Syniti

Fig 01 - Search in Syniti Knowledge Platform

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.

Fig 02 - Defining a policy in Syniti Knowledge Platform

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.

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 (including data quality rules), policies and business terms; and, of course, data assets. 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. 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).

Fig 2 - Applying data quality to improve business outcomes

As far as data quality is concerned Syniti’s view is that unless data quality is combined with business impact it is “just providing metrics for IT”. There is more than a grain of truth in this. For example, consider the screenshot in Figure 2, which shows account payable leakage for major customers, vendor payment terms that are non-standard (corporate rules state 90 days) and where payment terms have been overwritten manually. There are many other such use cases. For example, unrealised discounts or rebates due to supplier duplication.

More specifically, as far as data quality is concerned, Syniti provides the sorts of profiling, cleansing and matching capabilities one would expect, though the company is ahead of many of its competitors in implementing machine learning. As mentioned previously, the architecture of Syniti’s data quality offering is currently going through a significant re-development but it is too early for use to evaluate the product on that basis.

Bloor Research has previously evaluated the Syniti Knowledge Platform as a data governance solution and we do not wish to repeat all the comments made there. From a data quality perspective, we especially like the fact that this is not seen as a stand-alone capability. Indeed, while it is entirely plausible to implement Syniti software purely for the purposes of better data quality that would, in our opinion, be a wasted opportunity: it is in the synergies that exist across the Syniti Knowledge Platform that this solution has its greatest strength and it is that that you should be considering leveraging.

The Bottom Line

Syniti has been innovative in the way that it has designed the Knowledge Platform, and it is ahead of much of the market in its use of machine learning and knowledge graphs. If you appreciate that data quality, governance and associated processes should really be about improving business outcomes, then you should certainly be evaluating Syniti.