The Visionary Entrepreneurs Driving the Big Data Revolution

In the age of big data, entrepreneurs and innovators are harnessing the power of advanced analytics, data science, and machine learning to transform industries and tackle some of the world‘s greatest challenges. At the forefront of this revolution are a cadre of visionary founders who have built groundbreaking companies that are shaping the future of how data is leveraged to drive business value and societal progress.

The Godfathers of Business Analytics

Long before "data scientist" became the "sexiest job of the 21st century" in the words of the Harvard Business Review, SAS co-founders Jim Goodnight and John Sall were pioneering the use of statistical analysis software for business applications. Since launching SAS in 1976, Goodnight and Sall have grown the company into an analytics behemoth with $3.1 billion in revenue in 2018, 14,000 employees, and customers at more than 83,000 sites in 147 countries.

SAS‘s success has stemmed from its relentless focus on developing advanced analytics tools that can solve real-world business problems across industries like banking, healthcare, retail, and government. The company has been at the forefront of innovation in areas like data mining, machine learning, text analytics, and data visualization. Its flagship product, SAS 9, provides an integrated environment for predictive modeling, data processing, and business intelligence.

Reflecting on SAS‘s enduring leadership in analytics at the company‘s 2015 Global Forum, Goodnight emphasized the importance of staying at the cutting edge: "We‘ve been in the big data business for 39 years. My worry is not that we have new competition. My worry is that we won‘t be able to continually reinvent ourselves to address the new and changing needs of our customers."

Bringing Data Storytelling to the Masses

As big data has gone mainstream, one of the biggest challenges has been enabling non-technical users to explore and derive insights from the deluge of information. Tableau Software, co-founded by Christian Chabot, has been a pioneer in the democratization of data through its intuitive data visualization software. Tableau‘s drag-and-drop interface allows users to easily connect to disparate data sources, create interactive dashboards, and share insights across their organizations.

By focusing on the user experience and abstracting away the complexity of the underlying data and computations, Tableau has empowered business users to ask and answer questions of their data without needing to rely on IT or data experts. The company‘s platform has found fans in organizations of all sizes, from small nonprofits to large enterprises like Bank of America, Deloitte, and Allstate.

Tableau‘s vision of "analytics for everyone" has proven prescient as self-service BI and visual data discovery have become key priorities for businesses seeking to build data-driven cultures. The company, which went public in 2013, generated $985.6 million in revenue in 2018 and continues to lead the pack in the intensely competitive business intelligence market.

Chabot, who served as Tableau‘s CEO from 2003 to 2016, has been a passionate evangelist for the power of data visualization to drive better decision-making. In a 2014 interview with McKinsey, he argued: "The core skill that people need in order to be successful with big data is not the ability to write code, but the ability to achieve insights from the data, to see the patterns and the outliers, and to make good inferences and decisions from those insights."

The Dawn of the Big Data Platform

While Tableau and other BI vendors have focused on the "last mile" of data analytics, entrepreneurs have also been building the foundational technologies to enable big data processing and storage at massive scale. Perhaps no company has been more synonymous with the rise of big data than Hadoop pioneer Cloudera.

Founded in 2008 by Christophe Bisciglia, Amr Awadallah, and Jeff Hammerbacher, Cloudera was the first company to commercialize Apache Hadoop, the open source framework for distributed processing of large datasets across clusters of commodity servers. Hadoop emerged from the early work of Google on its MapReduce programming model and Google File System (GFS), which were designed to enable the search giant to index and analyze the vast amounts of data on the web.

Cloudera‘s distribution of Hadoop became the de facto standard for enterprises looking to build scalable data processing infrastructure, offering features like security, governance, and management on top of the open source core. The company‘s customer roster includes over half of the Fortune 100, across sectors like financial services, telecom, healthcare, and government.

As Hadoop has evolved and spawned an ecosystem of related projects like Spark, Hive, and HBase, Cloudera has expanded its offerings into a full-fledged data platform for machine learning and AI applications. In 2018, the company merged with its chief rival Hortonworks in a $5.2 billion deal that cemented its position as the leader in the Hadoop market.

Reflecting on the merger, Cloudera CEO Tom Reilly stated: "Our businesses are highly complementary and strategic. By bringing together Hortonworks‘ investments in end-to-end data management with Cloudera‘s investments in data warehousing and machine learning, we will deliver the industry‘s first enterprise data cloud from the Edge to AI."

Mining Insights from Complex Data

While Hadoop has become a cornerstone of the big data landscape, entrepreneurs have also been applying machine learning and advanced analytics to a wide range of domain-specific data challenges. One of the most prominent players in this space is Palantir Technologies, the secretive Silicon Valley company founded by Alex Karp and Peter Thiel.

Palantir‘s software is designed to help organizations in fields like defense, intelligence, and law enforcement to integrate, visualize, and analyze vast amounts of disparate data to detect patterns, uncover insights, and support decision-making. The company‘s tools have been used for purposes ranging from tracking down terrorists to identifying fraudulent activity in financial markets.

At the core of Palantir‘s technology stack is its Gotham platform, which provides a suite of data integration, analysis, and collaboration tools for working with complex, sensitive data. The platform‘s key innovation is its use of "human-machine symbiosis", which involves a tight feedback loop between algorithms and human analysts to iteratively refine models and surface actionable insights.

Palantir‘s approach has proven highly effective but also controversial, given the sensitive nature of much of the data it works with and its close ties to government agencies. Karp has been an outspoken defender of the company‘s mission, arguing in a 2019 interview with The Telegraph: "Our central purpose is to do the integration work that allows the entire corpus of data to be in one place and enables men and women in the intelligence community, and in the military, to ask the questions they need to ask to keep us safe."

The Rise of Data Science Education

As the demand for data science skills has exploded, a number of entrepreneurs have built businesses aimed at closing the talent gap through education and training. One of the pioneers in this space is Daphne Koller, the co-founder of online education provider Coursera.

Koller, a computer science professor at Stanford, co-founded Coursera with her colleague Andrew Ng in 2012 with the goal of making high-quality education accessible to anyone, anywhere. The company offers online courses, specializations, and degrees in partnership with top universities and companies, serving over 40 million learners worldwide.

Data science and AI have been a key focus area for Coursera from the start, with popular offerings like Ng‘s "Machine Learning" course attracting over 2 million enrollments. In 2017, the company launched a series of Master‘s degrees in data science, machine learning, and analytics in partnership with leading institutions like the University of Michigan, Imperial College London, and HSE University.

Koller sees Coursera as playing a critical role in preparing the workforce for the jobs of the future, particularly in domains like data science where the skills landscape is rapidly evolving. "We are at a critical moment in the development of the technologies that will shape the future," she said in a 2018 interview with VentureBeat. "It is our responsibility to ensure that these technologies are built and used in a way that benefits society as a whole."

Building the Data-Driven Enterprise

As data becomes an increasingly critical asset for businesses across industries, entrepreneurs are building an array of tools and platforms to help companies leverage their data for competitive advantage. One of the most ambitious players in this space is enterprise AI company C3.ai, founded by Thomas Siebel.

Siebel, who previously founded CRM giant Siebel Systems (acquired by Oracle for $5.8 billion in 2006), launched C3.ai in 2009 with the goal of providing an end-to-end platform for developing and deploying enterprise AI applications at scale. The company‘s technology stack includes tools for data integration, machine learning, predictive analytics, and IoT, as well as pre-built applications for specific industry verticals.

C3.ai has raised over $367 million in funding from investors like TPG, Breyer Capital, and Sutter Hill Ventures, and counts major enterprises like Shell, 3M, and the U.S. Air Force among its customers. The company has been at the forefront of evangelizing the concept of "digital transformation" through AI, with Siebel arguing that businesses that fail to embrace data-driven decision-making and automation will be left behind.

In a 2019 interview with MIT Technology Review, Siebel laid out his vision for the future of enterprise AI: "The companies that will be successful in the 21st century will be those that are able to harness the power of big data, to develop predictive analytics, to apply AI and machine learning to every business process, to every decision, to every customer interaction, in ways that dramatically increase productivity and drive competitive advantage."

Navigating the Challenges of the Data Economy

As the big data landscape continues to evolve, entrepreneurs will need to navigate a range of technical, ethical, and regulatory challenges. On the technical front, the sheer scale and complexity of data being generated by businesses and consumers alike will require ongoing innovation in areas like data processing, storage, and analytics.

At the same time, concerns around data privacy, security, and algorithmic bias are increasingly in the spotlight, with high-profile scandals like the Facebook-Cambridge Analytica affair underscoring the risks of unchecked data collection and use. Entrepreneurs will need to be proactive in building safeguards and best practices around data governance and ethics into their products and business models.

The regulatory landscape is also shifting, with laws like the European Union‘s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) imposing new obligations on companies that collect and process personal data. Compliance with these and other emerging data protection frameworks will be a key priority for startups and established players alike in the years ahead.

Despite these challenges, the opportunities in the big data space remain vast and largely untapped. As data becomes an increasingly central driver of business value and societal progress, entrepreneurs who can harness its power while navigating its pitfalls will be well-positioned to build the next generation of transformative companies. By standing on the shoulders of the data science pioneers profiled here and learning from their examples, the data entrepreneurs of tomorrow can help shape a future in which the benefits of big data are shared by all.