated search provided by products like the Google Search Appliance and getting ready for what’s next.
The Big Picture: Real-time Data is Defining Intelligent OffersCloudera, Inc.
New research shows that 57% of the buying cycle is completed before a prospect even speaks to a company. Marketers already know this, Ninety-six percent (96%) of organizations believe that email personalization can improve email marketing performance. But where do we get this increasingly personal direction? The answer is likely in your customer data. In order to understand your customer needs contextualized in the moment they feel the need to act you will require a platform that can leverage real-time data. Apache Kudu is a Cloudera component that makes dealing with quickly changing data fast and easy. Companies are leveraging next generation data stores like Kudu to build data applications that deliver smart promotions, real-time offers, and personalized marketing. Join us as we discuss modern approaches to real-time application development and highlight key Cloudera use cases being powered by Cloudera’s operational database.
IBM Watson Jeopardy! white paper which explains Watson’s workload optimised system design based on IBM DeepQA architecture and POWER7® processor-based servers
No fewer than 80% have digital transformation at the centre of their corporate strategy with the aim of improving efficiency, driving innovation and becoming more agile. Though it's clear that insight into the data they hold is going to help them get there, many organisations find themselves at a crossroads. Big data, machine learning, data science: these are all initiatives every company knows they should take on in order to evolve their business, yet few know how to tackle the projects for successful outcomes.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
IBM Cognos Social Media Analytic Solution - G A InfoMartGA InfoMart Ltd
IBM Cognos Social Media Analytic Solution helps you to analyse the voice of your customer on any user generated content like blog, forum, Facebook Page...etc, so you could easy identify:
1. Who the key influencer - some review/blog writer got 3000 page view in a day, can you leverage him?
2. What's the evolving topic - what's mostly mentioned topic while the user discussing your product/services?
3. what's the best time and best channel to release news?
Check more details in the slide and you will know how to unveil the hidden gems!
The Ultimate Guide to using Social Media Media AnalyticsSocialmetrix
How to get insights from quantitative data to improve your
social media performance.
-How do you measure social media?
-How to use quantitative data to improve your Audience.
-How to use your social analytics to create a Content MKT Strategy for social media.
-How to use quantitative data to improve your engagement.
-How to get valuable insights from your Competitors Analytics.
-How to get valuable insights from your Campaign Analytics.
Knowing what data matters, and what doesn't, is critical to creating your own social media metrics tracking system. This presentation reviews the basics of Google Analytics, Facebook Insights, and YouTube Insights, and the data you need to track in order to know what your online community wants, develop engaging content, support the community, and meet your goals. The presentation also includes references to several DIY social media metrics dashboards you can use in your business.
Libraries use social media channels to connect with customers, to answer questions, and to just "be there" for their community. Do you know if your social media channel is successful, and you are meeting your library's goals? Most social media channels have analytics or insights that help figure this out. King explores analytics for different social media channels and explains what you should track and why. The second presentation looks at what it takes to create a strong and sustainable social media presence, including successes and bummers. Speakers discuss staff management and participation, community participation, and moving beyond event promotion.
IBM Watson Ecosystem roadshow - Chicago 4-2-14cheribergeron
IBM Watson is powering a new generation of cognitive applications. Learn how IBM is partnering with visionaries and entrepreneurs to bring innovative cognitive applications to market through the IBM Watson Ecosystem.
World of Watson 2016: Journey to Cognitive Excellence - Harness the Force of ...Julie Severance
Becoming a cognitive business is a journey, not a destination. A cognitive analytics culture is not something you can just buy or install. Although the right technology is crucial, its true value arises when the organizational mindset changes. Many organizations have learned to embrace analytics, but embracing cognitive is another step entirely, and it’s one that may be even more challenging. However, the possibilities are endless and the potential rewards make it worthwhile.
How difficult should it be to use analytics? IBM thinks it should be as easy as using an app, so they have introduced IBM Watson Analytics. Learn more in this presentation.
Who wouldn’t prefer to wear a custom-tailored suit over something bought off the rack? Especially if it can be had for the same price, or even cheaper? In much the same way, we find that companies have a taste for supply chain analytics that are carefully tailored to their own business, quirks and all. In this talk we will discuss supply chain analytics broadly, provide some examples, and then address conditions when a custom approach to creating a supply chain decision support tool makes good sense.
Content marketing analytics: what you should really be doingDaniel Smulevich
My presentation from Digital Marketing Show 2014. #DMSLDN
A journey through web analytics processes, from setting up KPIs to integrating data sources and automating reports.
It's all about conversion. Every e-commerce business that cares about improving revenue has a narrow focus of optimizing their website to improve customer experience.
However, most companies still lack the ability to create realistic website performance tests due to limitations in their current test methods.
In this webinar you'll learn:
1) How to tie business metrics (ROI) with website performance metrics and real user data
2) How to build performance tests that will model user behavior on your site
3) How to correlate data analytics so you can troubleshoot bottlenecks to improve performance
#1NWebinar: Digital Blindspots - A Q&A on Common Marketing Analytics HurdlesOne North
Although we have all kinds of technology at our fingertips, marketers continue to struggle to quantify and report on the effectiveness of their activities. In this Q&A-style #1NWebinar, Senior Data Strategist Ben Magnuson sat down with One North’s Marketing Coordinator Olivia Koivisto to discuss common data analytics and reporting questions from B2B and professional services marketers. During the session, Ben explained what to look for in analytics tools, how to identify which data points matter, the importance of goal-setting, and more.
Watch the recording: https://youtu.be/RsQZxFLfYnI
It's all about conversion. Every e-commerce business that cares about improving revenue has a narrow focus of optimizing their website to improve customer experience.
However, most companies still lack the ability to create realistic website performance tests due to limitations in their current test methods.
In this webinar you'll learn:
1) How to tie business metrics (ROI) with website performance metrics and real user data
2) How to build performance tests that will model user behavior on your site
3) How to correlate data analytics so you can troubleshoot bottlenecks to improve performance
Building with Watson - Social Media Monitoring with Watson APIsIBM Watson
Learn how the Watson Developer Cloud services can help you gain insights into the conversation around your brand. Our experts discuss the AlchemyLanguage, AlchemyData News, Emotion Analysis and Tone Analyzer APIs and how they can be applied to social media monitoring use cases. View the on-demand webinar and other sessions here: https://www.ibm.com/smarterplanet/us/en/ibmwatson/building-with-watson-webinar.html
"Planning Your Analytics Implementation" by Bachtiar Rifai (Kofera Technology)Tech in Asia ID
Bachtiar is a tech startup & science enthusiast with more than 7 years experience in digital marketing, ecommerce, analytics and product development. Bachtiar has spend his career life as marketing leader at top ecommerce such as Lazada & Blanja.com. Currently Bachtiar develop a startup called Kofera, a technology company who provides Software as a Service (SaaS) marketing automation platform powered by Artificial Intelligence (AI) and machine learning. Established in 2016, Kofera helps companies build & optimize PPC campaign using machine learning algorithm to maximize business ROI. Kofera has helped many clients from various industries. Recently, Kofera received pra-series A funding lead by MDI Ventures and followed by Indosterling, DNC & Gunung Sewu.
***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
Using Google Data Studio and Supermetrics to create your dashboard by Ann Sta...Ann Stanley
Ann Stanley presented a "Practical guide for using Data Studio (and Supermetrics) for report visualisation" at InOrbit 2018 conference in Slovnia.
This covers the following sections:
Getting started
Purpose and objectives
Metrics and Dimensions/Segments
Data sources
Demonstration of tools
Introduction to Supermetrics
Data Studio
Simple editing functions
Use of data controllers
Use of community connectors
Case study – tracking online leads to offline sales (integrating Salesforce data via Analytics)
Measure what matters for your agile projectMunish Malik
While working with Agile projects, we simply can't get away from tracking and showcasing the progress of the project. A typical Agile project would be working with estimates, story points, velocities, burn-up or burn-down charts.
I have witnessed numerous sprint reviews and showcases where the business is only waiting to see those few slides of the presentation where there is the "actual" red worm, running against the "planned" green worm, trying to catch-up. If the red worm is ahead, I have seen a smile on the faces of the stakeholders. If it matches the green one, there is a sigh of relief. And as a development team you should just pray that the poor red guy is not falling behind the green one, lest it might lead to a lot of questions starting with why, how, what etc.
There have also been times where there have been some unfortunate heated discussions that last forever on why did the team end up not claiming a few points that they had committed. What gets lost is what the team accomplished in the sprint that adds good value to the product. There have also been times where the estimates are being questioned by the product owner or account managers. If you are working in a distributed setup where the product owner is working out of a different country, the problem is even bigger.
Let us think about a scenario where the project gets completed on time, budget and scope. Majority (or all) of estimates were correct. However, when the product went live to the market it failed big time. What is the use of building such a product?
Are we focusing too much on numbers and points and overlooking the other important aspects of Agile software development such as producing software that delights the customers and looking for ways on how we can measure that? Are we measuring if we are creating a solid, robust and a scalable platform that is ready for future developments and enhancements? Are we measuring the outcomes of the time we are spending in the shoes of the people who will actually use the software?
The objective of this presentation is to promote the thinking of measuring what matters for your project. To measure the goals that your software development wants to achieve. I don't plan to showcase an exhaustive list of measurements that can solve all your problems, however, I instead want to highlight some samples that I have used in my projects with the help of my team, that helped us to measure things that add value to the business and development v/S simply creating burn down charts.
Majorly, I want to encourage thinking out of the box to identify what measurements will really matter for your projects. Perhaps from the eyes of the users and business and see what things if measured will add a lot more value than simply estimates, and will help in creating a valuable product that will truly delight the business and the users of the product.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.