Informatica

Fig 01 - CCPA dashboard in Informatica Axon Data Governance

Axon Data Governance allows you to view and manage a variety of business and data assets within a single location, including data sets, business terms, policies, processes, and so on. A contextual graph search is provided, as are automated workflows, approval processes, and a variety of dashboards at both the system and local levels (one of which is displayed in Figure 1).

Assets come equipped with a variety of information, notably including connections with your other assets. This includes explicit associations, applicable rules and policies, impact analysis, and data lineage. For policies, this also includes their position in your overall policy hierarchy. The product thus provides a connected view of your overall system. Relevant stakeholders are also highlighted, including both direct stakeholders as well as the broader stakeholder community, and discussion features are provided to facilitate collaboration. The product also offers a view into your technical metadata, held within Enterprise Data Catalog, and allows you to create data sets within Axon Data Governance directly from that metadata.

Fig 02 - System lineage view with data quality overlay in Informatica Axon Data Governance

Data lineage in Axon Data Governance is business-oriented and available at multiple levels. It is displayed visually, and can be filtered, explored, and so forth on the fly. You can also overlay a variety of metadata, such as data quality and risk, onto your lineage view. This is shown in Figure 2. Corresponding technical lineage is available within the Enterprise Data Catalog.

The product’s data discovery capabilities are quite extensive, leveraging CLAIRE to automatically sort your data into a variety of ‘Smart Domains’, a number of which are provided out of the box. CLAIRE can also be used for intelligent business term association, tagging relevant data assets with business terms based on data discovery rules equipped to each term, and thus connecting your business and technical assets.

For data quality, natural language processing is used via CLAIRE to automatically generate new (or recommend existing) data quality rules based on plain English descriptions of your quality requirements. Data quality checks are automated – in particular, newly ingested data is checked automatically – as is reporting. Several categories of data quality are also offered, allowing for nuanced quality measurements.

Several features are provided to support data privacy and regulatory compliance. In addition to the data discovery described above, the product offers sensitive data discovery and classification across structured and unstructured data, support for subject access requests, and the ability to track policy violations and understand and assess risk. Policies can be managed just as any other asset, and can also be associated to business terms and automatically enforced on correspondingly tagged data assets. Integration with Informatica Data Privacy Management also allows your policies to drive risk analysis and privacy monitoring (for example, alerting) inside Axon Data Governance.

Finally, Axon Data Governance provides data democratisation via Informatica Axon Data Marketplace, a new, embedded feature. The Axon Data Marketplace solution allows data owners to publish their data directly from Axon Data Governance, as well as manage and track access to it. For data consumers, it provides a means to search for data – which is organised into meaningful business categories – and request access to it centrally via a checkout process. Axon Data Marketplace also provides a significant degree of automation, automatically notifying data owners of incoming requests, checking requests against existing policies, and delivering the relevant data when a request is approved.

Axon Data Governance provides a notably broad set of data governance capabilities, which are extended even further by its integration with Enterprise Data Catalog and Data Privacy Management. What’s more, these capabilities are, in general, highly automated, often owing to shared metadata-driven intelligence and other functionality provided by CLAIRE. In effect, Informatica provides a complete and often intelligent solution for data governance.

In fact, separating out business concerns within Axon Data Governance from technical concerns in Enterprise Data Catalog and Data Privacy Management provides its own benefits, by offering experiences and views tailored specifically for business and technical users, respectively. The privacy compliance and risk monitoring provided by Informatica Data Privacy Management, and especially the ability to bring the results of that monitoring back into Axon Data Governance for consumption by your business users, is a particular strength.

Axon Data Marketplace also provides significant advantages. By acting as a centralised ‘one stop shop’ for data approval, it makes it much easier for data consumers to find and request access to the data they need. Likewise, the Axon Data Marketplace benefits data owners by providing a single location to manage access requests, by automating much of the approval process, and by automatically delivering data assets to your data consumers upon the completion of said process. In short, it makes the lives of your data owners and consumers simpler and easier, allowing them to spend more of their time and energy on other concerns, such as creating value from the data within your organisation.

The Bottom Line

Informatica Axon Data Governance is the keystone for Informatica’s intelligent data governance solution. Said solution is eminently integrated and complete, highly automated and scalable, and well worth your consideration.

Data governance has emerged as a prerequisite for the successful management of corporate data. A decade ago, it was normal for large organizations to leave data quality and ownership to their IT departments, but this approach rarely works, since IT departments do not usually have the political clout to insist on data standardization, for example standardising on a common customer or product hierarchy across a company. Many ambitious master data management projects foundered due to a lack of business ownership of data. With a well-run data governance program, ownership of data is taken by business users, with data stewards embedded within business lines, supported by a data governance committee with business leaders who are senior enough to resolve issues.

Implementing such a data governance program is a great deal easier with a central data catalog to discover data, document business ownership of data, data relationships, policies and measurement of data quality. Data governance tools have emerged over the last decade as a way to support and cement data governance processes. This allows a company to gain greater trust in its data and to be able to confidently deal with regulatory and compliance requirements, while keeping data open for business with data democratization support that accelerates data delivery through a marketplace shopping experience. The Informatica Data Catalog has emerged as one of the major contenders in this important market.

The bottom line

Informatica’s Data Catalog is a capable offering that is cloud-native and has a full range of functionality for data governance within Informatica’s unified Cloud Data Governance and Catalog service, further easily extensible with Cloud Data Marketplace for data delivery on a common platform, IDMC. In particular, it has an exceptionally strong artificial intelligence story, drawing on its previous and long-established machine-learning experience with CLAIRE. Customers looking for a data governance solution should carefully consider evaluating it to see whether it matches their specific needs.

Fig 01 - Informatica Data Privacy Management integration with Axion

Considered holistically, Informatica’s approach is that you start by creating actionable data privacy policies (integration with Axon – see Figure 1 – from which you can overlay policies into Informatica Data Privacy Management). Then, discover and classify your sensitive data; uncover and map “identities” to data (that can be used to support data subject rights requests under regulations such as the GDPR and CCPA); analyse the risks posed by sensitive data so that you can prioritise your protection plans; protect the data (masking and other techniques); respond to rights and consent requests; and, finally, be able to track and report on all of this.

The facilities for discovering sensitive data, which may be run against samples of the data, if required, are extensive and can be automated using ML and AI. You can match on the metadata via patterns, regular expressions and rules and you can also introspect SQL – both SQL queries and any SQL used for data movement purposes – though not stored procedures. Distance constraints (for example, post code needs to be near city name) can be used and you can define white (always sensitive) and black (never sensitive) lists. In the latest version, the underlying engine can make recommendations about what should be in these lists. The leverage of primary/foreign key relationships is planned for the next release. For unstructured data the product uses AI to look for parts of speech and otherwise relies heavily on the use of reference data. When potentially sensitive data is discovered you can set your system up to automatically agree that this is sensitive or that it is not sensitive or that it needs human validation, according to thresholds that you define.

Fig 02 - Risk simulation planning in Informatica Data Privacy Management

The use of identity mapping, is interesting because it allows you to support rights requests: for example, where is Philip’s data? To discover and map identities, the product uses fuzzy matching and the product ships with various pre-built classification policies such as PCI, GDPR and so forth. This is augmented by support for domains (name, email address and so forth) which are provided out of the box and can be combined.

Finally, we should mention the risk scoring (see Figure 2). In addition to providing risk analytics and key performance indicators there is also risk simulation planning. This allows you to see the impact of using different approaches to say, masking.

While sensitive data isn’t only about personal data, it is issues over complying with new privacy regulatory mandates that are driving the market for sensitive data discovery and the subsequent protection of that data. Informatica is well-known as a market leader in the data management space and this is where its strength lies. The company has very strong credentials when it comes to structured data and it has focused its discovery capabilities in this area, where it has significant strengths. We particularly like the company’s support for managing identities, which makes a lot sense within the context of GDPR, CCPA and similar regulations to determine data access.

Conversely, Informatica has respectable rather than comprehensive capabilities when it comes to discovery in unstructured environments. But, and this is a big but, companies that have focused on discovery for unstructured data tend to have very limited structured capability, typically limited to just Oracle and SQL Server. Most large enterprises are not limited to just these providers which would mean having two different sensitive data discovery solutions which, to our minds, does not represent any sort of solution.

The Bottom Line

Organisations should be aiming to have a single solution for sensitive data discovery that enables a data privacy governance strategy across a global enterprise. Organisations with multiple heterogeneous database implementations, as well as file systems, that they need to secure, would do well to shortlist Informatica as one of only a few companies that offers significant structured data discovery along with unstructured support.

Informatica’s approach is to first enable you to create actionable data privacy policies. Then, you discover and classify your sensitive data, analyse the risk posed by it in order to determine and prioritise further actions (most likely including masking or other anonymisation methods), carry out those actions, and track and report on all of the above. You’ll also uncover and map “identities” to your data as part of the discovery process that can be used to build a registry of data subjects and thereby respond expediently to rights and consent requests. The results of your discovery process (among other things) is presented in a data privacy dashboard.

The product’s facilities for discovering sensitive data, which can be run against samples of the data if required, are extensive, and can be automated using machine learning and AI. You can pattern match on the metadata (using either regular expressions or a data dictionary) and you can introspect SQL – both SQL queries and any SQL used for data movement purposes – though not stored procedures. Unstructured data is supported via NLP (Natural Language Processing) followed by the same sort of pattern matching.

Data is examined in its context, meaning that data that is only contextually identifying (which is to say, when combined with other information) will still be flagged. Proximity matching – in other words, distance constraints – can be used (for example, post code needs to be near city name) and you can define white (always sensitive) and black (never sensitive) lists. CLAIRE will make automated recommendations about what should be in these lists.

For unstructured data the product uses AI to look for parts of speech and otherwise relies heavily on the use of reference data. When potentially sensitive data is discovered, your system will either automatically agree that it is or is not sensitive, or decide that it needs human validation, according to configurable confidence thresholds. Discovery can also be actioned on images and documents (PDFs, for example) via optical character recognition, as well as compressed files and Outlook 365 emails (including attachments).

As mentioned, identity mapping supports rights requests, such as locating all of a given customer’s data. The product uses fuzzy matching to facilitate this, and it ships with various pre-built classification policies such as PCI, GDPR and so forth. This is augmented by out-of-the-box domain support (name, email address and so forth) as well as a handful of response templates for rights requests.

Finally, we should mention risk scoring. In addition to providing risk analytics and key performance indicators, including proliferation and user activity analysis on sensitive data, there is also risk simulation planning. This allows you to see the impact of using different approaches to protecting your sensitive data. Policy-driven alerting is also included, and can be used with sensitive data. Likewise for automated workflows.

Informatica is well-known as a market leader in the data management space, and has strong credentials in that regard. This certainly shows in its Data Privacy Management product, which offers a broad suite of capabilities that only get broader when you consider it as part of the company’s overall portfolio. This breadth is always an advantage, but sensitive data discovery particularly benefits because of how much it works in concert with other technologies – masking, policy management, and so on – to achieve the desired outcome of protecting your sensitive data and achieving regulatory compliance. We particularly like its support for managing identities, which makes a lot sense within the context of GDPR, CCPA and similar regulations.

Moreover, Informatica is a proponent of the idea (and in this case we wholeheartedly agree) that data privacy can be about more than just compliance. Rather, you can derive real business value from your privacy efforts. For sensitive data discovery, you can make headway in terms of the visibility and accessibility of your data, thus generating significant quantities of actionable data intelligence: recall that Informatica does not scan for sensitive data particularly, but rather for data intelligence generally. In turn, this can be used to examine and improve data quality, enable analytics, and so on.

The Bottom Line

Informatica offers a catch-all, value-oriented proposition for data privacy in general, and for sensitive data discovery in particular, that we find very appealing. It is well worth adding to your shortlist.

Cloud Data Integration (CDI), Informatica’s data integration capability, leverages CLAIRE and the connectivity layer as you would expect. It supports ETL and ELT processes, with data transformations available via a drag and drop interface alongside CLAIRE-driven recommendations. It also provides a unified, wizard-driven interface for ingestion and replication, alongside real-time job monitoring, lifecycle management and alerting.

Fig 1 - Cost-performance optimisation in Informatica CDI

CDI uses elastic scaling to ensure resiliency and high availability, and serverless deployment is available to minimise infrastructure management. In addition, Informatica Advanced Pushdown Optimization supports ELT in the cloud, wherein transformation logic is converted into native commands and pushed to the source and/or target databases. This accelerates data processing without incurring any additional ingress or egress charges. Moreover, Figure 1 shows further options that Informatica provides for cost-performance optimisation.

The platform provides a variety of features to further automate your data integration. This includes intelligent field mappings, automated structure discovery/schema inference, automated entity creation based on documented (and/or discovered) relationships, pipeline and expression auto-complete based on classifications and other metadata, auto-tuning and auto-scaling at runtime, auto-healing for resiliency, and more.

Fig 2 - Informatica ModelServe

Capabilities for data pipeline orchestration, programmatic pipeline creation, and pipeline/workflow scheduling, tasking and monitoring are also available. You can reach out to the broader Informatica portfolio to incorporate data quality and governance into your pipelines, and validation is available alongside a degree of support for automated testing. Pipeline monitoring is provided, including resource monitoring (such as spike detection and alerting) and operational analytics. ModelServe (shown in Figure 2) and INFACore, two recent additions to the Informatica platform, also exist specifically to make it easy for data scientists and engineers to build and deploy machine learning (ML) models and compose data pipelines, respectively, in an automated fashion.

Cloud Data integration is supported by Informatica Data Loader, a standalone, self-service data loading offering designed for easy onboarding and a straightforward, wizard-driven user experience. It uses a freemium pricing model alongside a flexible payment scheme that stretches across all of Informatica’s services, and it is available on AWS, Azure, Google Cloud, Snowflake, and Databricks. It features automatic schema drift detection and supports a variety of data sources. It is suitable for initial and incremental loading, and going from setup to starting to load data takes only a few minutes.

Informatica also offers a real-time streaming data ingestion and processing solution with support for high-volume, low-latency stream data integration. In addition, the platform provides a wizard-based data ingestion and replication service - Cloud Mass Ingestion (CMI) – that is compatible with various data sources, including databases, applications, streams, files, and so on. This includes support for Change Data Capture (CDC) and IoT. Moreover, CMI allows you to ingest in bulk or incrementally, and can be used to enable real-time analytics consumption. It can also automatically detect schema drift at the source and replicate incremental changes to the target.

Informatica’s data management platform offers a substantial breadth of capability. Moreover, the individual solutions within it are generally of a very high quality, and in fact we would consider many of them to be best of breed in their own right.

To wit, it is highly suited to the cloud, which for data integration specifically manifests as – for example – pushdown optimisation to various cloud vendors as well as cross-ecosystem pushdown capabilities for workloads utilising multiple clouds simultaneously. It also features extensive automation that is present throughout the platform in general and its data integration capabilities in particular. What’s more, the shared capabilities underpinning the platform – the metadata management layer, the security layer, the connectivity layer, and, most of all, CLAIRE – add significant value, ensuring that each individual solution is well-integrated into a greater whole and enabling the platform itself to be more than the sum of its parts.

Finally, the platform is designed, in part, to democratise data management by enabling collaboration and self-service. This certainly applies to Cloud Data Integration, which is equipped with easy-to-use interfaces and collaboration features like version control integration. Data Loader in particular is notably straightforward to onboard with, thanks to its simple interface and freemium pricing model. It also places no limit on the amount of data users can load.

The Bottom Line

Informatica Intelligent Data Management Cloud is an exceptional solution in many ways. Via Cloud Data Integration (and assisted by Informatica Data Loader) it is more than capable of providing a robust, cloud-native, and highly automated solution for data ingestion, data replication and data integration.

Informatica’s solution for stream processing consists of several different products and services that combine within the singular platform of IDMC. The backbone of this solution consists of three services: Cloud Mass Ingestion, High Performance Messaging for Distributed Systems, and Data Engineering Streaming. Respectively, these provide streaming ingestion, high-speed messaging, and stream processing. Other Informatica offerings, such as Cloud Data Integration and Enterprise Data Catalog, can then add to this core. Taken as a whole, IDMC lets you ingest streaming data and move it to wherever it needs to be, while processing, transforming, and governing it as necessary.

More specifically, Cloud Mass Ingestion provides format-agnostic data movement and mass data ingestion, including file transfer, CDC (Change Data Capture) and exactly-once database replication. It also offers mass streaming ingestion from a variety of sources, complete with real time monitoring, alerting, and lifecycle management.

High Performance Messaging for Distributed Systems is what it says on the tin: a performant messaging system boasting “ultra-low latency”, targeted at distributed systems. In addition, it provides high resiliency and guaranteed message delivery. Alternately, you could use Kafka, or another messaging service, via the connectivity options that Informatica provides. For instance, Cloud Data Integration can gather and load in batch data from Kafka directly, with an “at least once” delivery guarantee. Enterprise Data Catalog can also scan Kafka deployments in order to extract relevant metadata (message structure, for instance).

Fig 2 - Informatica Data Engineering Streaming pipeline

Finally, Data Engineering Streaming is a continuous event processing engine built on Spark Streaming that is designed to handle big data. It supports both batch and streaming data, and it features out of the box connectivity to various messaging sources, as well as no-code visual development (shown in Figure 2). As part of the latter, it provides hundreds of prebuilt transformation functions, connectors and parsers. You can also pipe in your own code, or build your own functions and whatnot using Informatica’s business rules builder. Essentially, it allows you to enrich your streaming data in real time. This could mean improving data quality, masking sensitive data, aggregation, or what have you.

IDMC also supports Spark Structured Streaming, which can be important if you want to aggregate streaming data based on event time (not processing time) and hence reorder data that has arrived out of order before delivering it to your data target. It also supports Confluent Schema Registry, which can be used to parse Kafka messages, retrieve message structure, and handle schemas as they change and grow.

Moreover, you can use CLAIRE to augment your solution with machine learning. For example, to automatically detect and generate schemas. This is particularly beneficial in that it allows you to discover and rectify data (and schema) drift. Informatica also provides a ready-made integration pipeline for data science and machine learning which helps you to apply machine learning and AI models to your streaming flows.

In addition, Informatica is keenly aware of the need to govern streaming data, and the company’s suite of data governance products are available for this purpose. Data cataloguing, preparation, discovery, lineage, and visualization are all available, and have been designed to promote self-service and collaboration. Security features, like masking, authentication and access control, are also available, and real-time job monitoring, analytics, and visualisation are provided via the Operational Insights service.

Informatica provides a high-quality, comprehensive stream processing solution that is positioned as just one part of a much larger, and broader, integrated data platform. Moreover, it is highly compatible with the cloud, and includes native cloud ingestion; it provides a broad range of connectivity, exemplified by the sheer quantity of connectors and scanners provided; and it offers a unified user experience, regardless of whether you’re deploying in-cloud or on-prem, which data sources you’re using, whether you’re using it for streaming or batch processing, and so on. The latter is particularly important, in that it allows you to abstract out much of the underlying complexity of stream processing, thus enabling your business users to work that much more efficiently and effectively.

We are also impressed by IDMC’s ability to transform streaming data in real-time, especially regarding its substantial number of built-in transformations. This is an area where Informatica’s breadth of capability really shines, by allowing you to combine high-end data quality and masking with stream processing. Moreover, Informatica’s data governance and security solutions combine with streaming in much the same way, with similar benefits.

The Bottom Line

Informatica Data Management Cloud offers highly capable and well-integrated stream processing as part of its overall data management functionality.

The product supports a variety of methods and options for data subsetting, data masking, and rules-based synthetic data generation. Data masking in particular is policy-driven, complete with out-of-the-box masking policies that support PCI, PII and PHI compliance. Masking works with structured and unstructured data, is federated to ensure consistency across multiple datasets (including datasets in multiple locations, such as on-prem and in cloud), and can leverage encryption that can be reversible or irreversible, and format or metadata preserving, as required. For auditing purposes, you can also generate compliance reports that show exactly how much of your sensitive (test) data is masked.

Fig 01 - Informatica Test Data Management self-service portal

Furthermore, there are multiple ways to accelerate your test data provisioning within this environment. For instance, the product includes a self-service portal (see Figure 1) that allows test data admins to publish test data sets for consumption. Testers can provision any data sets that are made available to them, and can modify, subset or copy them locally as they will, allowing them to customise their test data for their specific testing requirements. Data within the portal can also be tagged to make it easier to search through. Somewhat more primitively, you can distribute data to your testers via parameterised test data plans, allowing your testers to fill in the plan’s parameters whenever they need relevant test data, and receive it accordingly.

On the other hand, you could integrate provisioning into your existing test automation workflows, perhaps by leveraging the product’s test data warehouse. This is an additional capability that can be used to store, and subsequently provision, up-to-date test data on-demand without troubling your production environment. You can also use it as a baseline to reset your personal testing environment against, allowing you to experiment as you wish without negatively impacting either other testers or your own overall testing efforts. It can even link into your DevOps environments, CI/CD pipelines, and test automation workflows, thus automating your provisioning. You can also use it to contribute to the solution’s self-service capability by publishing data sets in the warehouse directly to the portal.

Fig 02 - Informatica Test Data Management test data coverage

In addition, the product provides a number of neat visual capabilities, including graphical test data coverage and an entity view while subsetting. The former in particular allows you to see whether you have sufficient data to achieve the level of coverage you desire. This is shown in Figure 2.

Finally, TDM can discover (and hence mask) your sensitive data using several methods. This includes domain and pattern matching, dictionaries, algorithmics, and AI/machine learning. These discovery capabilities are extended by Informatica Data Privacy Management, an additional product that provides enterprise-wide discovery and classification of sensitive data, DSAR reporting, and continuous multi-factor sensitive data risk monitoring with AI-enabled analytics. In particular, Data Privacy Management also tracks the lineage of your masked data and allows you to see where your data has been masked as it flows through your organisation. It is also integrated with TDM’s masking functionality, allowing you to, for example, automate a masking process in TDM from within Data Privacy Management to support risk remediation policies.

TDM is an outstandingly broad test data solution. It offers functionality that covers almost all aspects of test data management, which consequently enables it to address a wide variety of use cases. Its capabilities include data subsetting, static data masking, synthetic data generation, data discovery, test data provisioning, self-service, and format-preserving encryption. Moreover, it is capable in all of these areas, and you could reasonably argue that it is best-of-breed in several.

Standout features include visual test data coverage, the test data warehouse, and the self-service portal. The ease of use, reuse, enhanced collaboration, and reduced dependency on admins that the latter two provide are particularly notable as boons to your testers’ productivity.

Even then, TDM is still only one solution among many available from Informatica, and the breadth of that ecosystem – even if you only consider privacy and governance – is its own advantage, and one that TDM benefits from substantially. For example, shared metadata can be used to enable a far more comprehensive approach to data protection and transparency. There is not enough space on this page to go into the finer details, but suffice it to say that when it comes to Informatica, the whole is greater than the sum of its parts (and the parts themselves are highly capable to begin with).

The Bottom Line

TDM proves very competent in almost every area of test data management, with a particular penchant for robust self-service and expedient test data provisioning. There is little reason it shouldn’t be on your shortlist, especially in the context of building out more complete data governance and privacy operations.