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K is a Data Knowledge platform enabling data discovery, knowledge management and data governance for all data users.
This page will provide a brief introduction to K and its architecturehighlight some of it’s key features that make it unique.
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Introduction to K
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K focuses on identifying and storing how users work with data; leveraging this information to enable data producers to improve their products; data owners to take accountability for the proper use of their data; and to scale hidden knowledge to all data workers. The product vision is to become the central platform for all Enterprise data users to easily discover, understand and govern the use of data.
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K Services
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Component
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Description
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Extractors
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The service is used for connecting to, extracting and loading metadata and logs from data sources and tools.
The extractors can also be deployed as a collector service for on-premise sources when using the K SaaS offering if access to between the on-premise source and the SaaS offering is not available.
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Profiler
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The service is used to identify and profile data assets and their usage. A set of proprietary algorithms are used to automatically match and analyse data assets over their lifecycle.
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Identity
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The service is used to integrate with the Enterprise Identity Management service to provide single sign on.
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Search
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The service provides fast, accurate and contextual search for all assets within K.
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Applications
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The service is used to access dedicated applications built to solve specific data problems. E.g. migration assessment, impact assessment etc.
Interfaces
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Component
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Description
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API
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This interface is used by applications and services to interact and access data managed by K.
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Web Portal
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This interface is used by end users (e.g. Data managers, analysts etc) to access K and its services.
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Notifications
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This interface is used to engage with end users via push notifications e.g. Email.
Stores
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Component
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Description
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Metadata
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The metadata store is used to store the details and relationships between data assets, reports, users, teams and other objects within the data ecosystem.
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Timeseries
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The timeseries is used to store each data asset, person or content item and its lifecycle over time.
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Index
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Each object in the data ecosystem is added to a search index to enable the contextual search service.
Inputs
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Component
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Description
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Data Sources
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Data sources (e.g. Teradata, Hadoop, Snowflake, SQL Server etc.) where data is stored and used by the Enterprise data teams. K has integrators for many on-premise and cloud data sources and can also ingest custom data sources through the K ingestion framework.
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Data Tools
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Reporting and Analytics applications (e.g. Tableau, Power BI etc.) used by the Enterprise data teams to create, manage and distribute content. K has integrators for common data tools and can also ingest custom data tools through the K ingestion framework.
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Identity
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Data Discovery
K is your metadata copilot for finding the right data to use for any use case.
K's unified Search Experienceenables business and technical users in your organisation to find the right data asset across databases, reporting platforms, ML feature stores, ETL tools and more.
To help optimise search results, K calculates Trust Scoreto improve search relevancy and ensure that the most suitable, up to date, and relevant data asset is promoted.
Users can create Filters using all of the metadata collected to customise their search experience. Saved filters that helps a user save time can be easily shared to colleagues.
K's best in class automated Lineage Maps make it easy to navigate the data ecosystem and understand upstream dependencies, or downstream impacts from any data asset. Users can also personalise their map with custom filters, highlight trusted paths and drill through to knowledge.
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Knowledge Management
K uses automated intelligence and smart engagement to build meaningful data profiles by analysing how your data is created and used.
From one Data Profile Page you can directly access all information specific to that data asset including, data issues, data quality, data lineage and more.
K automatically suggests glossary terms that can be linked to a Data Asset and can Auto Generate Descriptions through K.ai.
Crowdsourcing and collaboration is made easy through smart features that link your current workflow tools or Collaboration Channels(e.g. MS Teams, Slack or Discord). This blend of automation and crowdsourcing takes the boring manual work out of building your data catalog.
K knows who the top users of each data asset are. Through K, you can directly Ask Questions to Top Users that are most likely to know the answer. Similarly, when a decision, note, or change is added, K automatically notifies any recent users of the report, so they are kept in the loop.
Through key Usage Metrics so you can also understand how a data asset is used, and where it is in its lifecycle.
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Data Governance
K can help data governance teams fast-track the process to understand their data ecosystem and design the appropriate governance framework. Key features in K that help data governance teams include:
Automate Data Tagging - K can automate the tagging of data assets to collections (e.g. CDE and PII) to minimise data governance gaps
Dashboards and Insights - Utilise the usage dashboard to target governance effort on the most widely used assets. Data Owners and Stewards can view key governance and data quality metrics for the data assets they own
Data Change Timeline - When it's needed, data governance managers can drill into historical data change timeline to investigate what has happened or when data decisions were made.
Centralise DQ Results - Use K to capture DQ results from tools like Great Expectations and DBT. Alert the right people to DQ failures and leverage workflow tools like JIRA to address data problems.
Personalise governance notifications - Automate data governance change management by using K to inform impacted business and technical users on any relevant changes implemented by the data governance team (e.g. Updates to PI data policy)