Ataccama

Ataccama provides a data catalogue with active metadata support as described earlier. It uses this to ingest metadata from source systems and can manage data pipelines. Its data quality firewall ensures that all data that it deals with is subject to data quality rules that have been established. Its master data management capability means that data records that may be duplicated in source systems are matched and merged into a golden record based on business rules and survivorship rules applied to the sources of data. This is a genuine strength of Ataccama compared to many vendors in the fabric space.

The knowledge graph of Ataccama has been around since 2016 and has several ways of being represented in order to show business users their data landscape in a way that makes sense in business terms. Ataccama can also generate queries to retrieve data as needed from source systems based on its understanding of the data structures stored in its data catalogue. It does this by pushing down queries to the underlying source systems, such as Snowflake or other sources. While it can cache certain regularly used data, it is not a database, and any data that it stores is transient other than master data and metadata. While the vendor does not describe itself as having an optimiser, it does in fact do many things that a database optimiser would do in terms of deciding the best way to satisfy a user query, including a cost-based optimisation approach.

The vendor does not pretend to provide an all-encompassing solution to all data management activities, and has links to a variety of partner technologies such as data visualisation and analysis tools.

Ataccama can provide many elements of a modern data fabric architecture. While many vendors have a data catalogue, few have an in-depth background in data quality and master data management that ensures that data served up to business users is accurate, complete and timely. The vendor had seven years of production code incorporating various types of artificial intelligence, and it has a well-thought-out approach to the use of generative AI that is already live within its product rather than just being on a technology roadmap. I have been following Ataccama since soon after its inception in 2008, and its technology stack has always seemed to me very well thought out, which is reflected in its successful implementations in some very demanding customer environments.

The bottom line

Ataccama can provide a significant part of a data fabric architecture. Its modern technology stack has hundreds of customers and its underlying focus on data quality makes it well suited as the basis for serving up data to business users. Ataccama should be seriously considered for customers wanting to implement a data fabric architecture.

Fig 2 - Ataccama platform differentiators

Ataccama ONE covers the full range of data quality functionality. Users can connect to a wide range of data sources and ingest the metadata about these sources, as well as running a range of profiling statistics. This includes such things as record counts, recognising patterns in the data such as postal codes or social security numbers, detecting null values etc. Data quality rules can then be set up and applied to this data, either manually or via the underlying AI in Ataccama suggesting rules to the business users. The AI can also generate draft business descriptions from the data to populate the catalogue. Textual data quality rules can be entered by a business user in normal English and the AI translates this into Ataccama’s underlying scripting language as a business rule. Data quality can also be monitored over time.  A new feature, released in January 2024 is data preparation, a way to build data pipelines. This has a visual interface showing the various stages of process or transformation to be applied such as “read table”, “remove sensitive data”, “standardise contact” etc. Another example of the use of AI in the product is the internal documentation, where you can chat with an AI to search the documentation and come up with answers to questions, saving you from having to search through the documentation yourself.

Ataccama’s products can be deployed either on-premise, in a public or private cloud or a hybrid environment. Ataccama’s data quality suite competes with products like Talend, Informatica and IBM in terms of platform suites, and more specialist products like Experian and others in certain areas like customer name and address validation. It now competes with Collibra in the data quality space, given the latter’s recent acquisition of the OwlDQ data quality product.

Ataccama customers, who range across industries such as financial services, often have quite challenging environments in terms of size and scale, and this has long been a strong point for Ataccama. The vendor recently commissioned a 3rd party market research firm to interview its customer base and this feedback reported a 30-50% productivity increase across customer data management teams, a 10-25% reduction in spending on data management tools, at least a halving of issues related to fraud and compliance breaches, and better outcomes from core business processes due to better data management.

The bottom line

Ataccama has a full-function data quality product with a rich feature set, proven high performance and a substantial customer base that has deployed the product in challenging environments. It should be high on your candidate list if you are looking for a data-quality solution.

In terms of the data governance area specifically, it has a data catalogue, business glossary, workflow, data lineage, policy management, data quality (profiling, merge matching, data enrichment etc), data observability and monitoring etc. In other words, it has pretty much the full range of capabilities that you would expect from a data governance tool. The product has a knowledge graph that can visually show relationships between data, and this is interactive, so for example you can focus on one particular data area and switch to a detailed view to see information like the data quality scores and statistics for that particular piece of data. Data lineage is provided organically to a degree, though there is also a partnership with data lineage specialist Manta. All this range of functionality is delivered via a unified user interface, since Ataccama has built its software from scratch rather than assembling it through acquisition. Other than the data lineage partnership, just about the only OEM of other software is the niche area of customer name and address validation, which it does via Loquate, software used by almost all data quality vendors these days.

Ataccama provides an integrated platform ranging from data governance through to data quality and master data management. All this capability, other than a couple of very specialist areas such as name and address validation, is provided by software that was developed from the ground up to work together, rather than being patched together from various acquisitions. Consequently, Ataccama ONE is well suited for companies that want to implement a data governance solution that will not just be a stand-alone solution that documents the state of play with their data. Using this technology, they can move beyond this into data quality improvement and remediation, which will be a foundation for initiatives such as digitisation, and enable artificial intelligence initiatives with a sound basis of good quality data.

The Bottom Line

Ataccama ONE is well suited for companies that want to implement a data governance solution, but also want to move beyond this into actual data quality improvement and remediation, all using the same product suite.

Fig 02 - The Ataccama ONE Knowledge Catalog

The other notable result of the deep integration provided by Ataccama ONE is that the various user interfaces that are provided, which are targeted at different personas, are consistent across all of the platform’s capabilities. As illustrated in Figure 2, this is visually appealing. More notably, this screenshot shows the forthcoming release (2.0) of Ataccama ONE, in which the company is introducing its Knowledge Catalog as a superset of both its Data Catalog and Business Glossary.

More generally, the company describes its platform as “self-driving”, by which it means that it has built significant levels of machine learning and AI into Ataccama ONE. As an example, the data catalogue, which includes full text search and supports natural language processing, will learn from past experience of relevant searches, and come up with notifications if it detects an anomaly or other alert-worthy information, such as “looks like a table with GDPR content”. While on this topic, it is worth noting the company’s support for data masking (both static and dynamic), which includes consistent masking and the ability to preserve referential integrity, amongst other algorithms. In addition, consent management is provided within the MDM module. Unfortunately, there are no features to support the automation of data subject access requests (DSARs). While the company is by no means unique in building machine learning into its platform we particularly like the fact that it has also built explanations of its AI into the offering. We have not seen this level of explainability from other vendors.

Other features of note include the fact that the IDE is Eclipse-based, there is support for REST APIs and, where relevant, jobs can run natively on Spark, Databricks and so on, so that transformations can either be supported in ETL or ELT mode. Beyond this, the company provides a Reference Data Management module, with approval-based workflow capabilities and governance workflows built in to manage and distribute reference data. Data quality rules can be exposed as a web service.

Ataccama ONE is a comprehensive platform for establishing and managing (cloud-based) analytic environments. It has all the fundamental requirements that one could ask for, though from a marketing perspective it might be advantageous to the company if it called out data governance as a module in its own right. That said, spreading governance capabilities across the environment makes a lot of sense so this is a marketing argument rather than a technical one.

Ataccama ONE has the distinct advantage of being a natively integrated, single engine solution, as opposed to a bunch of individual modules that have been stitched together. This is not only relevant when it comes to the performance and scalability of the platform but also to the end-user interface, which is consistent – by persona – across the environment. The product’s “self-driving” approach to automation is also significant and perhaps ahead of some of its competitors. That said, all major market players are investing into AI and automation capabilities, and one area where Ataccama stands out is in its explainability.

The Bottom Line

Ataccama, while by no means one of the biggest names in this space, is a significant contender with a proven track record. It is certainly worth serious consideration as a potential supplier.