Alex Solutions update

Written By:
Published:
Content Copyright © 2023 Bloor. All Rights Reserved.
Also posted on: Bloor blogs

Alex Solutions update banner

Alex Solutions is a data governance vendor founded in 2016 in Australia but with over fifty customers across the USA, Europe and Asia Pacific. It has a comprehensive data catalog and supporting features that compete directly against products such as Collibra and Alation.

With a data catalog at its heart, Alex has built an extensive range of over 85 connectors to assorted sources of corporate metadata, both structured and unstructured. It can read the catalogs of data warehouse and data lake products like Snowflake and Databricks as well as transaction systems like SAP, file systems and even data movement tools like Informatica. The product is cloud-native, running on either AWS, Azure or GCP,  but can access systems in the cloud or on-premise. The technology has a business glossary, data profiling, data lineage tracking as well as features to support compliance with legislation such as banking regulations.

Once the catalog is populated, business users see a highly configurable interface that allows various ways to visualise the data landscape that has been mapped out. There are knowledge graphs, data lineage flows, impact analysis charts and more. Business users can explore the flow of data through these graphical tools, or use keyword searches to identify, for example, all the data flows where a business term like “sales” is used. The support for tracing data lineage is unusually sophisticated and intuitive. The highly intuitive user interface shows up prominently in customer testimonials that I examined, with one describing Alex as being a “Bentley for the price of a Ford”. Some of the very elaborate graphical relationships that the product can display is helped by its underlying use of a graphical database that is suited to handling complex relationships.

The rise in interest in generative AI may spur further interest in the subjects of data governance and data quality, since if a corporation wants to train a large language model on their own corporate data then that data itself needs to be reasonably accurate and trustworthy. Alex itself is being cautious in its deployment of this technology within the product, trying it out in various possible application areas such as generating supplemental business description content, and doubtless future releases of the product will reveal more in this regard.

One thing that Alex seems particularly good at is the depiction and navigation of extremely complex data landscapes, such as those found in the major banks that formed its earliest customers. Its strength in data lineage and impact analysis should appeal to any large organisation that wants to govern and manage its data better. Indeed, it is telling that some of its customers have been large corporations that have tried to implement data governance solutions from very large vendors and found these wanting in a number of areas in practice. Such customer success stories and more active marketing will be important in helping this vendor grow from its current customer base to a wider audience.