Search results for monetization

    Found 102 documents, 13051 searched:

  • Data Monetization 101

    The evolving marketplace of data now includes many firms that support a variety of needs from organizations looking to grow with data. This listing of the key players categorized by target market provides an interesting picture of this exciting industry sector.

  • Lessons from Game of Thrones: Stopping the White Walkers of Data Monetization

    As I watched the impending battle between the White Walkers and humanity, I couldn’t help but identify a number of lessons that we can learn from Jon Snow’s battle with the leader of the White Walkers… and the power of Valyrian steel!

  • The Key to Data Monetization

    While I have talked frequently about the concept of Analytic Profiles, I’ve never written a blog that details how Analytic Profiles work. So let’s create a “Day in the Life” of an Analytic Profile to explain how an Analytic Profile works to capture and “monetize” your analytic assets.

  • Is Blockchain the Ultimate Enabler of Data Monetization?

    Is blockchain the ultimate enabler of data and analytics monetization; creating marketplaces where companies, individuals and even smart entities (cars, trucks, building, airports, malls) can share/sell/trade/barter their data and analytic insights directly with others?

  • Big Data Monetization Lessons from Zillow

    In the current tsunami of “Big Data” every business wants to get value out of the data. Here, we are sharing lessons learned by the new real estate websites who have brought together Big Data sets, home buyers, and home sellers.

  • 5 Key Components of a Data Sharing Platform

    Read this article for an overview of what the components of a data-sharing platform are.

  • Unstructured Data: The Must-Have For Analytics In 2022

    Let's investigate the current need that enterprise organizations have to rapidly parse through unstructured data and examine several data management trends that are highly relevant in 2022.

  • 10 Key AI & Data Analytics Trends for 2022 and Beyond

    What AI and data analytics trends are taking the industry by storm this year? This comprehensive review highlights upcoming directions in AI to carefully watch and consider implementing in your personal work or organization.

  • Amazon Web Services Webinar: Leverage data sets to create a customer-centric strategy and improve business outcomes

    Register now for this webinar, Oct 28, to learn how using third-party data enhances applications to better prioritize your target customer - helping you build a more customer-centric business.

  • Text Preprocessing Methods for Deep Learning

    While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.

  • 11 Best Data Science Education Platforms

    We cover 11 best Data Science Education platforms for 11 different use cases, ranging from specific languages to hands-on learners, to the best free option.

  • KDnuggets™ News 21:n29, Aug 4: GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks

    GitHub Copilot Open Source Alternatives; 3 Reasons Why You Should Use Linear Regression Models Instead of Neural Networks; A Brief Introduction to the Concept of Data; MLOps Best Practices; GPU-Powered Data Science (NOT Deep Learning) with RAPIDS

  • Awesome list of datasets in 100+ categories

    With an estimated 44 zettabytes of data in existence in our digital world today and approximately 2.5 quintillion bytes of new data generated daily, there is a lot of data out there you could tap into for your data science projects. It's pretty hard to curate through such a massive universe of data, but this collection is a great start. Here, you can find data from cancer genomes to UFO reports, as well as years of air quality data to 200,000 jokes. Dive into this ocean of data to explore as you learn how to apply data science techniques or leverage your expertise to discover something new.

  • 6 side hustles for an aspiring data scientist

    As an aspiring data scientist or an employed professional, many opportunities exist for you to offer your skills to a broader audience through side gigs. While the difficulty and risk vary, experiences from applying your data science practice to areas outside your immediate career path can increase your expertise while even increasing your bank account.

  • Reducing the High Cost of Training NLP Models With SRU++

    The increasing computation time and costs of training natural language models (NLP) highlight the importance of inventing computationally efficient models that retain top modeling power with reduced or accelerated computation. A single experiment training a top-performing language model on the 'Billion Word' benchmark would take 384 GPU days and as much as $36,000 using AWS on-demand instances.

  • Past 2021 Meetings / Online Events on AI, Analytics, Big Data, Data Science, and Machine Learning

    Past | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec Read more »

  • Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology">Gold BlogMain 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology

    Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  • How “Anonymous” is Anonymized Data?

    As the collection of personal data democratized over the previous century, the question of data anonymization started to rise. The regulations coming into effect around the world sealed the importance of the matter.

  • 10 Use Cases for Privacy-Preserving Synthetic Data

    This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals' privacy.

  • Top 10 Data Visualization Tools for Every Data Scientist">Silver BlogTop 10 Data Visualization Tools for Every Data Scientist

    At present, the data scientist is one of the most sought after professions. That’s one of the main reasons why we decided to cover the latest data visualization tools that every data scientist can use to make their work more effective.

  • AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020">Silver BlogAI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020

    We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.

  • The title CDO started out as a joke

    How did the role of Chief Data Officer come to drive data literacy at companies around the world? Find out how it all began in this interview with the first who held the title at Yahoo!

  • Sisense BloX – Go Beyond Dashboards

    Introducing Sisense BloX, the tool that allows you to integrate your business platforms inside your dashboards using prebuilt templates. Users stay within the dashboard environment and go from understanding insights to taking action—in one click.

  • Top 8 Data Science Use Cases in Gaming

    The understanding of the data value for optimization and improvement of gaming makes specialists search for new ways to apply data science and its benefits in the gaming business. Therefore, various specific data science use cases appear. Here is our list of the most efficient and widely applied data science use cases in gaming.

  • AI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019">Gold BlogAI, Data Science, Analytics Main Developments in 2018 and Key Trends for 2019

    Review of 2018 and Predictions for 2019 from our panel of experts, including Meta Brown, Tom Davenport, Carla Gentry, Bob E Hayes, Cassie Kozyrkov, Doug Laney, Bill Schmarzo, Kate Strachnyi, Ronald van Loon, Favio Vazquez, and Jen Underwood.

  • The Big Data Game Board™">Silver BlogThe Big Data Game Board™

    Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  • A Winning Game Plan For Building Your Data Science Team">Silver BlogA Winning Game Plan For Building Your Data Science Team

    We need to understand the responsibilities, capabilities, expectations and competencies of the Data Engineer, Data Scientist and Business Stakeholder.

  • Leveraging Agent-based Models (ABM) and Digital Twins to Prevent Injuries

    Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.

  • What’s the Difference Between Data Integration and Data Engineering?

    Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.

  • Great Data Scientists Don’t Just Think Outside the Box, They Redefine the Box

    The best data scientists have strong imaginative skills for not just “thinking outside the box” – but actually redefining the box – in trying to find variables and metrics that might be better predictors of performance.

  • Blockchains and APIs

    Major technological advances are providing opportunities for new business models, based on blockchain, which will see an increase in the number of connected devices in our day-to-day lives.

  • Age of AI Conference 2018 – Day 1 Highlights

    Here are some of the highlights from the first day of the Age of AI Conference, January 31, at the Regency Ballroom in San Francisco.

  • Exclusive Interview: Doug Laney on Big Data and Infonomics

    We discuss 3Vs of Big Data; Infonomics and many aspects of monetizing information including promising analytics methods, successful companies, main challenges; Information marketplaces and why data ownership concept is misguided, and more.

  • Democratizing Artificial Intelligence, Deep Learning, Machine Learning with Dell EMC Ready Solutions

    Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.” AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.

  • The Convergence of AI and Blockchain: What’s the deal?

    This article wants to give a flavor of the potentialities realized at the intersection of AI and Blockchain and discuss standard definitions, challenges, and benefits of this alliance, as well as about some interesting player in this space.

  • KDnuggets™ News 18:n01, Jan 4: Computer Vision by Andrew Ng: 11 Lessons Learned; How much Math do you need for Data Science? Top stories of 2017

    Also How Much Math Does an IT Engineer Need to Learn to become a Data Scientist? Yet Another Day in the Life of a Data Scientist; 70 Amazing Free Data Sources You Should Know.

  • Why Use Data Analytics to Prevent, Not Just Report

    The best way to reduce operating and business costs and risks is to prevent them!

  • Big Data: Main Developments in 2017 and Key Trends in 2018">Silver BlogBig Data: Main Developments in 2017 and Key Trends in 2018

    As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2017 and their 2018 key trend predictions.

  • Blockchain Key Terms, Explained

    Need a quick glance over some important definitions associated with the Blockchain? Then consider this article your Blockchain Definitions 101!

  • Introduction to Blockchains & What It Means to Big Data">Gold Blog, Sep 2017Introduction to Blockchains & What It Means to Big Data

    Perhaps most significant development in IT over the past few years, blockchain has the potential to change the way that the world approaches big data, with enhanced security and data quality.

  • Full Stack Data Science at ODSC

    Improve your skills in every layer of the Data Science stack at ODSC West 2017 and test drive the leading open source tools. Save 60% with code KD60 until Sep 1.

  • Google Analytics Audit Checklist and Tools

    In this post, a Google Analytics & Google AdWords expert shares his tips and tools of intelligent Google Analytics auditing. Read on for some practical insight.

  • KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail

    Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data

  • How will Big Data companies monetize data in 2018?

    In today’s data driven economy, Data is a strategic asset to a company and data monetization is prime focus of many companies. Let’s see how data monetization will be achieved in 2018.

  • How to Turn your Data Science Projects into a Success

    This interview with Dr. Olav Laudy, Chief Data Scientist for IBM Analytics, is a summary of a recent conference where he participated in a panel on the Big Data and Analytics

  • Why Every Company Needs a Digital Brain

    As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.

  • Difference Between Big Data and Internet of Things

    If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion. So what is required to support these IOT data management and analytic requirements?

  • KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook

    Also Top mistakes data scientists make when dealing with business people; New Online Data Science Tracks for 2017; Cartoon: Why AI needs help with taxes.

  • Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science, Mar 23-24, San Francisco

    This course will examine how firms can take big data to big profits through data monetization strategies and the best use of data science for growth and innovation across your organization.

  • Industry Predictions: Key Trends in 2017

    With 2017 almost upon us, KDnuggets brings you opinions from industry leaders as to what the relevant and most important 2017 key trends will be.

  • Artificial Intelligence Classification Matrix

    There might be several different ways to think around machine intelligence startups; too narrow of a framework might be counterproductive given the flexibility of the sector and the facility of transitioning from one group to another. Check out this categorization matrix.

  • Learn How to Turn Big Data into Big Profits: Northwestern MS in Analytics Executive Education Course

    Join Northwestern University's Master of Science in Analytics for an upcoming Executive Education Course, March 23 - 24, 2017 in San Francisco: Big Data to Big Profits.

  • Chief Data Officer Toolkit: Leading the Digital Business Transformation – Part 1

    CDOs are the new hot role to rock. Read about the CDO Toolkit, which integrates the disciplines of economics and analytics to help the CDO to ascertain the economic value of the organization’s data and data sources.

  • Valuable Data Products: Answers to Career Questions and More

    Collecting high quality data from various resources and turning it into data products is one of the ways to monetize data in today’s digital economy. Lets take a deeper look into it.

  • Big Data Dilemma: Save Me Money Versus Make Me Money">Silver BlogBig Data Dilemma: Save Me Money Versus Make Me Money

    Does your organization see Big Data as an opportunity to “Save Me More Money”, or does your organization see Big Data as an opportunity to “Make Me More Money”?

  • KDnuggets Interview: Inderpal Bhandari, IBM Global Chief Data Officer on 4 key ideas of Cognitive Computing

    In this wide-ranging interview, we discuss the role of IBM global chief data officer, 4 key ideas of cognitive computing, risks of AI, IBM Data Science Experience, healthcare, basketball, sports analytics, and more.

  • TalkingData Data Science Competition: understand mobile users

    Unique opportunity to solve complex real world big data challenges for the China mobile market - predict users demographic characteristics based on their app usage, geolocation, and mobile device properties.

  • Big Data into Big Profits: Northwestern Executive Education Course, Aug 25-26, San Francisco

    Join Northwestern Executive Education Course "Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science", Aug 25-26, in San Francisco.

  • What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data

    The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.

  • Big Data Business Model Maturity Index and the Internet of Things (IoT)

    This post explores how organizations could use the Big Data Business Model Maturity Index (BDBMMI) to exploit the Internet of Things (IoT).

  • Automakers Must Partner Around Big Data

    A discussion on the need for auto manufacturers to come together and leverage Big Data.

  • CRN Top Business Analytics Vendors 2016

    The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Business Analytics Vendors.

  • Tips for Data Scientists: Think Like a Business Executive

    Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.

  • Meet the 11 Big Data & Data Science Leaders on LinkedIn

    In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!

  • Practical Career Advice and Best Practices in Analytics

    Being an analyst is not only a technical job it also has a peoples side to it. Given that many MBAs, engineers, and even non-quantitative graduates are interested in Analytics careers, we are sharing some advice on best practices for excelling with Analytics in your career.

  • Big Data 2016: Top Influencers and Brands

    Onalytica gives us a new list of the top 100 Big Data influencers and brands, and provides some insight into both the relationships between influencers and their selection methodology.

  • Favorite 2015 Schmarzo Big Data Blogs

    A top Big Data influencer lists, outlines, and summarizes his favorite blog posts of 2015. Gain some additional insight into various data science topics with some of these great entries.

  • Airbnb: Lessons on Digital, Startups, Big Data and Disrupting Markets

    AirBnB has brought together unmatched supply and demand and allowed for market-driven evaluation of assets. We are sharing lessons learnt from them for digital startups and big data organisations.

  • Learning from Hurricanes: Big Data Analytics, Risk, & Data Visualization

    This year, Florida has experienced its 10th consecutive year without a hurricane, which is longest period without a hurricane strike in modern times. Exploring this is worthy of some examination, as it offers us many lessons in Big Data Analytics, Risk, and Data Visualization.

  • Uber-fication: Lessons from Uber in Economics, Digital, Risk, and Analytics

    Uber-fication or Uberisation is the conversion of existing jobs and services into discrete tasks that can be requested on-demand; the emulation or adoption of the Uber’s business model. Here we have discussed opportunities, risk and challenges while doing uberisation.

  • U. of California, Santa Cruz: Assistant Professor in Economic and Information Networks

    We seek outstanding candidates who do empirical and/or analytical fundamental research in information economics, network science, computational advertising or marketing analytics, mechanism design, or computational/algorithmic economics.

  • Top stories for Sep 13-19: Trying to acquire Machine Learning Skills? Deep Learning and Artistic Style

    Trying to acquire Machine Learning Skills? Deep Learning and Artistic Style - Can art be quantified?; Free Books on Big Data, Data Science.

  • KDnuggets™ News 15:n30, Sep 16: 60+ Free Books on Big Data, Data Science; Analytics Jobs Length; Salary by Role

    60+ Free Books on Big Data, Data Science, Data Mining, ML; Top 20 Data Science MOOCs; Data Scientists don't stay long in their jobs; Salaries by Roles in Data Science and BI.

  • From Big Data to Big Profits: A Lesson from Google’s Nest

    Google Nest is a very interesting example of how such a seemingly simple item as thermostat, with the addition to Big Data can transform an industry and lead to significant profits.

  • Macy’s: VP, Marketing Data Science and Analytics

    Informing business and marketing strategy, guiding tactical execution and identifying new opportunities to drive and grow sales across Macy's.

  • Strategies for Monetizing Big Data

    In the current tsunami of “Big Data” every business wants to get value out of the data. We examine four overarching data strategies and their specific monetization strategies.

  • Gaming Analytics Summit 2015, San Francisco – Day 1 Highlights

    Highlights from the presentations by Gaming Analytics leaders from Facebook, Turbine/Warner Bros Games, and Sega on day 1 of Gaming Analytics Innovation Summit 2015 in San Francisco.

  • Interview: Dave McCrory, Basho on Why Data Gravity Cannot be Ignored in Architecture Design

    We discuss data gravity and its implications, Riak Enterprise 2.0, Riak CS 1.5, competitive landscape, challenges and more.

  • Feb 2015 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    Feb 2015 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: @Kaggle cuts 1/3 of staff, Infosys buys Panaya, RapidMiner gets $15M, Palantir buys Fancy That, Hitachi buys Pentaho, and more.

  • Interview: Daqing Zhao, on Advanced Analytics for Marketing in the Big Data era

    We discuss Analytics at, comparison of advanced analytics with traditional BI, building data models for scalability, problem of data models becoming quickly obsolete and challenges in customer targeting.

  • Big Data & Analytics Innovation Summit, Australia: Day 2 Highlights

    Highlights from the presentations by Big Data leaders from Paypal, Huawei and Qantas on day 2 of Big Data & Analytics Innovation Summit 2014 in Sydney, Australia.

  • Interview: Pallas Horwitz, Blue Shell Games on Why Gaming Analytics is Not a Piece of Cake

    We discuss the challenges of gaming analytics, most desired missing data, current trends, career advice, important soft skills in data science and more.

  • Interview: Pallas Horwitz, Blue Shell Games on Why Data Science is So Critical for Gaming Studios

    We discuss the role of data science at Blue Shell Games, the importance of "Lean Data", key metrics for online games, cross-product projects and optimizing meeting the data needs across an organization.

  • Machine Zone: Data Scientist for World’s Largest Mobile MMO

    Develop and investigate hypotheses, structure experiments and build mathematical models to identify game optimization points that will encourage users to play our games.

  • Paychex: Sr. Manager, Predictive Analytics and Data Science

    Lead award winning team of 10 people responsible for enterprise wide analytics and model building, including all algorithm building across the enterprise and leveraging operational data into business insight.

  • June 2014 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    June 2014 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: MapR, CloudPhysics, Clari, ThoughtSpot, SiSense, Elasticsearch, Trifacta, @CBinsights startup post-mortem lessons, and more.

  • Gaming Analytics Innovation Summit: Day 2 Highlights

    Highlights from the presentations by Gaming Analytics experts from Ubisoft, Electronic Arts, Sega on Day 2 of Gaming Analytics Summit 2014.

  • UBS Research: Digital/Web Analytics Manager

    Help define our attribution strategy, KPIs, and reporting framework for our cross channel campaigns, track research campaigns from send to open to click to landing page activity and finally monetization.

  • KDnuggets™ News 14:n13, May 28

    Features (5) | Software (3) | Opinions (5) | News (1) | Webcasts (1) | Courses (1) | Meetings and Reports (3) | Jobs (5) Read more »

  • Paychex: Manager, Risk Modeling & Review

    Lead efforts to plan, design strategy, build, deploy and monitor predictive models to leverage revenue opportunities, and mitigate risks.

  • Big Data Innovation Summit 2014 Santa Clara: Highlights of Selected Talks on Day 1

    Highlights from the presentations by big data technology practitioners from eBay, YarcData, LinkedIn, Trulia, and other leading companies on day 1 of Big Data Innovation Summit 2014 in Santa Clara.

  • Useful Business Analytics Summit in Boston, June 10-11

    Join the Useful Business Analytics Summit (Boston, June 10-11), the unique conference where corporate peers can meet and determine how USEFUL analytics can improve business decision making. Special KDnuggets discount and early bird by Apr 18.

  • December Analytics, Big Data, Data Mining Companies and Startups Activity

    December 2013 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science:, Talend, Palantir, KPMG, Datameer, Dell

  • MachineZone: Data Scientist

    Develop and investigate hypotheses, structure experiments and build mathematical models to identify game optimization points that will encourage users to play our games more.

  • Top KDnuggets tweets, Sep 27-30: Data Mining and Analysis, free book (draft) download; Random Forests Algorithm – why it works

    New Book: Data Mining and Analysis: Fundamental Concepts and Algorithms, free PDF dow; Random Forests Algorithm - what is it, why does it work so well; Penn researchers use Facebook data to predict users age, gender, personality; Google Hummingbird is a completely new search algorithm and incredibly no one noticed

  • Sr BI Engineer, Amazon Product Ads

    Product Ads is an innovative hybrid of ecommerce product selection with a cost-per-click advertising model. Success for this role is about putting information into the hands of business owners.

  • DeveloperWeek Analytics Awards

    Here are top innovators in Analytics-as-a-service, SQL Technologies, NoSQL, Big Data, Consumer Data, Hadoop, App Analytics, and Social Data, as chosen by DeveloperWeek community.

  • Strata Conference Reports and Highlights

    Highlights from Strata Feb 2013 Conference on Big Data, covering Hadoop, Python Data Science, game data mining, Groundhog day, data thoughtcrime, situational awareness, and more.

  • Additions to KDnuggets Directory in February, open government data from US, EU, Canada, and new companies, datasets, education, 22 meetings, software, solutions, websites.

  • Software Development Manager – Advertising

    Looking for talented managers who enjoy ideating new features, product definitions, creative algorithms, helping build large scale systems and who thrive in a fast paced fun environment.

  • ACM SIGKDD 2013 Service Award to Gabor Melli

    Dr. Gabor Melli is recognized for his substantial technical contributions to the practice and application of data mining and for his outstanding service to the global KDD community.

  • Companies with Analytics, Data Mining, Data Science, and Machine Learning Products

    A B C D E F G H I J K L M N O P Q R S T U V W XYZ Advanced Read more »

Refine your search here: