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Elevating Business Strategies with Top Data Visualization Companies

ultimate-checklist-for-mastering-aesthetics-in-data-visualization

Dive into mastering aesthetics in data visualization with this checklist. Explore techniques to enhance visualizations, and achieve lasting impact. Learn to create visually stunning presentations.

Contents

1. Introduction
2. Key Principles of Data Visualization
3. Ultimate Checklist for Improved Data Visualization
4. Top Companies Revolutionizing Data Visualization 5. Summing Up

1. Introduction

Effective communication of insights and discoveries is essential for taking action and making well-informed decisions in today's data-driven society. When combined with excellent storytelling, data visualization offers powerful tools for converting complex data into Engaging stories that captivate audiences.

Acquiring proficiency in data visualization aesthetics becomes imperative for businesses looking to extract the most value from their information. Organizations can enhance their understanding of intricate data and revolutionize their interactions with it by adopting modern technologies and innovative approaches to information visualization. Insights can be efficiently communicated through data visualization with the help of this guide, to empower data science professionals and companies in mastering the aesthetics of data visualization.

Aesthetics in data visualization refer to the design principles and techniques used to enhance the visual appeal and effectiveness of data presentations. These include aspects such as color choice, typography, layout, and interactivity, all aimed at making the data more engaging and understandable to the audience.

2. Key Principles of Data Visualization

In the field of data visualization, graphical information representation is an important tool for improving interpretation, analysis, and communication. A variety of methods, including maps, graphs, charts, and infographics, are used to simplify complex statistics into representations that are easy to understand and visually appealing. Selecting the appropriate visualization method depends on the type of data and the insights that are sought. For example, scatter plots and maps work well with multivariate and spatial data, but bar charts and line graphs are excellent at displaying trends. To guarantee that the audience understands, it is crucial to emphasize simplicity and clarity. To reduce cognitive burden and facilitate understanding, usage of clear labeling, detailed names, and sensible color schemes are followed.

To create impactful visualizations, certain principles should be followed:

  • Simplify complex data by presenting it in clear and concise visual representations, aiding comprehension.
  • Utilize appropriate chart types tailored to data types to ensure accurate and meaningful representation.
  • Design for clarity and readability by choosing layouts, fonts, and sizes that enhance comprehension.
  • Incorporate meaningful colors, labels, and annotations to highlight key information and guide attention.
  • Emphasize storytelling through data visualization, structuring visuals to drive insights and action.

3. Ultimate Checklist for Improved Data Visualization

Optimizing data visualization in businesses rely on adhering to essential best practices throughout the design and implementation process. Here are the top five strategies to ensure impactful data presentations:

  • Audience-Centric Approach: Tailor visualizations to meet the specific needs of the audience. Whether targeting executives for high-level insights or analysts for in-depth data exploration, align the visualizations with their expectations to deliver meaningful insights effectively.
  • Strategic Chart Selection: Choose chart types that best convey data insights. Opt for bar charts to compare values across categories, line charts to illustrate trends over time, and other formats that align with the data narrative for clarity and comprehension.
  • Streamlined Visuals: Simplify visualizations by decluttering unnecessary elements that could distract or confuse viewers. Maintain a clean design with a restrained color palette and avoid overly complex effects to ensure clarity and focus on the data message.
  • Engaging Storytelling: Elevate the visualizations by integrating storytelling techniques. Add context through annotations, craft compelling titles and captions, and structure visualizations in a logical sequence to guide viewers through the data narrative seamlessly.
  • Consistent Design Standards: Ensure consistency in design elements like fonts, axis labels, and legends across all visualizations. This fosters readability and provides a cohesive visual experience, enhancing understanding and retention of the data presented.

4. Top Companies Revolutionizing Data Visualization

4.1 Databox

Overview:

Databox is a versatile data visualization platform offering powerful features for connecting and analyzing data from various sources. It enables users to create interactive dashboards, track key metrics, and collaborate with team members effectively.

Key Features:

  • Integrates with popular platforms like Google Analytics, HubSpot, and Facebook Ads, allowing consolidation of data from multiple sources into a single dashboard.
  • Offers ready-made templates and dashboards to expedite visualization creation, saving time and effort.
  • Users can customize dashboards with various visualization options, chart types, and widgets to suit specific needs.
  • Facilitates teamwork through collaboration and sharing features, enabling automated alerts, notifications, and insights sharing with stakeholders.
  • Provides advanced features like automated alerts, goal tracking, performance monitoring, data transformations, and calculations to enhance data visualization and analysis.

4.2 NinjaCat

Overview:

NinjaCat is a robust data visualization tool designed to streamline reporting and analysis tasks for digital marketers and agencies. It offers a comprehensive suite of features aimed at simplifying data aggregation, visualization, and reporting across various advertising platforms.

Key Features:

  • NinjaCat seamlessly integrates with popular advertising platforms such as Google Ads, Facebook Ads, Microsoft Advertising, LinkedIn Ads, and more, enabling users to gather data from multiple sources in one centralized location.
  • Users can create highly customizable dashboards tailored to their specific reporting needs. NinjaCat provides a wide range of visualization options, including charts, graphs, tables, and widgets, allowing users to present data in a visually appealing and easily digestible format.
  • NinjaCat automates the reporting process by scheduling and sending reports to stakeholders on a regular basis. Users can set up custom report templates, define reporting intervals, and choose delivery methods (e.g., email, PDF, URL) to streamline communication and collaboration.
  • NinjaCat offers advanced performance monitoring capabilities, allowing users to track key metrics, analyze trends, and identify opportunities for optimization. Users can set up alerts and notifications to stay informed about significant changes in campaign performance.
  • NinjaCat allows users to create interactive reports with drill-down capabilities, enabling stakeholders to explore data in more detail and gain deeper insights into campaign performance.

4.3 Plecto

Overview:

Plecto is a cloud-based performance dashboard designed to streamline data management and visualization for businesses of all sizes. Its suite of features includes data integration, scorecards, gamification, and data analytics, empowering teams to visualize performance in real time on customized dashboards. Key functionalities such as drag-and-drop widgets and integration with various data sources make it easy for users to create comprehensive dashboards tailored to their needs.

Key Features:

  • Gain instant insights into key performance indicators (KPIs) with real-time data visualization, enabling quick decision-making and action.
  • Create customized dashboards by integrating data from multiple sources, including integrations, spreadsheets, databases, and manual registrations, to centralize information and streamline analysis.
  • Drive employee engagement and motivation by using gamification features such as contests, achievements, and leaderboards to recognize and reward team members based on performance.
  • Seamlessly integrate Plecto with popular third-party applications like Salesforce, Pipedrive, Podio, Zendesk, and Zapier to consolidate data and enhance visibility across systems.

4.4 Venngage

Overview:

Venngage is a user-friendly infographics tool that empowers users to create stunning infographics, reports, and data visualizations quickly and effortlessly. With its intuitive interface and extensive library of templates and design elements, Venngage caters to both beginners and advanced users, offering highly customizable controls for creating professional-looking infographics.

Key Features:

  • Choose from a vast collection of professionally designed templates and themes to kickstart the infographic creation process.
  • Easily add charts, icons, text, or images to the template by simply dragging and dropping elements onto the canvas.
  • Customize and stylize infographic elements to match brand's identity and messaging, with controls for colors, fonts, shapes, and more.
  • Share infographics directly on social media platforms or download them as images for offline use, enabling easy distribution and sharing with the audience.

4.5 Prodoscore

Overview:

Siloed data can impede collaboration and growth within organizations. Prodoscore addresses this challenge by aggregating data from core cloud applications into one intuitive dashboard. It seamlessly integrates with Google Workspace, Microsoft 365, and other common applications, allowing teams to visualize employee engagement with software tools. This visualization helps identify underutilized solutions, optimize tech stacks, and pinpoint training needs. Prodoscore provides management with complete transparency, enabling informed decisions about technology effectiveness and resource allocation.

Key Features:

  • Prodoscore merges data from various business applications into a user-friendly dashboard, presenting it as a simple score.
  • The platform offers tools to visualize employee data trends in real time, empowering informed decision-making.
  • Prodoscore focuses on surfacing actionable insights around engagement and productivity, providing leaders with objective data to make smarter decisions.
  • With just a 15-minute installation process, Prodoscore quickly integrates into existing workflows without disrupting operations.
  • Utilizing AI, Prodoscore aggregates critical data to generate scores that reflect employee engagement and productivity effectively.

4.6 Genially

Overview:

Genially is an innovative online tool designed for creating engaging digital content, including presentations, infographics, gamifications, images, and more. With a user-friendly interface and a focus on interactivity and animation, Genially empowers users to captivate their audience and elevate their content to new heights.

Key Features:

  • Genially allows users to create a wide range of digital content, termed as ‘geniallys,’ which are not limited to traditional presentations or infographics. These geniallys are interactive and animated, offering a unique and engaging experience for viewers.
  • Genially's features make it practical for visualizing data, enabling users to easily transform raw data into visually appealing content. Users can import content from various platforms and formats, including Google Sheets, Infogram, and Data wrapper.
  • Genially seamlessly integrates with external tools like Infogram and Data wrapper, allowing users to embed graphics directly into their geniallys using HTML code. Users can even embed dashboards from Google Data Studio, maintaining filters and structure for efficient reporting.
  • Users can share geniallys using links or embed them in websites, digital newspapers, blogs, and more. Updates made to geniallys are automatically reflected everywhere they are embedded or inserted, ensuring that viewers always see the latest version.

4.7 Forsta

Overview:

Forsta is a pioneering technology company specializing in experience and research technology, with a unique focus on collaboration and client-centered solutions. At the heart of Forsta's offerings is the human experience ( HX ) platform, an award-winning platform that integrates customer experience (CX), employee experience (EX), and market research to provide comprehensive insights into audience experiences across industries.

Key Features:

  • The Forsta HX Platform seamlessly integrates CX, EX, and market research, breaking down silos and providing a holistic understanding of audience experiences.
  • Forsta prioritizes collaboration, listening, and co-designing solutions with clients to meet their unique needs effectively.
  • With cutting-edge technology and expert consulting services, Forsta assists organizations across various industries in leveraging data for informed decision-making to drive innovation.
  • Forsta's commitment to excellence is recognized by industry leaders, with accolades including leadership positions in the Gartner Magic Quadrant and the G2 grid, along with recognition in the TMC CUSTOMER Experience Innovation Awards.
  • Forsta's Visualizations module continues to evolve with frequent updates and enhancements, introducing 36 new features over seven months. These enhancements cater to diverse analytical needs, from exploratory analysis to dynamic dashboard creation and automated reporting.

4.8 AmplifAI

Overview:

AmplifAI is the forefront of platforms providing AI in HR to drive people enablement, dedicated to creating employee-centric work environments. By transforming employee data into actionable insights modeled after top performers, AmplifAI facilitates hybrid teams in maximizing business outcomes, enhancing productivity, and fostering engagement. As organizations transition to hybrid work models, AmplifAI emerges as a trusted partner for innovative leaders, enabling performance improvement and injecting fun into work, regardless of the location.

Key Benefits:

  • Accelerate employee performance to desired levels swiftly through metric-based performance analysis, self-paced micro-learnings, and intelligent coaching interventions.
  • Equip supervisors with automated, data-driven recommendations that consistently elevate agent and team performance, enhancing overall leadership effectiveness.
  • Role-based visualizations deliver personalized actions directly to employees, empowering them with clear directives to enhance their performance based on data-driven insights.
  • Elevate employee experience by providing resources necessary for skill growth and daily excellence, thereby reducing attrition rates within contact centers and fostering a culture of continuous improvement.

4.9 think-cell Software

Overview:

Think-Cell Software is at the forefront of productivity software, particularly in the area of data-driven presentations in Microsoft PowerPoint. Its purpose is to simplify complex charting tasks, allowing users to generate complex charts with simplicity. It increases productivity and efficiency by providing a full toolbox and streamlining processes, making it a reliable partner for professionals looking to improve their presentations and give relevant insights.

Key Advantages:

  • Leveraging Think-Cell is intuitive, as its processes closely resemble powerpoint's functionalities. The underlying datasheet is excel-based, ensuring a seamless transition for users familiar with microsoft office tools.
  • Think-Cell fills the gaps left by PowerPoint, offering a plethora of frequently used chart types and elements absent in the native software. This comprehensive toolkit empowers users to create visually stunning charts with ease.
  • Leading consulting firms rely on Think-Cell for a reason that it saves up to 70% of their time spent on charting tasks. Delve into some unique Think-Cell charting features, exclusive to the platform, that expedite chart production and optimize efficiency.
  • With Think-Cell at disposal, one can elevate the corporate presentations to new heights. Discover innovative charting solutions and unleash the creativity while saving valuable time.

4.10 Qalyptus

Overview:

Qalyptus offers a comprehensive reporting solution tailored for users of Qlik Sense and QlikView, aiming to make information accessible to everyone with intuitive ease. With Qalyptus, the entire reporting process, from creation to distribution and administration is streamlined for maximum efficiency.

Key Features:

  • Qalyptus simplifies the report creation process, allowing users to generate custom reports from Qlik Sense and QlikView data effortlessly. Whether it's in PDF, powerpoint, excel, word, HTML, or other formats, creating a personalized report takes less than 5 minutes.
  • Once reports are created, Qalyptus enables seamless distribution to an unlimited number of users. Whether it's internal stakeholders, clients, or partners, sharing insights derived from Qlik Sense and QlikView is hassle-free with Qalyptus.
  • Qalyptus provides robust administration capabilities, allowing users to manage and oversee the entire reporting workflow efficiently. From user access controls to scheduling and automation, Qalyptus empowers administrators to maintain control and ensure smooth operations.

5. Summing Up

Delving deep into data visualization aesthetics proves instrumental in unraveling the insights concealed within vast datasets and transforming them into actionable intelligence. By adhering to the principles of aesthetics and functionality, organizations can craft visualizations that inform and inspire, paving the way for strategic decision-making and fostering a culture of data-driven innovation. Looking ahead, the future scope for mastering the aesthetics of data visualization holds promising prospects. As technology continues to evolve and anticipate advancements in interactive visualization tools, AI-driven design recommendations and augmented reality integration offers even greater opportunities for creating compelling and impactful visualizations. With a continued focus on innovation and adaptation, organizations can stay at the forefront of data visualization excellence, driving further advancements in business intelligence and decision-making processes.

Spotlight

Tangent Works

Tangent Works is the brand of 2BRIDGZ solutions NV. Tangent Works is a machine learning company that bridges cutting edge academic research in the field of machine learning to challenges in the industry. Tangent Works is currently located in Ghent - Belgium and Bratislava - Slovakia. As machine learning is rapidly becoming a key asset in an interconnected data driven society, Tangent Works is building a world class team of data scientists to develop and implement cutting edge solutions in the field of predictive analytics...

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Learn from professionals that share their success stories and insights about predictive analytics in a variety of industries, including media, healthcare, and finance. The complimentary ticket grants complete access to all Tech Show London events, offering attendees an extensive overview of the technology environment influencing predictive analytics. 3. Summing Up In 2024, predictive analytics professionals must seize the opportunity at top events like Predictive Analytics Events 2024. These events provide priceless networking opportunities, insights, and resources for understanding the data-driven area of artificial intelligence and machine learning. For B2B executives and specialists, these events are essential for gaining a competitive edge and driving innovation in today's digital era.

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Predictive analytics and business intelligence have become some of the most important tools for businesses because of their outstanding capabilities. Most people believe that predictive analytics is a part of business intelligence (BI), but that is not the case. If we look at the definition of business intelligence, we can argue that predictive analytics actually falls under the umbrella of BI, but that's not entirely true. While that definition is pretty much correct for both terms, if we dig down a little deeper, we will see that there are significant differences between business intelligence and predictive analytics in both practices as well as theories. Let’s drill down to understand the key differentiators between predictive analytics and business intelligence. Key Differentiators: Predictive Analytics VS BI BI seeks to answer queries like "what happens now" and "what is happening now," whereas predictive analytics tries to predict "what will happen" and provides a more practical method to assess information. Data Raw data is processed into insights for direct consumer use during the business intelligence process. With predictive analytics, unstructured data is turned into structured data that can be used to make predictions about the future. Decision Users can make decisions based on insights provided by business intelligence. Businesses can use predictive analytics to make decisions based on facts, data sets, and predictions. Purpose The objective of business intelligence tools is to equip users with information about their company's historical data performance. Predictive analytics utilizes forecasting techniques to help in the solving of complex business challenges. Methods Business intelligence uses data visualization, data mining, reporting, dashboards, OLAP, etc., with previous performance indicators. Predictive analytics predicts future occurrences and analyzes raw data patterns. Technologies Ad-hoc reporting technology, alerting technology, and other technologies are covered in business intelligence. Predictive analytics includes technologies such as predictive modeling, forecasting, etc. Use Predictive Analytics in Business Intelligence to Optimize Marketing Efforts Businesses now have a plethora of information about their customers’ and target audience's purchasing patterns and preferences, all thanks to business intelligence insights. With all of this information, predictive analytics can determine the possibility of a consumer purchasing a product, allowing businesses to target their marketing efforts on customers who are more likely to purchase their items. Businesses that employ predictive analytics and business intelligence solutions can constantly remain one step ahead of their competition. Summing Up At times, the sheer variety of tools available can be intimidating, and misinformation can sometimes hamper the selection process of technology. Business intelligence and predictive analytics are two of the most productive technologies in the market, but when combined, they can do wonders for businesses.

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Thinking Like a Data Scientist

Article | December 23, 2020

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Big Data Management, Data Visualization, Data Architecture

Data Virtualization: A Dive into the Virtual Data Lake

Article | August 18, 2022

The telecom industry has witnessed spectacular growth since its establishment in the 1830s. Enabling distant communications, collaborations, and transactions globally, telecommunication plays a significant role in making our lives more convenient and easier. With enhanced flexibility and advanced communication methods, the telecom industry gains more customers and creates new revenue streams. According to Grand View Research, the global telecom market size would expand at a compound annual growth rate (CAGR) of 5.4% between 2021-2028. With the rapidly growing digital connectivity, the communication service providers (CSPs) have to deal with large datasets. Datasets that can allow them better to understand their customers, competitors, industry trends and derive valuable insights for decision making.

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Data Visualization

Data Visualization: Why Does It Matter in Businesses?

Article | February 18, 2022

Explore the world of predictive analytics for businesses with the help of this comprehensive article. Examine vital predictive analytics events in 2024 to improve business strategy and outcomes. Contents 1. Introduction to Predictive Analytics 2. Top Predictive Analytics Events in 2024 2.1 Machine Learning Week 2024 2.2 Machine Learning Week Europe 2024 2.3 Marketing Analytics Summit 2024 2.4 Generative AI World 2024 2.5 SMX Paris 2024 2.6 MLconf New York City 2024 2.7 Data Science Salon Austin 2024 2.8 Data Innovation Summit 2024 2.9 Gartner Data & Analytics Conference 2024 2.10 Big Data & AI World 2024 3. Summing Up 1. Introduction to Predictive Analytics For business executives looking for innovation and long-term vision, 2024 stands out as a critical year in the quickly developing field of data science and analytics. The major predictive analytics events in 2024 throughout the year provide essential forums for change by providing in-depth knowledge of the most recent tactics and trends in the field. For CEOs and analytics specialists, these conferences are crucial because they offer a special fusion of expertise, teamwork, and creativity needed to successfully negotiate the challenges of the data-driven world. They guarantee to provide participants with the tools and connections required to lead their companies toward expansion and gain a competitive edge. These developments will be crucial in shaping the direction of predictive analytics and corporate strategy going forward, making 2024 a historic year for those leading the charge in digital transformation. 2. Top Predictive Analytics Events in 2024 The field of predictive analytics is developing rapidly and pushing significant developments in several industries. In 2024, professionals who are keen to learn about the newest strategies and ideas will have access to a wide range of top conferences. These conferences provide unmatched chances for networking and knowledge sharing in a variety of industries, guaranteeing that participants stay at the top of predictive analytics. 2.1 Machine Learning Week 2024 Date: June 4-7, 2024 Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ The future of artificial intelligence in business is expected to be drastically changed by Machine Learning Week in 2024. For those at the forefront of machine learning, this will be a vital gathering that offers a combination of keynote addresses, plenary sessions, and practical workshops. It is a must-attend event for anybody hoping to stay ahead in the constantly changing machine learning fields, as it provides a unique chance for experts from a variety of sectors to explore the nexus between AI and business. Principal Aspects of the Event Exploring Machine Learning: A perspective on game-changing advances and transformative AI developments Practical Applications: Workshops and case studies centered on actual implementation in a variety of sectors. Responsible AI: Using moral principles to inform responsible AI development and application. Networking and Collaboration: Chances for participants to meet visionaries and leaders in the field. Who Can Attend ML Week 2024 Data Scientists & Analytics Managers: Individuals working in analytics and data-driven decision-making. Software Engineers & AI Practitioners: Experts who develop and implement artificial intelligence systems. Entrepreneurs & Decision-Makers: Professionals who aim to comprehend artificial intelligence and include it into their working life. 2.2 Machine Learning Week Europe 2024 Date: November 18-19, 2024 Venue: Munich, Germany An important event for utilizing AI, machine learning, and predictive and prescriptive analytics across a variety of industries. With sessions in Tech/Deep Dives, Business and Industry Case Studies, and extensive seminars, this will be one of the top data analytics events of this year that aim to create a collaborative platform for data science experts throughout Europe. Key Benefits of Attending Machine Learning Week Europe 2024 Practical insights: Learn how machine learning and artificial intelligence are being applied across various industries. Case Study Learning: Learn from in-depth case studies across a range of industries. Technical Engagement: Take part in in-depth technical discussions of the newest machine learning innovations. Professional networking: Network with a group of individuals working in data science. Trend Awareness: Stay abreast with the newest developments in an area that is evolving quickly to improve knowledge and abilities. 2.3 Marketing Analytics Summit 2024 Date: June 6-7, 2024 Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ The Marketing Analytics Summit 2024 is a premier event of marketing professionals and leaders in digital analytics, with the goal of developing the discipline of marketing analytics. The summit's main goal is to empower participants with the most recent information and resources for using marketing data to inform strategy and decision-making. Phoenix, which is renowned for its dynamic corporate scene, provides a bright backdrop for the event, which has a strong history of innovation and community building. Keynote Speakers Lina Mikolajczyk, Director of Analytics Joe A. Miscavige, Senior Director of Data Distribution Strategy Ely Rosenstock, Associate Director, Human Health Digital, Data, and Analytics Jim Sterne, Founder Benefits of Attending the Marketing Analytics Summit 2024 Knowledge Sharing: Learn from more than 1,500 speakers who have made major contributions to the field of marketing analytics. In-Depth Workshops: Take part in programs aimed at technical mastery and effective marketing insight sharing, such as ‘SQL on GA4 in BQ: BigQuery for Digital Marketers’ and ‘Building World-class Business Dashboards.’ Networking Opportunities: Make meaningful connections with a varied range of business leaders, opinion leaders, and online analysts to develop fruitful professional partnerships. Career and Business Growth: Gain knowledge from a conference that provides unrivalled chances for personal and entrepreneurial development, having advanced over 20,000 careers and acted as a launchpad for 100 enterprises. Community Engagement: Be a part of a group that is leading the way in establishing the standards and practices in the marketing analytics sector, having established the Digital Analytics Association with great pride. 2.4 Generative AI World 2024 Date: June 6-7, 2024 Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ Generative AI World 2024 emerges as a pivotal event, set against the dynamic backdrop of Phoenix, AZ. It aims to convert the burgeoning excitement around Generative AI into actionable business strategies and value, positioning itself as a critical summit for data analytics professionals driving digital innovation. Workshops and Key Speakers June 4: ‘Deep Learning in Practice: A Hands-On Introduction’ by Bardia Beigi and Prerna Singh ‘Generative AI: From Basic Concepts to Real-World Applications’ by Martin Musiol Networking Cultivation Platform Hundreds of CEOs, data scientists, and AI pioneers from a variety of industries will have a common platform to interact at Generative AI World 2024. The goal of the conference is to provide an environment for deep industry networking, lively discussions, and the exchange of significant information, ensuring that participants depart with meaningful relationships and new perspectives. Noteworthy Focus The conference's business focus sets it apart from the competition as it seeks to bridge the gap between scholarly research and the real-world application of generative AI in the marketplace. Through hands-on demonstrations and discussions on the most recent developments, attendees will be able to see personally the transformational potential of generative AI. These sessions will highlight real-world applications and the challenges associated with implementing these technologies. 2.5 SMX Paris 2024 Date: March 14-15, 2024 Venue: Étoile Business Center, Paris, France Immerse in the evolving field of search marketing at SMX Paris 2024. This 13th edition of the conference, taking place in the heart of Paris, is designed to arm professionals with the insights and trends that will define the future of the industry. With keynote speakers Rand Fishkin and Sam Tomlinson leading the charge, attendees will be in for an enlightening experience that will push the boundaries of search marketing into new territories. Why Attend SMX Paris 2024 The event has been carefully planned to include a wide range of challenges that are important for today's professionals, such as the implications of automation and artificial intelligence in advertising and e-commerce, as well as SEO methods designed for scaling firms. In addition to being an excellent source of information, the conference serves as a dynamic forum for networking, giving participants the ability to get in touch with thought leaders, entrepreneurs, and other professionals. For individuals who aspire to be successful in the search marketing industry, SMX Paris is an essential event due to its exceptional combination of in-depth education, professional insights, and networking possibilities. Who Must Not Miss This Event A wide range of people in the search marketing and digital advertising industries are the target audience for SMX Paris 2024. The conference is tailored to meet the goals of corporate search marketing experts, agency SEO/SMO/SEA professionals, marketing managers or directors seeking strategic enhancement, as well as webmasters and site administrators focused on optimizing site referencing efficiently. Attending the sessions and workshops will be fruitful for anyone aiming to stay ahead in the ever-changing field of search marketing. 2.6 MLconf New York City 2024 Date: March 28, 2024 Venue: New York City Professionals from a variety of academic disciplines and industries are invited to attend MLconf New York City 2024 to learn more about the latest developments in the field of machine learning innovation. This conference will provide alternatives for both virtual and in-person attendance that are accessible to a worldwide audience. Speakers Prominent speakers on AI research, data science, engineering, and machine learning applications will include Debasmita Das from Mastercard, Arun Krishnaswamy from Zuora, Jeremy Wilken from NVIDIA, Facundo Santiago from Microsoft, and Sanket Gupta from Spotify. Who Should Attend A wide range of professionals, including data scientists, engineers, software developers, computer vision specialists, technical leaders, entrepreneurs with start-ups, and academics made up of instructors and students, are an ideal audience for MLconf NYC 2024. By exchanging extensive knowledge, innovative approaches, and networking opportunities, attendees will be positioned to lead the way in machine learning innovation. 2.7 Data Science Salon Austin 2024 Date: March 20-21, 2024 Venue: Austin, TX Austin, Texas will be the host to the Data Science Salon 2024 once again, providing a platform for thought leaders in the field for a meeting and explore into innovative subjects including predictive analytics, generative AI, and machine learning. Through a unique combination of technical discussions, seminars, and use scenarios, participants will get practical insights from the leaders of machine learning inside a business environment. Learning and Innovation Creative Solutions: Participants will take part in talks with eminent speakers—Fatma Tarlaci, Eric Landry, Sreevani Konda, and others—who will share their thoughts on the most recent developments in generative AI, machine learning, and predictive analytics. Special Addition: The ‘Future of ML & AI Startups + Showcase’ will highlight the inventive spirit of the AI and ML startup community by providing a rare chance to examine VC panels, startup success stories, and a startup display with a $10,000 award. Exposure to Startup Showcase: Explore innovative AI and ML startups, learn from their success stories, and witness groundbreaking ideas in action, providing inspiration and potential opportunities for investment or collaboration. 2.8 Data Innovation Summit 2024 Date: April 24-25, 2024 Venue: Onsite at Kistamässan, Stockholm | Online through Agorify Take advantage of the most important Data and AI event of the year, the Data Innovation Summit 2024, and join the latest generation of data, analytics, and AI specialists. This hybrid event, which will be streamed live online via Agorify and held at Stockholm's Kistamässan, will launch a new chapter in the history of data-driven transformation. Key Benefits of Attending Discover better from more than 300 sessions covering a broad spectrum of topics from applied AI to IoT, all led by 300 Nordic and international speakers. Connect with over 3000 attendees from around the world at networking events, expo explores, and the special DIS24 AW & Data After Dark. Explore more than 100 exhibitors' modern data and AI solutions, which highlight creative initiatives, goods, and technologies. Who Should Attend Practitioners in any enterprise looking to speed up AI-driven organizational change. Technology companies showcasing their most recent breakthroughs and solutions. Innovative startups looking to expand their networks, become more visible, and find possible partners. Academics who want to remain up to date on the newest innovations and patterns in the data and artificial intelligence domains. 2.9 Gartner Data & Analytics Conference 2024 Date: March 26-27, 2024 Venue: São Paulo, Brazil Attracting Chief Data Analytics Officers (CDAOs) and Data & Analytics (D&A) professionals from many industries, the Gartner Data & Analytics Conference 2024 in Sao Paulo, Brazil, becomes the essential gathering place for IT and business executives. This conference provides beneficial insights and connections as it is committed to launching enterprises into a new era of innovation through the strategic use of data, analytics, and artificial intelligence. Participants can anticipate gaining priceless insights and strategies to propel company expansion and success in the dynamic field of data-driven decision-making. Key Benefits of Participation Learn important lessons from over 72 sessions covering the most recent research from Gartner, designed to maximize corporate value and decision-making. Join a global network of thought leaders, D&A executives, and CDAOs to exchange strategies, difficulties, and innovations. Examine a wide range of subjects that are important to D&A executives, such as maximizing company value, data management best practices, and creative models. Who Should Attend Heads of Data & Analytics Chief Data Officers (CDOs), Chief Analytics Officers (CAOs), Chief Data Analytics Officers (CDAOs) Analytics and Business Intelligence (BI) Leaders Information Management and Master Data Management Leaders Architects and IT Professionals 2.10 Big Data & AI World 2024 Date: March 6-7, 2024 Venue: ExCeL London Big Data & AI World 2024, a premier event in the UK for cutting-edge technologies and innovative ideas in big data and artificial intelligence, is poised to revolutionize the field of predictive analytics. Scheduled for March 6-7, 2024, at ExCeL London, this conference promises to be an unparalleled convergence of technology enthusiasts eager to drive digital evolution with predictive analytics at the forefront. Why Should You Be There Prepare for the next phase of digital transformation by investing in sessions that center on predictive analytics. Optimize with Big Data, improve with AI. Learn from professionals that share their success stories and insights about predictive analytics in a variety of industries, including media, healthcare, and finance. The complimentary ticket grants complete access to all Tech Show London events, offering attendees an extensive overview of the technology environment influencing predictive analytics. 3. Summing Up In 2024, predictive analytics professionals must seize the opportunity at top events like Predictive Analytics Events 2024. These events provide priceless networking opportunities, insights, and resources for understanding the data-driven area of artificial intelligence and machine learning. For B2B executives and specialists, these events are essential for gaining a competitive edge and driving innovation in today's digital era.

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Text Analytics, Business Intelligence, Data Visualization

Top Text Analytics Software Providers That Are Powering Businesses

Article | June 21, 2024

Predictive analytics and business intelligence have become some of the most important tools for businesses because of their outstanding capabilities. Most people believe that predictive analytics is a part of business intelligence (BI), but that is not the case. If we look at the definition of business intelligence, we can argue that predictive analytics actually falls under the umbrella of BI, but that's not entirely true. While that definition is pretty much correct for both terms, if we dig down a little deeper, we will see that there are significant differences between business intelligence and predictive analytics in both practices as well as theories. Let’s drill down to understand the key differentiators between predictive analytics and business intelligence. Key Differentiators: Predictive Analytics VS BI BI seeks to answer queries like "what happens now" and "what is happening now," whereas predictive analytics tries to predict "what will happen" and provides a more practical method to assess information. Data Raw data is processed into insights for direct consumer use during the business intelligence process. With predictive analytics, unstructured data is turned into structured data that can be used to make predictions about the future. Decision Users can make decisions based on insights provided by business intelligence. Businesses can use predictive analytics to make decisions based on facts, data sets, and predictions. Purpose The objective of business intelligence tools is to equip users with information about their company's historical data performance. Predictive analytics utilizes forecasting techniques to help in the solving of complex business challenges. Methods Business intelligence uses data visualization, data mining, reporting, dashboards, OLAP, etc., with previous performance indicators. Predictive analytics predicts future occurrences and analyzes raw data patterns. Technologies Ad-hoc reporting technology, alerting technology, and other technologies are covered in business intelligence. Predictive analytics includes technologies such as predictive modeling, forecasting, etc. Use Predictive Analytics in Business Intelligence to Optimize Marketing Efforts Businesses now have a plethora of information about their customers’ and target audience's purchasing patterns and preferences, all thanks to business intelligence insights. With all of this information, predictive analytics can determine the possibility of a consumer purchasing a product, allowing businesses to target their marketing efforts on customers who are more likely to purchase their items. Businesses that employ predictive analytics and business intelligence solutions can constantly remain one step ahead of their competition. Summing Up At times, the sheer variety of tools available can be intimidating, and misinformation can sometimes hamper the selection process of technology. Business intelligence and predictive analytics are two of the most productive technologies in the market, but when combined, they can do wonders for businesses.

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Data Science

Thinking Like a Data Scientist

Article | December 23, 2020

Introduction Nowadays, everyone with some technical expertise and a data science bootcamp under their belt calls themselves a data scientist. Also, most managers don't know enough about the field to distinguish an actual data scientist from a make-believe one someone who calls themselves a data science professional today but may work as a cab driver next year. As data science is a very responsible field dealing with complex problems that require serious attention and work, the data scientist role has never been more significant. So, perhaps instead of arguing about which programming language or which all-in-one solution is the best one, we should focus on something more fundamental. More specifically, the thinking process of a data scientist. The challenges of the Data Science professional Any data science professional, regardless of his specialization, faces certain challenges in his day-to-day work. The most important of these involves decisions regarding how he goes about his work. He may have planned to use a particular model for his predictions or that model may not yield adequate performance (e.g., not high enough accuracy or too high computational cost, among other issues). What should he do then? Also, it could be that the data doesn't have a strong enough signal, and last time I checked, there wasn't a fool-proof method on any data science programming library that provided a clear-cut view on this matter. These are calls that the data scientist has to make and shoulder all the responsibility that goes with them. Why Data Science automation often fails Then there is the matter of automation of data science tasks. Although the idea sounds promising, it's probably the most challenging task in a data science pipeline. It's not unfeasible, but it takes a lot of work and a lot of expertise that's usually impossible to find in a single data scientist. Often, you need to combine the work of data engineers, software developers, data scientists, and even data modelers. Since most organizations don't have all that expertise or don't know how to manage it effectively, automation doesn't happen as they envision, resulting in a large part of the data science pipeline needing to be done manually. The Data Science mindset overall The data science mindset is the thinking process of the data scientist, the operating system of her mind. Without it, she can't do her work properly, in the large variety of circumstances she may find herself in. It's her mindset that organizes her know-how and helps her find solutions to the complex problems she encounters, whether it is wrangling data, building and testing a model or deploying the model on the cloud. This mindset is her strategy potential, the think tank within, which enables her to make the tough calls she often needs to make for the data science projects to move forward. Specific aspects of the Data Science mindset Of course, the data science mindset is more than a general thing. It involves specific components, such as specialized know-how, tools that are compatible with each other and relevant to the task at hand, a deep understanding of the methodologies used in data science work, problem-solving skills, and most importantly, communication abilities. The latter involves both the data scientist expressing himself clearly and also him understanding what the stakeholders need and expect of him. Naturally, the data science mindset also includes organizational skills (project management), the ability to work well with other professionals (even those not directly related to data science), and the ability to come up with creative approaches to the problem at hand. The Data Science process The data science process/pipeline is a distillation of data science work in a comprehensible manner. It's particularly useful for understanding the various stages of a data science project and help plan accordingly. You can view one version of it in Fig. 1 below. If the data science mindset is one's ability to navigate the data science landscape, the data science process is a map of that landscape. It's not 100% accurate but good enough to help you gain perspective if you feel overwhelmed or need to get a better grip on the bigger picture. Learning more about the topic Naturally, it's impossible to exhaust this topic in a single article (or even a series of articles). The material I've gathered on it can fill a book! If you are interested in such a book, feel free to check out the one I put together a few years back; it's called Data Science Mindset, Methodologies, and Misconceptions and it's geared both towards data scientist, data science learners, and people involved in data science work in some way (e.g. project leaders or data analysts). Check it out when you have a moment. Cheers!

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Tangent Works

Tangent Works is the brand of 2BRIDGZ solutions NV. Tangent Works is a machine learning company that bridges cutting edge academic research in the field of machine learning to challenges in the industry. Tangent Works is currently located in Ghent - Belgium and Bratislava - Slovakia. As machine learning is rapidly becoming a key asset in an interconnected data driven society, Tangent Works is building a world class team of data scientists to develop and implement cutting edge solutions in the field of predictive analytics...

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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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Big Data

Airbyte Racks Up Awards from InfoWorld, BigDATAwire, Built In; Builds Largest and Fastest-Growing User Community

Airbyte | January 30, 2024

Airbyte, creators of the leading open-source data movement infrastructure, today announced a series of accomplishments and awards reinforcing its standing as the largest and fastest-growing data movement community. With a focus on innovation, community engagement, and performance enhancement, Airbyte continues to revolutionize the way data is handled and processed across industries. “Airbyte proudly stands as the front-runner in the data movement landscape with the largest community of more than 5,000 daily users and over 125,000 deployments, with monthly data synchronizations of over 2 petabytes,” said Michel Tricot, co-founder and CEO, Airbyte. “This unparalleled growth is a testament to Airbyte's widespread adoption by users and the trust placed in its capabilities.” The Airbyte community has more than 800 code contributors and 12,000 stars on GitHub. Recently, the company held its second annual virtual conference called move(data), which attracted over 5,000 attendees. Airbyte was named an InfoWorld Technology of the Year Award finalist: Data Management – Integration (in October) for cutting-edge products that are changing how IT organizations work and how companies do business. And, at the start of this year, was named to the Built In 2024 Best Places To Work Award in San Francisco – Best Startups to Work For, recognizing the company's commitment to fostering a positive work environment, remote and flexible work opportunities, and programs for diversity, equity, and inclusion. Today, the company received the BigDATAwire Readers/Editors Choice Award – Big Data and AI Startup, which recognizes companies and products that have made a difference. Other key milestones in 2023 include the following. Availability of more than 350 data connectors, making Airbyte the platform with the most connectors in the industry. The company aims to increase that to 500 high-quality connectors supported by the end of this year. More than 2,000 custom connectors were created with the Airbyte No-Code Connector Builder, which enables data connectors to be made in minutes. Significant performance improvement with database replication speed increased by 10 times to support larger datasets. Added support for five vector databases, in addition to unstructured data sources, as the first company to build a bridge between data movement platforms and artificial intelligence (AI). Looking ahead, Airbyte will introduce data lakehouse destinations, as well as a new Publish feature to push data to API destinations. About Airbyte Airbyte is the open-source data movement infrastructure leader running in the safety of your cloud and syncing data from applications, APIs, and databases to data warehouses, lakes, and other destinations. Airbyte offers four products: Airbyte Open Source, Airbyte Self-Managed, Airbyte Cloud, and Powered by Airbyte. Airbyte was co-founded by Michel Tricot (former director of engineering and head of integrations at Liveramp and RideOS) and John Lafleur (serial entrepreneur of dev tools and B2B). The company is headquartered in San Francisco with a distributed team around the world. To learn more, visit airbyte.com.

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Big Data Management

The Modern Data Company Recognized in Gartner's Magic Quadrant for Data Integration

The Modern Data Company | January 23, 2024

The Modern Data Company, recognized for its expertise in developing and managing advanced data products, is delighted to announce its distinction as an honorable mention in Gartner's 'Magic Quadrant for Data Integration Tools,' powered by our leading product, DataOS. “This accolade underscores our commitment to productizing data and revolutionizing data management technologies. Our focus extends beyond traditional data management, guiding companies on their journey to effectively utilize data, realize tangible ROI on their data investments, and harness advanced technologies such as AI, ML, and Large Language Models (LLMs). This recognition is a testament to Modern Data’s alignment with the latest industry trends and our dedication to setting new standards in data integration and utilization.” – Srujan Akula, CEO of The Modern Data Company The inclusion in the Gartner report highlights The Modern Data Company's pivotal role in shaping the future of data integration. Our innovative approach, embodied in DataOS, enables businesses to navigate the complexities of data management, transforming data into a strategic asset. By simplifying data access and integration, we empower organizations to unlock the full potential of their data, driving insights and innovation without disruption. "Modern Data's recognition as an Honorable Mention in the Gartner MQ for Data Integration is a testament to the transformative impact their solutions have on businesses like ours. DataOS has been pivotal in allowing us to integrate multiple data sources, enabling our teams to have access to the data needed to make data driven decisions." – Emma Spight, SVP Technology, MIND 24-7 The Modern Data Company simplifies how organizations manage, access, and interact with data using its DataOS (data operating system) that unifies data silos, at scale. It provides ontology support, graph modeling, and a virtual data tier (e.g. a customer 360 model). From a technical point of view, it closes the gap from conceptual to physical data model. Users can define conceptually what they want and its software traverses and integrates data. DataOS provides a structured, repeatable approach to data integration that enhances agility and ensures high-quality outputs. This shift from traditional pipeline management to data products allows for more efficient data operations, as each 'product' is designed with a specific purpose and standardized interfaces, ensuring consistency across different uses and applications. With DataOS, businesses can expect a transformative impact on their data strategies, marked by increased efficiency and a robust framework for handling complex data ecosystems, allowing for more and faster iterations of conceptual models. About The Modern Data Company The Modern Data Company, with its flagship product DataOS, revolutionizes the creation of data products. DataOS® is engineered to build and manage comprehensive data products to foster data mesh adoption, propelling organizations towards a data-driven future. DataOS directly addresses key AI/ML and LLM challenges: ensuring quality data, scaling computational resources, and integrating seamlessly into business processes. In our commitment to provide open systems, we have created an open data developer platform specification that is gaining wide industry support.

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Big Data Management

data.world Integrates with Snowflake Data Quality Metrics to Bolster Data Trust

data.world | January 24, 2024

data.world, the data catalog platform company, today announced an integration with Snowflake, the Data Cloud company, that brings new data quality metrics and measurement capabilities to enterprises. The data.world Snowflake Collector now empowers enterprise data teams to measure data quality across their organization on-demand, unifying data quality and analytics. Customers can now achieve greater trust in their data quality and downstream analytics to support mission-critical applications, confident data-driven decision-making, and AI initiatives. Data quality remains one of the top concerns for chief data officers and a critical barrier to creating a data-driven culture. Traditionally, data quality assurance has relied on manual oversight – a process that’s tedious and fraught with inefficacy. The data.world Data Catalog Platform now delivers Snowflake data quality metrics directly to customers, streamlining quality assurance timelines and accelerating data-first initiatives. Data consumers can access contextual information in the catalog or directly within tools such as Tableau and PowerBI via Hoots – data.world’s embedded trust badges – that broadcast data health status and catalog context, bolstering transparency and trust. Additionally, teams can link certification and DataOps workflows to Snowflake's data quality metrics to automate manual workflows and quality alerts. Backed by a knowledge graph architecture, data.world provides greater insight into data quality scores via intelligence on data provenance, usage, and context – all of which support DataOps and governance workflows. “Data trust is increasingly crucial to every facet of business and data teams are struggling to verify the quality of their data, facing increased scrutiny from developers and decision-makers alike on the downstream impacts of their work, including analytics – and soon enough, AI applications,” said Jeff Hollan, Director, Product Management at Snowflake. “Our collaboration with data.world enables data teams and decision-makers to verify and trust their data’s quality to use in mission-critical applications and analytics across their business.” “High-quality data has always been a priority among enterprise data teams and decision-makers. As enterprise AI ambitions grow, the number one priority is ensuring the data powering generative AI is clean, consistent, and contextual,” said Bryon Jacob, CTO at data.world. “Alongside Snowflake, we’re taking steps to ensure data scientists, analysts, and leaders can confidently feed AI and analytics applications data that delivers high-quality insights, and supports the type of decision-making that drives their business forward.” The integration builds on the robust collaboration between data.world and Snowflake. Most recently, the companies announced an exclusive offering for joint customers, streamlining adoption timelines and offering a new attractive price point. The data.world's knowledge graph-powered data catalog already offers unique benefits for Snowflake customers, including support for Snowpark. This offering is now available to all data.world enterprise customers using the Snowflake Collector, as well as customers taking advantage of the Snowflake-only offering. To learn more about the data quality integration or the data.world data catalog platform, visit data.world. About data.world data.world is the data catalog platform built for your AI future. Its cloud-native SaaS (software-as-a-service) platform combines a consumer-grade user experience with a powerful Knowledge Graph to deliver enhanced data discovery, agile data governance, and actionable insights. data.world is a Certified B Corporation and public benefit corporation and home to the world’s largest collaborative open data community with more than two million members, including ninety percent of the Fortune 500. Our company has 76 patents and has been named one of Austin’s Best Places to Work seven years in a row.

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