Big Data 50—Companies Driving Innovation in 2019

Big Data Trailblazer by Ravi Shankar, Senior Vice President and Chief Marketing Officer

The biggest challenges with big data are not around storage, but around leveraging the data for an increasingly demanding set of use cases. These include data science, which aims to discover insightful business patterns through highly descriptive data models, as well as AI and machine learning, which have enormous potential with today’s advanced processing capabilities.

Big data implementations are often unable to adequately support such use cases, and ironically, this is due to the ease with which data can be stored; with schema-on-read capabilities, different departments can quickly and easily store data in a variety of different formats, creating silos.

Data virtualization serves as the critical big data fabric that knits together the disparate domains of a big data implementation, in real time. Rather than replicating data to a new, centralized repository, data virtualization provides real-time views into the data, no matter the format and no matter where it is physically stored. It acts as a single, unified access layer across the entire big data implementation. And because it can deliver views of the data across all of the standard interfaces, it can support myriad applications.

The award-winning Denodo Platform offers the most advanced data virtualization capabilities available for leveraging big data in the service of data science, AI, machine learning. It enables data scientists to work in R and Python in addition to SQL, and it employs machine learning to ensure that data scientists have access to the most actionable intelligence. Denodo Platform 7.0 takes this further with the integration of Zeppelin Notebook, facilitating the sharing of data models.

Denodo Technologies
denodo.com

Big Data Trailblazer by Adam Famularo, CEO

DATA VISIBILITY, GOVERNANCE & VALUE

Knowing what data you have, where it lives and where it came from is complicated. The lack of visibility and control around “data at rest” combined with “data in motion,” as well as difficulties with legacy architectures, means organizations spend more time trying to find the data they need rather than using it to produce meaningful business outcomes. As adoption for data governance grows, best-in-breed enterprises are looking for ways to use their data for competitive advantage. These organizations are evolving their data governance practices to data intelligence—connecting all the pieces of their data management and data governance lifecycles to create actionable insights.

With erwin’s approach to data governance, organizations can discover, understand, govern and socialize mission-critical information. And because many of the processes are automated, both errors and reliance on technical resources are reduced while the speed and quality of the data pipeline increases.

erwin is the only software provider with a complete, metadata-driven approach to data governance. The erwin EDGE platform integrates enterprise modeling and data intelligence suites to create an “enterprise data governance experience” so customers can:

• Understand their business, technology and data architectures and the relationships between them
• Create and automate a curated enterprise data catalog, complete with physical assets, data models, data movement, data quality and on-demand lineage
• Increase data literacy with agile, well-governed data preparation and integrated business glossaries and data dictionaries that provide business context

With erwin, you can stop wasting time on data discovery and start using it to produce real value.

erwin, Inc.
www.erwin.com

Big Data Trailblazer by Jan Aasman, CEO

AI KNOWLEDGE GRAPH SOLUTIONS: FRANZ INC.

Gartner recently identified Knowledge Graphs as a key new technology in both their Hype Cycle for Artificial Intelligence and Hype Cycle for Emerging Technologies. Using AI to create “Enterprise Knowledge” and link it across the Enterprise to create a “Knowledge Graph” is a key differentiator for companies in an increasingly competitive landscape. Semantic Graph databases, such as AllegroGraph, provide the core technology environment to enrich and contextualize the understanding of data. The ability to rapidly integrate new knowledge is the crux of the Knowledge Graph and depends entirely on Semantic Graph technologies.

ALLEGROGRAPH

AllegroGraph is a multi-model (Graph and Document) database technology that enables businesses to extract sophisticated decision insights and predictive analytics from highly complex, distributed data. AllegroGraph employs graph technologies that process data with contextual and conceptual intelligence and significantly enhances the document database model with its native support for JSON and JSON-LD. Knowledge Graphs can leverage JSON-LD to swiftly integrate with web-based applications. Organizations can therefore link specific information in their internal Knowledge Graphs (e.g., pertaining to customers or products) to web applications for timely action such as recommendations.

KNOWLEDGE GRAPH DEVELOPMENT

Franz provides a variety of services as part of its Knowledge Graph solution, from architectural consulting and technical seminars to training. If you really want to develop your corporate Knowledge Graph and address complex AI problems, you need a data system that goes beyond just data. You have to create a system that can link to anything outside your own predefined parameters—and that can learn from previous experiences. That is where a Semantic Graph database, like AllegroGraph, comes into the picture.

Franz Inc.
https://franz.com/