DataWalk Graph Analytics
Take full advantage of graph analytics, without building new graph technology competencies in your organization.
Analyze Data And Relationships
A foundational DataWalk technology is an innovative graph/relational database hybrid. Data and connections are stored, which enables high performance for complex querying and analytics. The graph structure enables management and analysis of complex data; the ability to derive value from connections as well as values; analysis of the relationships between individual objects on a link chart, and more efficient processes for machine learning.
Fuse, Model, And Visualize Your Complex Data Via A Knowledge Graph
The DataWalk Universe Viewer is a knowledge graph that represents all of an organization’s data, and the relationships/connections between that data. The Universe Viewer is architected to support the scale and complexity of enterprise deployments, while organizing data around easily understood business objects. The Universe Viewer embodies a highly flexible logical data model, enabling new data sources and connections to easily be added or modified.
Answer Complex Business Questions Through The Knowledge Graph
Being able to formulate and answer complex business questions is a crucial DataWalk graph analytics capability. DataWalk uniquely meets the challenge for big data query tools, enabling you to quickly generate complex no-code queries that quickly complete, even across vast amounts of data. The knowledge graph query facility enables visual exploration and interaction with graph data structures allowing insights to be found without the need for any query language. Unlike alternative solutions built around joining multiple tables, with the DataWalk knowledge graph you walk through business data sets using persistent and available links.
Instantly Identify Distant Connections and Organized Crime Groups
DataWalk graph algorithms enable you to perform various relationship-based analyses across all your data at the push of a button. For example, you can automatically find the shortest paths between distant entities, or automatically identify clusters that have the characteristics of organized crime groups or other networks of interest. Without these algorithms, deriving such results could otherwise take days or months.
Efficient Artificial Intelligence (AI)
DataWalk graph analytics improves the process for creating efficient machine learning (ML), explainable artificial intelligence (xAI), and other types of AI solutions. Graph analysis enables data scientists and data engineers to create a richer set of features and training sets based on relationships between data points. Embedded AutoML and xAI engines enable data scientists and analysts to automate the generation and explanation of machine learning models. All ML models can be developed with real data and then be instantly deployed in production without re-coding. With DataWalk machine learning, data scientists, data engineers, and developers can accelerate the deployment cycle and quickly get to production results.
No Need To Develop New Graph Competencies
DataWalk provides ad-hoc, no-code facilities for both querying and graph algorithms, such that users do not need to learn a new programming language or otherwise develop new expertise in graph technology. This addresses one of the key inhibitors to organizations that want to utilize graph, but cannot support a significant re-training effort.