Cloud Ecosystem of Managed Services
Data Governance
Data Governance capabilities refers to a mechanism for supporting the CDO and business organizations in managing and tracking data policy, change management; stewardship roles and responsibilities; data lineage; metadata/dictionary management; data usage; notifications.
Description
The Data Catalog is an organized inventory of data assets in the organization. It uses metadata to help FAS manage its data, and helps data professionals collect, organize, access, and enrich metadata to support data discovery and governance. The data catalog stores dataset and attribute-level metadata and enables data stewards to create and maintain that metadata for existing and new data sets.
Key Capabilities Include:
- Data search & discovery - Find relevant information within the huge volumes of enterprise data, contextualize it, and determine how the data can be accessed/used
- Curation & governance - Ensure analytics and insights are derived from the best, most trusted data. By applying governance at the point of data use, the data catalog reduces misuse of data and ensures compliance with agency and regulatory policies
- Collaboration & analysis - Through wiki-like articles, ratings, reviews, and conversations, the data catalog facilitates collaboration among an increasingly global and remote workforce
Maturity:
FCS believes this capability has reached a level of maturity in FCS where it can be used and deployed in a limited capacity or upon special request from a customer/tenant.
Technology
Additional Documentation
Description
The Data Quality service provides the necessary capabilities to assess the validity, accuracy, completeness, correctness, and timeliness of the data. The service supports data users, as they are evaluating new data sets and production applications and data pipelines as they are performing CRUD functions and processing data.
Key Capabilities Include:
- Data profiling - Generate descriptive metadata about a data set, (e.g. schema, data types, field lengths, value distribution, valid values, etc.)
- Rule definition - Specify data quality rules based on prescriptive, (e.g. business rules) and descriptive, (e.g. technical) constraints and specify applicability, (e.g. full data set, sampling, etc.)
- Rule execution - Invoke rules through data pipelines/orchestration solutions and support corrective data quality, including logging of data corrections and rule execution results.
- Rule lifecycle management - Modify rules and track changes over time.
- DQ Results Reporting/Notification - Includes DQ dashboard results, alerts/notifications for users and systems
Maturity:
FCS believes this capability has reached a level of maturity in FCS where it can be used and deployed in a limited capacity or upon special request from a customer/tenant.
Technology
None
Additional Documentation
Description
The Master Data service provides capabilities to rationalize core data domains and create an authoritative data sets that can be exposed and leveraged across systems and business domains.
Key Capabilities Include:
- Unique Identifier Creation and Management - Create/apply unique IDs to drive consistent identification/linking of master data elements across systems
- Data Standardization - Apply consistent formatting and correct data inconsistencies of master data elements, (e.g. address formatting standardization)
- Exact Matching - Identify master data relationship across systems based on byte-for-byte matching values
- Fuzzy Matching - Identify potential master data relationships across systems based on similar values and/or complex logic across multiple attributes
- Recommendations - Show potential master data record matches across systems and allow users to determine if they are valid or invalid matches.
Maturity:
FCS believes this capability has reached a level of maturity in FCS where it can be used and deployed in a limited capacity or upon special request from a customer/tenant.
Technology
None
Additional Documentation
Description
The Data Lifecycle Service provides the mechanism to manage data storage in alignment with data retention, archiving, and purge requirements to reduce data sprawl and storage costs
Key Capabilities Include:
- Lifecycle Definition - Define the conditions under which data can be retained, archived and or purged
- Time Driven Lifecycle - Move data to lower tiered storage based on elapsed calendar time since the data was created or modified.
- Utilization Driven Lifecycle - Move data to lower tiered storage based on elapsed calendar time since the data was last touched.
- Intelligent Tiering - Move data between storage tiers based on utilization/access patterns
Maturity:
FCS is exploring the capabilities in this area. It is is currently in as a proof of concept phase and not ready for broad consumption.
Technology
Additional Documentation
Description
The Data Lineage service enables understanding, recording, and visualizing data as it flows from data sources to consumption. This includes all transformations the data underwent along the way—how the data was transformed, what changed, and why. Data lineage shows the history of the data you're looking at today, detailing where it originated and how it may have changed over time. It's a reflection of the data life cycle, the source, what processes or systems may have altered it, and how it arrived at its current location and state.
Key Capabilities Include:
- Lineage Mapping- Graphical representation of the data flow between source and target
- Lineage Details- Description of data transformations applied to the data through each step of the data processing pipeline
- Design-time Lineage- Lineage based on the intended process flow when the data pipeline was being created
- Run-time Lineage- Lineage based on the actual data pipeline execution
Maturity:
FCS is exploring the capabilities in this area. Currently only a proof of concept deployment is occuring with a single customer/tenant or with a handful. Use of the capability is still ad hoc and has yet to be baselined or have cost estimates established.
Technology
Additional Documentation
Description
The Reference Data service provides a means to manage bounded, common data sets across data domains to drive consistency. Reference data is slowly changing by nature and is used to group or organize other data. Within OLAP models, reference data is often represented through dimension tables. Managing reference data centrally ensures the ability to consistently group and organize data which enables easier cross-domain analytics.
Key Capabilities Include:
- Reference Data Inventory - Store and manage reference data sets centrally
- Reference Data Publication - Generate and expose authoritative copies of reference data to support different data consumers
- Change Notification - Create systematic alerts when reference data records are created, modified, or deleted
- Reference Data Harmonization - Standardization of multi-source reference data through business rules applied as transformation logic.
Maturity:
FCS is exploring the capabilities in this area. Currently only a proof of concept deployment is occuring with a single customer/tenant or with a handful. Use of the capability is still ad hoc and has yet to be baselined or have cost estimates established.
Technology
None
Additional Documentation
Description
The Data Policy service provides a centralized location to define and manage the rules for users interaction with data. Stewards can map the rules to specific data sets and identify which policies are being applied to what data and user groups.
Key Capabilities Include:
- Policy Definition - Specify rules, conditions, warnings mapped to data sets and elements
- Policy Execution - Based on defined rules manage users access/interaction with data consistent to the policy definition
- Policy Audit - Detailed view of policy definition and how it is applied to specific data sets and elements
Maturity:
FCS is exploring the capabilities in this area. It is is currently in as a proof of concept phase and not ready for broad consumption.
Technology
Additional Documentation
Description
The Sensitive Data Detection service provides an automated means to identify data elements that require additional data protection or special handling based on organizational or regulatory rules.
Key Capabilities Include:
- Pattern Matching - Identification of sensitive data elements based on attribute structure/format
- Metadata Matching - Identification of sensitive data elements based on attribute name or definition
- Rule Definition - Creation of detection rules based on business-defined conditions
- Catalog Integration - Automated updating of data catalog with tags for sensitive data attributes
Maturity:
FCS is exploring the capabilities in this area. It is is currently in as a proof of concept phase and not ready for broad consumption.
Technology
None