Red Hat: Leading the Charge

Valuation: $30 Billion

Red Hat, founded in 1993 and headquartered in Raleigh, North Carolina, stands as a giant in the open-source software realm for businesses. At its core, Red Hat champions the concept of community-driven development, providing professional quality assurance and subscription-based customer support. Their flagship product, Red Hat Enterprise Linux, has become synonymous with reliability and performance.

Red Hat’s revenue streams primarily flow from subscriptions for customer services, training, and integration services, enabling enterprises to harness the full potential of open-source software. Notably, Red Hat actively contributes to numerous free software projects, exemplifying its commitment to the open-source community. The company’s significance is underscored by IBM’s historic acquisition announcement in 2018, valuing Red Hat at a staggering $34 billion.

MuleSoft: Bridging the Divide

Valuation: $6.5 Billion

San Francisco-based MuleSoft has carved a niche for itself by providing an open-source integration platform that facilitates seamless data, application, and device connectivity across on-premises and cloud environments. Their flagship product, Anypoint Platform, empowers businesses to integrate a wide spectrum of software, including SaaS applications, legacy systems, and more.

Comprising components like Anypoint Design Center, Anypoint Exchange, and Anypoint Management Center, MuleSoft’s suite offers a holistic approach to API development, sharing, and management. In 2018, Salesforce recognized MuleSoft’s potential and acquired the company for $6.5 billion, further solidifying the open source’s place in the tech industry.

Databricks: Unifying Data Excellence

Valuation: $6 Billion

Databricks champions data analytics with its unified platform, driven by Apache Spark, enabling the convergence of data science, engineering, and business operations. This cloud-based solution offers support for a range of programming languages and data science frameworks, including Python, Scala, R, and more.

Databricks’ impact extends to industries and organizations seeking to harness the power of data, making it an indispensable player in the open-source ecosystem.

Elastic NV: Search Beyond Imagination

Valuation: $5 Billion

Elastic NV is synonymous with search solutions, offering self-managed and SaaS products tailored for various use cases, including search, logging, security, and analytics. Elasticsearch, a key offering from Elastic NV, has transformed the landscape of search engines with its distributed, full-text search capabilities.

Notable users like eBay, Wikipedia, and Netflix rely on Elastic’s technology for their search needs. The Elasticsearch community boasts over 100,000 members, reflecting the widespread adoption of Elastic’s open-source search technology.

Confluent: Real-time Insights with Apache Kafka

Valuation: $2.5 Billion

Confluent, an American big data company, has harnessed the potential of the open-source Apache Kafka, a real-time messaging technology. By providing Stream Analytics, Confluent enables users to access critical business intelligence insights in real time.

Kafka, which originated at LinkedIn in 2010, has evolved into a central repository for streams of data, making it invaluable in data-intensive environments. Confluent’s contributions to the Kafka ecosystem, including a managed cloud version and Confluent Hub marketplace, have enriched the open-source community.

HashiCorp: Orchestrating the Cloud

Valuation: $2 Billion

Founded in 2012, HashiCorp has established itself as a software company with a freemium open-source business model. HashiCorp’s suite of open-source tools aids developers, operators, and security personnel in provisioning, securing, and managing cloud-computing infrastructure efficiently.

These tools, characterized by a plugin-oriented architecture, seamlessly integrate with third-party technologies and services, empowering enterprises to scale and automate their operations.

In conclusion, the ascent of open source unicorns in the realms of analytics and real-time business intelligence showcases the transformative power of collaborative, community-driven development. These companies have not only disrupted traditional business models but have also fostered innovation and growth on an unprecedented scale. As the open-source movement continues to gain momentum, we can anticipate even more groundbreaking success stories on the horizon.

The post The Open Source Revolution: Unraveling the Success of Analytics and Business Intelligence Unicorns first appeared on AITechTrend.

As businesses and organizations continue to rely on data to drive decision-making, two terms that often come up are “analytics” and “statistics.” While both are related to data analysis, they have distinct differences in their approach, scope, and applications. In this article, we will delve into the disparities between analytics and statistics, exploring their definitions, types, applications, and key differences.

Introduction

In today’s data-driven world, businesses and organizations are constantly seeking insights from data to gain a competitive edge. Analytics and statistics are two methodologies that help in extracting valuable information from data, but they have different approaches and purposes. Understanding the differences between analytics and statistics can help businesses and data professionals make informed decisions about which approach to use for different data analysis tasks.

Definition of Analytics

Analytics is the process of examining, cleaning, transforming, and modeling data to extract insights, draw conclusions, and support decision-making. It involves the use of various tools, techniques, and algorithms to analyze data and discover patterns, trends, and relationships. Analytics can be descriptive, diagnostic, predictive, or prescriptive, depending on the type of analysis being performed. Descriptive analytics focuses on summarizing and understanding historical data, diagnostic analytics aims to identify the causes of past events, predictive analytics uses statistical models to forecast future outcomes, and prescriptive analytics suggests optimal solutions based on available data.

Types of Analytics

There are several types of analytics, each serving a specific purpose. Some common types of analytics include:

1. Descriptive Analytics: Descriptive analytics involves analyzing historical data to gain insights into past performance and understand trends and patterns. It answers questions such as “What happened?” and “Why did it happen?”
2. Diagnostic Analytics: Diagnostic analytics aims to identify the causes of past events or problems by analyzing data. It answers questions such as “Why did it happen?” and “What were the factors that led to it?”
3. Predictive Analytics: Predictive analytics uses statistical models to forecast future outcomes based on historical data. It answers questions such as “What is likely to happen in the future?” and “What are the possible outcomes of a particular decision or action?”
4. Prescriptive Analytics: Prescriptive analytics suggests optimal solutions based on available data and helps in decision-making. It answers questions such as “What should be done?” and “What actions should be taken to achieve a particular goal?”

Importance of Analytics

Analytics is crucial for businesses and organizations as it enables data-driven decision-making, identifies opportunities, uncovers insights, and improves performance. By analyzing data, businesses can gain a deeper understanding of their customers, markets, and operations, optimize resources, mitigate risks, and gain a competitive advantage. Analytics is widely used in various industries, including finance, healthcare, marketing, sports, and e-commerce, to name a few.

Definition of Statistics

Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It involves the use of mathematical techniques to summarize and analyze data, make inferences, and draw conclusions. Statistics can be descriptive or inferential, depending on the type of analysis being conducted. Descriptive statistics focuses on summarizing and describing data, while inferential statistics involves making inferences and drawing conclusions about a population based on a sample.

Types of Statistics

Statistics can be broadly categorized into two main types: descriptive statistics and inferential statistics.

1. Descriptive Statistics: Descriptive statistics involves the analysis and summary of data to describe its main features, such as measures of central tendency (e.g., mean, median, mode), measures of dispersion (e.g., range, variance, standard deviation), and graphical representations (e.g., histograms, bar charts, pie charts). Descriptive statistics provide a snapshot of the data and help in understanding its characteristics and trends.
2. Inferential Statistics: Inferential statistics involves making inferences and drawing conclusions about a population based on a sample. It uses probability theory and statistical techniques such as hypothesis testing, confidence intervals, and regression analysis to make predictions, estimate parameters, and test hypotheses. Inferential statistics allows researchers to make generalizations about a population based on a smaller subset of data.

Applications of Statistics

Statistics has a wide range of applications in various fields, including business, healthcare, social sciences, sports, and more. Some common applications of statistics include:

1. Business: Statistics is used in business to analyze sales data, customer behavior, market trends, and financial performance. It helps in making data-driven decisions, optimizing resources, and predicting future outcomes.
2. Healthcare: Statistics is used in healthcare to analyze patient data, conduct clinical trials, and study disease patterns. It helps in evaluating treatment effectiveness, identifying risk factors, and making evidence-based decisions.
3. Social Sciences: Statistics is used in social sciences to conduct surveys, analyze social data, and study human behavior. It helps in understanding social trends, measuring attitudes, and making policy decisions.
4. Sports: Statistics is used in sports to analyze player performance, game outcomes, and team dynamics. It helps in strategizing, scouting, and evaluating player effectiveness.

Key Differences between Analytics and Statistics

While analytics and statistics share similarities in terms of data analysis, they have key differences in their approach, scope, and applications.

1. Approach: Analytics focuses on examining data to gain insights, draw conclusions, and support decision-making. It involves the use of various tools, techniques, and algorithms to analyze data and discover patterns, trends, and relationships. On the other hand, statistics involves the collection, analysis, interpretation, presentation, and organization of data using mathematical techniques to summarize and analyze data, make inferences, and draw conclusions.
2. Scope: Analytics is a broader field that encompasses various types of data analysis, such as descriptive, diagnostic, predictive, and prescriptive analytics. It can involve both qualitative and quantitative data and can be applied to various domains, including business, healthcare, marketing, and more. Statistics, on the other hand, is a branch of mathematics that focuses on the analysis of data using statistical techniques and is primarily concerned with quantitative data.
3. Applications: Analytics is widely used in business, finance, healthcare, marketing, and other industries for decision-making, optimization, and gaining a competitive advantage. It is also used in fields such as sports, e-commerce, and social sciences, among others. On the other hand, statistics has applications in various fields, including business, healthcare, social sciences, sports, and more, and is used for data analysis, making inferences, and drawing conclusions.
4. Emphasis: Analytics emphasizes the use of advanced tools, techniques, and algorithms to analyze data and discover insights. It often involves the use of machine learning, artificial intelligence, and big data technologies to process and analyze large volumes of data. Statistics, on the other hand, emphasizes the use of mathematical techniques, probability theory, and statistical methods to analyze data and draw conclusions. It focuses on the mathematical foundations of data analysis and inference.

Conclusion

In conclusion, while analytics and statistics share similarities in terms of data analysis, they have key differences in their approach, scope, and applications. Analytics focuses on using various tools and techniques to gain insights and support decision-making, while statistics involves the use of mathematical techniques to analyze data and make inferences. Both fields have wide-ranging applications in various industries and fields, including business, healthcare, social sciences, sports, and more. Understanding the differences between analytics and statistics is essential for professionals working in data-driven fields to effectively analyze data, draw meaningful conclusions, and make informed decisions.

The post Beyond Numbers: Key Differences Between Analytics and Statistics first appeared on AITechTrend.

Neo4j^®, the world’s leading graph data platform, crossed $100 million in annual recurring revenue (ARR) during 2021. The year was marked by strategic product innovation that drove customer and partner excellence, strong community engagement, and super-sized venture funding investments.

“Neo4j has pioneered the graph space for a number of years, with critical deployments among major credit card firms for fraud detection, as well as use cases in areas driven by the pandemic, including product testing and supply chain analysis,” said Carl Olofson, Research Vice President at IDC.

Neo4j continued to grow in popularity throughout 2021 as the world’s most widely deployed graph database, maintaining its position as a top 20 database overall. Momentum drivers include the accelerated adoption of Neo4j AuraDB, a fully managed service that reduces friction as complex applications shift to the cloud, as well as the success of Neo4j Graph Data Science, a complete toolset for data scientists to apply graph algorithms for more effective machine learning and better predictions.

Over 1,000 organizations depend on Neo4j for mission-critical applications, and many thousands more experiment, prototype, and deploy Neo4j’s expanding portfolio of cloud services. Notable customers include Pfizer, PepsiCo, Inc., World Health Organization (WHO), Cable News Network, Inc. (CNN), and BMW Group.

Neo4j’s success in helping customers across industries such as Financial Services, Retail, and Healthcare caught the attention of investors, leading to $390 million in new investments raised in 2021, and launching Neo4j to a $2 billion valuation. On top of being the largest single funding round to date in the database space, Neo4j also welcomed GV (formerly Google Ventures) as a strategic investor and added former Google CFO, Patrick Pichette, to its board to offer increased industry expertise for the next phase of growth.

Patrick Pichette, Inovia Capital Partner and Neo4j Board Member, touched upon Neo4j’s momentum over the past year.

“2021 marked an incredible year for Neo4j and graph technology at large,” said Pichette. “What really sets Neo4j’s graph technology apart is that it uniquely solves some of the world’s most complex challenges. Neo4j is poised for strong, consistent growth leading into 2022, and we’re excited to be part of that journey.”

Emil Eifrem, CEO and Co-Founder of Neo4j, reflected on the past year and leading one of only a handful of private database companies to cross $100 million in ARR.

“In 2021, we demonstrated that Neo4j is a mainstay of modern data infrastructure, grounded in a global community of developers and data scientists, empowered with a rich portfolio of technology to address complex challenges, and scale without barriers,” said Eifrem. “We enter 2022 with the wind at our backs, and the right talent and leadership in place. We’re poised to deliver Neo4j to a fast-growing user base, and continue to delight our customers as their use cases become more exacting.”

The company ended 2021 with over 600 employees, representing the largest collective of graph expertise in the world. During the course of the year, Neo4j expanded rapidly in Asia-Pacific (Shanghai, Singapore, Sydney, Jakarta, and Bangalore), and Latin America (São Paulo).

Notable Neo4j 2021 milestones include:

Technology Leadership

• Breaking the Graph Scale Barrier: As part of NODES 2021, Neo4j demonstrated its super-scaling technology to show real-time query performance against a graph with over 200 billion nodes and more than a trillion relationships, running on over one thousand machines.
• Graphs and AI: Neo4j Graph Data Science was adopted by over 50 customers to build sophisticated AI, machine learning, and advanced analytics applications.
• AuraDB Enterprise: The most deployed and trusted graph technology platform was made generally available as a fully managed service, helping organizations including Levi Strauss & Co. and Adeo to radically accelerate time to value and get to production faster.
• Knowledge Graphs Accelerate Adoption: Two-thirds of Neo4j customers – including NASA – are implementing knowledge graphs to redefine what’s possible in data management and analytics.

Demonstrable Customer Value

• Unsurpassed ROI: The Neo4j Graph Data Platform pays for itself more than 4x in the span of three years (417% ROI), according to a recent Forrester TEI report.
• Accelerated Time to Value: According to Forrester, Neo4j showed 60% accelerated time to value, as average development time shrunk from 12 months to four.
• Digital Transformation: The TEI study was based on Forrester’s in-depth interviews with Neo4j customers who realized substantial cost savings from IT modernization and rationalization.

Commercial Impact

• Neo4j on Azure, GCP, and AWS: Neo4j is now globally available on Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS) marketplaces. Customers can now seamlessly deploy Neo4j on the cloud platform of their choice.
• New Executives and Board Members: Neo4j welcomed Kristin Thornby as Chief People Officer. Nathalie Kornhoff-Bruls of Eurazeo and Patrick Pichette of Inovia Capital both joined Neo4j’s board.
• Partner Traction: Neo4j trained and certified over 1,000 graph practitioners from leading global system integrators including Accenture, Deloitte, EY, Capgemini, and PwC, in addition to closing new business with nine U.S. Federal Programs. The company expanded its partner leadership in emerging markets including Brazil, China, India, and Australia.

Market Expansion

• Top 20 Database: Neo4j is the leading graph database and ranks among the 20 most popular databases globally, as tracked by DB-Engines, an independent ranking of all database management systems.
• Award Wins: Neo4j was named one of CRN’s Big Data 100 Coolest Database System Companies, 2021 Data Breakthrough Awards Graph DB Solution of the Year, and 2021 Datanami Readers’ and Editors’ Choice Awards for Best Data and AI Technology.
• Rise in Cybersecurity Graph Adoption: Companies like Deloitte and Uplevel Security increasingly sought to leverage graph databases to strengthen cybersecurity strategies in response to increasingly sophisticated threats. Graphs equip cybersecurity teams with the competitive advantage of rapid screening, improved turnaround time, quality of recommendations, and reduced false positives, ultimately allowing them to mitigate cyber threats.
• Inclusion in U.S. Department of Defense (DoD) Iron Bank: Neo4j collaborated with The DoD’s Platform One organization to develop a hardened container for its graph database technology and joins a repository of digitally signed, binary container images that have been hardened according to the Iron Bank’s Container Hardening Guide.

Community Engagement

• Growing Developer Base: The global Neo4j community surpassed 240,000 members over the last year. During 2021, developers downloaded Neo4j more than 36 million times and launched more than 150,000 Neo4j Sandbox instances. Upwards of 53,000 professionals list Neo4j as a skill on their LinkedIn profiles.
• The Pandora Papers: The International Consortium of Investigative Journalists (ICIJ) released the Pandora Papers, which used Neo4j to generate visualizations and make searchable records of the hidden riches of world leaders. Neo4j has been working with the ICIJ since the 2016 Panama Papers investigation.
• Graphs4Good: The efforts of the Neo4j community to collaborate and help fight against the spread of COVID-19 were recognized by two honorable mentions in the AI and Data and Software categories of Fast Company’s 2021 World Changing Ideas Awards.
• Largest Graph Event: Neo4j Online Developer Expo and Summit (NODES 2021) welcomed over 12,000 registrants to listen to presentations from Fujitsu Research Labs, Dataiku, BASF, Apiax, Linkurious, and more.
• 2021 Graphie Award Winners: This year’s nominations eclipsed all prior years, with Neo4j receiving nominations spanning more than 10 countries and awarding 27 winners including Pfizer, Qualicorp S.A., Commonwealth Bank of Australia, Lenovo, Volvo Cars, Levi Strauss & Co., and many more.

 Resources

• Neo4j ROI
• Neo4j Products
• Neo4j Customers
• Neo4j Community
• Neo4j on Twitter
• Neo4j on LinkedIn
• Neo4j on YouTube
• Neo4j is Hiring

About Neo4j
Neo4j is the world’s leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cyber crime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization and more. Find out more at neo4j.com and follow us at @Neo4j.

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Enterprise analytics solutions can be stand-alone information systems or can be provided with solutions for data mining, business intelligence, web analytics, and big data analytics.

What is Enterprise Analytics?

A unified, integrated set of decision-making tools and processes that addresses the entire business problem, rather than just one particular area, and employs the use of the complete spectrum of human and automated capabilities to maximize the performance of all decisions that affect the business.

The enterprise analytics market has been segmented into end-user, component, application, and vertical. Based on end-user, the market is segmented into media and entertainment, BFSI, retail and consumer goods, manufacturing, hospitality, and gaming. Similarly, based on components, the market is segmented into software and service.

Why is Enterprise Analytics Important?

Today, your business’s entire lifecycle is growing exponentially and there’s also been an explosion of the volume of data in organizations, ranging from desktop to the server room to the different places in between. There are various factors that are contributing to the growth of the amount of data that is currently being generated in organizations, the major one being the growth of the Internet and the growing usage of smartphones. With this large amount of data coming from various sources, it is a challenge for organizations to deal with it efficiently. Enterprise Analytics enables an organization to conduct real-time analysis of this data. In other words, enterprises can generate actionable insights from this huge amount of data.

The Importance of Data Quality in Enterprise Analytics

Data quality is a critical concern when an organization is using big data analytics software and deploying applications. One of the main concerns when dealing with big data is to guarantee the accuracy of the data. Thus, there are three major aspects of data quality. First, the data should be accurate or near accurate. This means that a particular data value should be predicted based on an associated prediction metric. The accuracy or precision of prediction depends upon the performance of the prediction metric. Second, the accuracy or precision of the prediction depends upon the quality of the prediction metric. The performance of the prediction metric should be good enough to maximize predictive power.

Benefits of Data Analytics in Business

Conclusion

The rise in popularity of various technologies and their increasing affordability has allowed enterprises to focus more on their business goals and not on the information technology (IT) infrastructure. With this knowledge in hand, IT professionals can easily identify which particular technology to invest in for improving their organization’s performance. Enterprise analytics is one such technological development. This is not to say that the companies that are lagging in analytics implementations are unable to offer a complete spectrum of software and services, but they certainly lack in their ability to leverage their data to derive value from their business processes and operations.

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“As the convergence of technologies is changing the global technology landscape and evolving business strategies, we are seeing companies of all sizes and in all industries start to embrace digital transformation,” said James R. Scapa, founder and chief executive officer, Altair. “Future.AI is the ideal event for business leaders to gain inspiration, insights, and best practices that can be applied to their organizations, regardless of where they fall in their digital transformation journey.”

The global event will connect scientists, engineers, business teams, and creative thinkers who are harnessing the power of data analytics and AI to gain competitive advantages and drive better business results. Attendees will be empowered to discover their data potential and learn from those who are operationalizing data analytics and AI to compete more effectively. Future.AI will include insightful keynotes from thought leaders, panels of experts addressing the latest trends, and more, including:

• Keynote presentation from Dr. Anima Anandkumar, director of machine learning research, NVIDIA
• “AI Takes to the Cloud” panel featuring Intel, Google, Microsoft, NVIDIA, and Oracle
• “AI and Digital Transformation: Paving a Path to Better Business Outcomes,” fireside chat with Capgemini and Sam Mahalingam, Altair chief technical officer
• “Women in Data Analytics” panel will explore challenges and opportunities for women in a male-dominated field
• Industry-specific breakout sessions featuring Rolls-Royce, HSBC, Jaguar Land Rover, FlexTrade, KLA, BreakForth, Mabe, and Meyers Constructors

To learn more and to register for Future.AI, visit https://web.altair.com/future-ai-2021.

The post Altair Future.AI Global Event to Demonstrate How Artificial Intelligence and Analytics Accelerate Digital Transformation first appeared on AITechTrend.