Knowledge Management Domain
The Knowledge Management Domain provides standards and policies on organizing, analyzing, and presenting information. Primary topics covered in this section include the DHS' Data Warehouse, Metadata repository, electronic document formats, web publishing, workflow system processes and document management system processes. The standards and policies in this domain will increase productivity and bring about a higher quality of service by providing a de facto standard for accessing, analyzing, and maintaining DHS' information.
The Knowledge Management Domain includes the following subsections:
- Business Intelligence (BI) — A technology-driven process for analyzing data and presenting actionable information to help business users make informed business decisions. BI should be related to strategic, tactical, and operational business objectives. A data warehouse is often the foundation that business intelligence is built upon.
- Online Analytical Processing — Business analysts use Online Analytical Processing (OLAP) tools to access, analyze, and share information interactively. OLAP tools provide multidimensional analysis, graphing, trend analysis, calculating, and summarizing capabilities.
- Dashboards and Visualizations — Business analysts and supervisor level staff use dashboards and visualizations to provide ‘at-a-glance’ information on the status of the department and the programs it oversees. Dashboards and visualizations also are used to provide detail level review through specific, guided, drill-down functionality.
- Query and Reporting — Business analysts use query and reporting tools to perform ad hoc queries against the data warehouse in order to view key performance information about DHS. This section provides all standards and policies around the query and reporting tool used at DHS.
This section provides all standards and policies around the query and reporting tool used at DHS.
- IBM Cognos 10 BI - Report Studio
- IBM Cognos Analytics
- IBM Cognos Access Manager
- OLAP - Cognos PowerPlay, Impromptu, Transformer
* Data Analysis
The Data Analysis Unit works with users to identify how they can turn their data into useful information to monitor and manage their programs. The unit assists users in identifying and locating the data needed to perform their job functions and works with upper management to identify key performance indicators (KPIs) and performance metrics.
The unit uses analytics to uncover relationships and patterns within large volumes of data. This Unit is responsible for creating dashboards, scorecards and metrics management as well as reviewing this type of work when it is developed by contractors.
Data Analysis Policy
Data Analysis Tools
- Tableau — The main tool used by the Data Analysis Unit is Tableau. Dashboards and stories are created in Tableau and published to the Commonwealth Tableau servers. The Commonwealth maintains Tableau servers inside and outside the firewall including one site that is used for public dashboards.
- * Data Warehouse — A data warehouse, also known as an Enterprise Data Warehouse (EDW), is an application used for reporting and data analysis. An EDW is a collection of integrated data from one or more disparate online transactional processing (OLTP) application sources contained in a structure specifically and solely designed to support analysis and reporting. This structure will have little resemblance to OLTP source system databases and is used for creating analytical reports by and for knowledge workers throughout the enterprise. The EDW should support a consistent strategic view of data across the enterprise.
Templates and Forms
- Data Modeling - ERwin
- ETL - Informatica
- Database - Oracle
* Metadata - information about data (that is, data definitions, data aliases, where OLTP and OLAP data reside). The metadata repository is an important aspect for a successful data warehouse effort because it contains all the information about the data and processes used to populate and access a data warehouse.
- Enterprise policy ensuring the capture and maintenance of metadata and to establish minimum requirements for the specification and documentation of metadata for all enterprise systems development initiatives.
Examples and Best Practices
Templates And Forms
* Enterprise Content Management - Deals with strategies, methods, and tools used to capture, manage, store, preserve, and deliver content and documents related to organizational processes to employees, business partners, and clients of DHS.
- Enterprise Content Management - DocuShare
- Imaging - DocuShare, FileClerk, Conveyor, eCopy
- Business Process Management - DocuShare
- Web 2.0 Technologies - DocuShare
* Geographic Information System (GIS) Mapping - Provide a means for users to store, analyze, and display information about places on a desired map. The key components of GIS software include tools for entering and manipulating geographic information, such as addresses or political oundaries. Such tools provide an intuitive user interface and allow the effective use of maps to analyze, query and print geographical information.
- ESRI - ArcIMS, ArcGIS, RouteMap
- Innovative Systems - iLytics, GeoOnline
* Information Management - Deliverables developed for Information Management (IM) projects require the usage of deliverable templates linked to below. Any project containing an IM reporting component must use these templates within the scope of that IM sub-piece.
Templates And Forms
- Requirements Definition Phase
- GSD Phase
- DSD Phase
- Development Phase
- Software Integration and Testing Phase
- Acceptance and Installation Phase
* MS Project file - we suggest saving the document then opening with MS Project.
** Erwin file - save the document then open with Erwin.
* Operational Data Store - An Operational Data Store (ODS) is a "subject-oriented, integrated, current, volatile collection of data used to support the tactical decision-making process for the enterprise." An ODS contains current or near term data and very little archival or summarized data. As operational data is changed, the ODS data get changed - no history of changes is kept. In practice, an ODS tends to be more reflective of source structures in order to speed implementations and provide a truer representation of production data.
Users then use supported tools to query the ODS directly in order to get near real-time information to support operational processes and decision-making. The information can also be moved into a data warehouse in order to support higher levels of aggregation and more advanced analysis. An ODS may contain 1 – 30 days of information while the Enterprise Data Warehouse (EDW) typically contains years of data. This timeframe is dependent on the update schedule of the subject area into the EDW. Currently most subject areas are updated weekly or monthly. Therefore, the ODS, either supports the timeframe in between the transactional data to the weekly update into the EDW, that being 7 days of data held in the ODS, or the timeframe for a subject area that is updated monthly into the EDW, that being 30 days of data held in the ODS. An ODS should not be used in lieu of a more frequent update to the EDW when requirements dictate a more frequent EDW update.