Orion Governance Is Proud to be Part of the MAD Landscape!

Orion Governance Featured on the 2023 MAD (Machine Learning, AI, and Data) Landscape

Thanks to Matt Turck and a small group of MAD (Machine Learning, AI, and Data) enthusiasts, the 2023 MAD Landscape was recently published. This is a monumental endeavor with the inclusion of 1,416 logos. Orion Governance is proud to be one of them.

As mentioned in Matt Turck’s “State of the Union” post regarding the 2023 MAD, it is hard to categorize some startups since their offering straddles two or more areas. Orion Governance is one of these startups. Though Orion’s Enterprise Information Intelligence Graph (EIIG) is listed under Data Governance and Catalog, it also covers Data Quality and Observability. In addition, EIIG is very much a part of the Data Mesh and Modern Data Stack (MDS) landscape. EIIG also has Data Security and Privacy capabilities.

What Makes Orion’s EIIG Stand Out

In a nutshell, EIIG is an end-to-end metadata platform, a self-defined data fabric. Powered by AI and ML, EIIG enables enterprises to automatically establish both intra- and inter-system connectivity. It connects all data assets, no matter how complex they are, within a system, whether it is a cloud based MDS or a legacy one such as AS400. At the same time, it also connects data from different systems, be it python or Java code, ETL or ELT jobs, BI reports, or the mainframe.

With this connectivity, you can visualize all your data in a federated manner. There are no more data silos since there are no more barriers between different technologies and no more chasms between different organizations (HR, Finance, Sales, subsidiaries etc.). This federated and detailed view of all your data, no matter where your data is located, is the foundation EIIG builds from bottom up.

End-to-End Data Lineage

As a result, enterprises can see end-to-end data lineage with column and code-level details. What is more, EIIG enables clients to gain visibility into what is going on in their data pipeline in near real-time with active metadata and analytics. It is like a living and breathing Google Map: real-time traffic information is at your figure tips, and issues can be alerted, and root causes are more easily identified. In other words, EIIG provides data observability near real-time and in fine granularity.

This federated view of your data can be presented in different ways in a knowledge graph. One of them is a dynamic catalog of all your data assets. AI/ML-powered term-to-asset mapping automates this otherwise very manual process. Data can be categorized according to business domains. This capability can be leveraged to enhance the Data Mesh initiative by connecting the logical layer with the physical layer, business ontology with the technical assets. At the same time, policies/rules are also mapped with the assets. Organizations are thus able to easily segment their data according to business domains to facilitate the use of the data in a governed matter. As a result, enterprise data citizens can more easily access relevant data more quickly without any manual requests in this unified, federated, governed, and secure environment.

Works for Modern Data Stack (MDS) and the Cloud

EIIG can also play a significant role in an MDS. As Matt Turck and team point out, “The MDS is now under pressure.” The two key reasons are complexity and cost. As part of this MDS mix, EIIG greatly reduces the complexity by visualizing information from the widest spectrum of technologies in a centralized knowledge graph. It optimizes cost with automation and by offering the lowest TCO in the market.

Of course, the MDS is not the only game in town. In reality, many enterprises have adopted a hybrid cloud approach, with data moving within and between systems on premises and on cloud. EIIG enables total transparency of all these data movements. In addition, it offers active metadata and analytics capabilities to enhance governance, facilitate migration, and empower data citizens.

Real-Time Analysis and Trust Propagation

One of such features is automated, accurate, and near real-time impact analysis, which gives a central point of control and removes fear of changes. EIIG is used to identify the dependencies between datasets and executables that are impacted by a proposed change. It enables small, reliable, and frequent changes that are essential to application development, data migration and compliance control.

Another example is EIIG’s ability to augment data quality and propagate trust. By incorporating data quality metrics, EIIG can track data quality throughout the entire data pipeline, before and after transformation, all the way to the end point. By the same token, data users can see the trust score of the data they are using. That helps them determine what data they may use in their projects or in the reports.

Data privacy/security and governance are usually two sides of the same coin. With EIIG enterprises can quickly identify sensitive data assets such as PII information and tag them with one click. EIIG also offers proactive governance of access control, including remediation of sensitive data exposure and enhanced security by detecting and destroying non-compliant infrastructure components built in the cloud to prevent exposure. This ensures that all cloud deployments comply with central business policies and reduce data exposure risks which is key for the next generation data governance solution.

In summary, EIIG is one stone that kills several birds, covering several disciplines in the MAD landscape. It is a self-defined data fabric that provides traceability of data on the DNA level and allows enterprises to use the same data from different perspectives for different personas. With EIIG, enterprises can truly know their data and know how to best unlock its value quickly. For more information about Orion Governance and the 2023 MAD Landscape contact Orion today.

About the Author: Niu Bai, Ph.D. is the Head of Global Business Development at Orion Governance, Inc. Connect with Niu on LinkedIn.