
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.