Solix Technologies, Inc. was founded in 2002 and provides Enterprise Data Management (EDM) solutions internationally. It operates worldwide through an established network of value added resellers (VARs) and systems integrators.
It claims to assist businesses to improve application performance, reduce storage costs, and meet compliance and data privacy requirements, by achieving the goals of Information Lifecycle Management (ILM).
Its mission is “to organize the world’s enterprise information with optimized infrastructure, data security and analytics”. Most recently, this has manifested in products designed to enable and empower the data-driven enterprise.
Solix Common Data Platform (CDP) for Data Governance
Last Updated: 14th July 2020
The Syniti Knowledge Platform is a unified solution for data governance and management with a variety of functions, such as data migration, data replication, data quality, master data management, and so on. Its focus is on providing every user in your organisation with access to a democratised library of data and knowledge assets that are presented and understood within an appropriate business context, and with the ultimate aim of tightly aligning your data governance processes with the business outcomes you want to achieve.
Particularly notable capabilities within the platform include Syniti DeepGuidance, which uses your data, metadata, and accompanying organisational knowledge and business context to guide user actions towards positive business outcomes; Syniti KnowledgeCapture, which captures technical knowledge within your system and presents it in simple, business-friendly language; and Syniti KnowledgeReuse, which allows you to reuse assets generated during data migration, such as business rules and ownership information, within your data governance processes.
“[Syniti]’s collaborative, customer-first approach and deep expertise were instrumental in helping us take our data quality standards to the next level.”
“From our first conversation with [Syniti] it was clear they understood our business need.”
The core of the Syniti Knowledge Platform is its knowledge graph, which contains both business and technical assets. In turn, your graph and its assets are accessed via the platform’s data catalogue. This allows you to view, edit and manage all of your assets, including strategic assets such as initiatives, strategies and goals; governance assets such as rules, policies and business terms; and, of course, data assets. Search access to these assets is provided (as shown in Figure 1), and all of them include a plethora of associated metadata, including related assets. For example, this could list associated business terms or applicable rules and policies. It could also contain business goals or strategies that the asset is contributing towards, or a business representation of the asset if it is primarily technical. In the latter case, the intention is that technical assets are linked directly to business assets that demonstrate what each asset does and why it is important.
Moreover, the assets within Syniti are democratised. All users are able to share their own descriptions or definitions for any business asset, and to comment on or endorse anyone else’s. In turn, each asset is equipped with a list of subject matter experts, or ‘sponsors’, who are responsible for curating their assigned assets, using these crowd-sourced suggestions and endorsements as a guide. When a user requests a change to an asset, the platform initiates an automated workflow that polls each of that asset’s sponsors for their opinion on that particular change. When and if a change wins majority approval, it can be implemented automatically.
What’s more, the Syniti Knowledge Platform understands assets within their business context. This consists of both identifying the relationships and connections between your assets and leveraging those connections to create business value, as evident within the product’s DeepGuidance capability, which uses machine learning to automatically and dynamically suggest improvements to your assets as you are viewing them. This could include suggestions for new business rules, acknowledgement and remediation of data quality issues, or detection of suspected – but undocumented – asset relationships, to name only a few examples.
For business terms in particular, natural language processing is employed to detect and highlight words or phrases that may represent additional, as yet undefined business terms. This is seen in Figure 2, along with several DeepGuidance suggestions. In turn, these terms can be referenced within business rules, which are written in natural language using business terminology. A given rule will also contain its written implications (in other words, its business meaning) as metadata, as well as an enforcement profile that shows where and how it is being enforced (for example, a data quality rule might link to its entry in your data quality solution). Policies operate in much the same way as rules, but as you might expect are much higher level. More importantly, terms, rules, and policies often act as connective tissue between your technical and data assets and your business goals and strategies, enabling you to understand precisely how the former relate to the latter.
The Syniti Knowledge Platform has a number of properties that make it very suitable for data governance. It allows you to consolidate and curate all organisational knowledge within a single platform, coaxing knowledge out of any silos that have built up and making it accessible to all; it enables technical assets and knowledge to be tightly coupled with business context and strategy, allowing you to ensure that they are contributing positively to business outcomes; and it does all this while encouraging collaboration, reuse, and understanding.
The collaborative capabilities that Syniti provides are particularly worth noting. As mentioned, the business terms, policies, and other assets within the platform are crowd-sourced. This means that everyone within your organisation will have the opportunity to help define them. This is a good thing, both for allowing your organisation as a whole to share understanding and knowledge of what your assets mean and how they are used in practice, and for allowing the users of a given asset to define it in the most useful possible way. What’s more, the fact that Syniti is not licensed on a per user basis means that your entire organisation will be able to participate in this process.
Syniti’s DeepGuidance capability is also notable: the ability to make AI-driven dynamic suggestions and recommendations across your entire catalogue is very powerful, both for promoting understanding and for driving positive change across the platform.
The Bottom Line
The Syniti Knowledge Platform prioritises the business value and the democratisation of your data and knowledge assets above all else. If you share these priorities – and there is every reason to – it is absolutely worth your consideration.
Solix Technologies Sensitive Data Discovery
Last Updated: 21st May 2020
Solix Common Data Platform (CDP), formerly the Big Data Suite, is a broadly applicable ‘Big Data Application Framework’ that acts as a one stop shop for the management and governance of all of your enterprise data (see Figure 1). To this end, it offers a variety of capabilities, not least of which is sensitive data discovery, provided as part of CDP’s Governance Workbench. The product also provides complementary functionality in the form of data masking, data security, database archiving, test data management, and so forth. It is accessible through the web browser, can be deployed in-cloud, on-prem, or as part of a hybrid solution, and operates at big data scale across both structured and unstructured data. Supported data sources are numerous for relational data, including Oracle, DB2, MSSQL, MySQL and Sybase, and a selection of file formats (JSON, CSV, XML, and several types of documents, spreadsheets and presentations) are supported. However, NoSQL support is currently limited to MongoDB and Hadoop/HDFS.
Sensitive data discovery in CDP allows you to identify and locate sensitive data across all of your applications, then catalogue it within a single platform. This not only supports regulatory compliance but provides additional value when used in conjunction with the data security and analytics features contained within CDP. For example, you could leverage CDP to discover your sensitive data, mask or encrypt it, then expose it to your analytics tools via an API. Equally, you could use it to find and protect the sensitive data hidden in your data lake, helping to prevent it from turning into a data swamp. The overall point is that CDP doesn’t just provide sensitive data discovery by its lonesome, but as the first step in addressing an end-to-end use case.
The discovery process itself examines both your data and the metadata attached to it. The process begins with the ‘low-hanging fruit’ of metadata matching (for instance, by examining column names) before moving on to the more difficult – but also more thorough – techniques of pattern and value matching within the data as well as master data lookup matching. Moreover, over 25 predefined classification policies are provided out of the box. These are based on compliance requirements such as GDPR, and have been designed to locate any PII, PCI or PHI data elements within your system. You are free to use these policies as-is, customise them, or create your own. The discovery process is fully configurable, and may include sampling to help your users detect false positives. Moreover, sensitive data is always discovered in place: it is never moved by the discovery process. Discovery dashboards and data flow diagrams (as shown in Figures 2 and 3) are provided to visualise your discovery results, and the process itself (as with many of the capabilities in CDP) is available to be run in the cloud as a service.
Solix also provides a dedicated GDPR tool for finding and actioning on all data that is sensitive under GDPR specifically. This will certainly include data discovery, but could also mean purging, archiving, or masking your sensitive data, depending on your requirements.
The sensitive data discovery capabilities offered by Solix are mature and competent. The out of the box policies provided allow you to hit the ground running and receive value very quickly, and this is particularly true in Oracle and PeopleSoft environments, for which dedicated project accelerators are provided, further reducing implementation time and improving ROI. Moreover, the dedicated GDPR tool the company offers provides a relatively easy way of getting your house in order for GDPR. Given that GDPR is a major driver for sensitive data management, this is a significant draw.
One of the other big benefits for CDP is that it is not by any means restricted to sensitive data discovery. On the contrary, it provides a broad range of (sensitive) data management and security capabilities, such as data masking, database archival and application retirement. These naturally pair well with sensitive data discovery. Moreover, CDP is intended as a single, scalable and all-encompassing solution. Not only does it provide a large quantity of data management and security features, it will also deploy those features across a wide range of environments and data sources, notably including big data environments such as data lakes. The only caveat to this, at least in terms of sensitive data discovery, is that its support for NoSQL data sources is relatively limited. That said, within the context of the sensitive data discovery space, it offers as much or more than many of its competitors.
The Bottom Line
Solix CDP offers sensitive data discovery as one feature among many within a broad and highly competent data management suite. Given that sensitive data discovery works most effectively in tandem with other data security and sensitive data management capabilities, this is no bad thing.
Solix Technologies Test Data Management
Last Updated: 18th June 2019
Solix has two solution suites: Solix Common Data Platform (Solix CDP, formerly the Big Data Suite) and Solix Enterprise Data Management Suite (Solix EDMS). Both can be deployed in-cloud, on-prem, or as part of a hybrid solution.
Solix EDMS contains the company’s offerings for test data management, data masking, and database archiving. The product’s test data management capability includes data subsetting, sensitive data discovery, and synthetic data generation, forming a comprehensive solution when dealing with structured data.
Solix CDP, on the other hand, is envisioned as a broadly applicable ‘Big Data Application Framework’ that acts as a one stop shop for the management and governance of all enterprise data. In many ways it provides a superset of Solix EDMS’ capabilities, offering test data management as well as sensitive data discovery and data masking. The product operates across both structured and unstructured data and at big data scale. It also provides an enterprise data lake, enterprise and database archiving, and enterprise data warehouse optimisation.
“We were able to download the software and mask an entire landscape of non-production instances in less than an hour.”
For subsetting, two approaches are supported. The first of these is conventional – you literally extract a subset against defined parameters – but in the second you take a back-up of your production database to create a gold copy with masked data which can then be mined to create the required subsets. The advantage of this approach is that the turnaround time can be significantly improved. The parameters on which subsets can be derived can be either vertical or horizontal across the database so that, for example, you can subset by time or operating unit as opposed to subsetting by table. Solix also supports snapshot technology, provided by hardware vendors such as NetApp and EMC.
In regards to synthetic data, there is tooling provided to ensure that generated test data is representative of your real data. The embedded rules engine which defines how data should be generated is helpful in automating this process.
As far as data masking is concerned (which may be required to secure sensitive data in test subsets), Solix’ masking capability can mask consistently (and without losing referential integrity) within single or multiple databases. It offers a variety of masking rules and algorithms, some of which are generic (for example, a rule that produces a random sequence of letters) while others are specific to particular types of data. For instance, date of birth, or social security number. Custom algorithms are also supported and can be written in Java, T-SQL or PL/SQL. A notable differentiator for Solix is that it offers pre-packaged masking capabilities for Oracle and PeopleSoft application environments. In addition, while Solix provides masking support across most leading relational databases as well as MongoDB and flat files, the company has implemented its masking algorithms natively in Oracle PL/SQL.
The product’s masking capabilities are complemented by a discovery tool that looks at both metadata (column names) and actual data (looking for patterns in the data values). To reduce false positives, it uses sampling to help users decide what is and is not sensitive. The solution comes pre-populated with discovery rules suitable for identifying PII, PCI, PHI, and other such industry-defined sensitive data elements. It also provides a dedicated GDPR tool for finding and actioning on data that is sensitive under GDPR. This could mean identifying, purging, archiving, or masking, depending on the situation.
Solix offers a well-rounded test data management solution capable of both data subsetting and synthetic data generation. Its data masking and data discovery capabilities are reasonably mature and offer several helpful features that distinguish the product. These include project accelerators for Oracle and PeopleSoft, which will help to reduce implementation time and improve ROI on those platforms; discovery based on patterns within the actual data, instead of just column names (which are often unclear or generic); and several capabilities designed to ease compliance with GDPR, including a dedicated tool.
It’s also worth noting that Solix EDMS offers several capabilities in addition to test data management and data masking. Most prominently, this includes database archival and application retirement, which are both useful and relevant capabilities when it comes to complying with regulations pertaining to data retention such as GDPR (and are put to particularly good use in combination with the product’s dedicated GDPR tool). Moreover, it’s important to note that the company’s offerings do not end with Solix EDMS. If you want to deploy test data management or data masking over big data, Solix CDP could be an important differentiator. The same could be said about the ability to deploy snapshot technologies in conjunction with test data management.
The Bottom Line
Solix EDMS and Solix CDP each provide capable test data management solutions that may be of particular interest to organisations that are struggling with compliance to GDPR or other data retention regulations.