- Larry Danberger
- Business Consulting, Business Process Improvement, Data Management
- No Comments
An example of how enterprise data moves through the organization from creation to use both within the business unit (primary purpose of the software application that works with the data) through to additional usages such as external reporting, dashboards, or alerts. The value chain specific to a business unit or line of business (often called the ‘owners’ or ‘power users’ of that data) is unique to each system, and would typically exist within the first 3 or 4 activities below.
Data becomes an asset to the organization separate from the original purpose, with the full value chain described below. In many instances, careful consideration and optimization opportunities should be given to the full life of the data. Ask us why, if it’s not obvious.
Some organizations may choose to skip components such as quality management, which can result in inferior and unreliable data and poorly accepted data and reports (information).
Originate: Data of interest is created or identified. This data will become part of core applications within the organization.
Gather/Process: Data of interest is gathered and stored within a system, with related processing for each LOB area of interest. These systems include automated software systems; manual software systems such as excel spreadsheets; and manual systems such as paper document filing.
Source: Results of the Gather/Process activity are retained in databases, excel spreadsheets, document management systems, or through other persistence means. Additional data sources for data not originating from LOB Gather/Process activity are identified and linked and/or imported. Other potential sources of data such as the results of scanning/converting unstructured data may also be included. Operational Data Store (ODS) may be utilized and receive updates from cleanse process.
Cleanse: An automated system continually providing data that has been cleansed as defined by the related business and quality rules, to data collection (ODS, warehouse, data lake, data mart, etc.) storage.
Cleanse – Data Quality Feedback: An automated system continually profiling data sources based on identified business and quality rules, and providing feedback (email, log file updates etc.) to source owners.
Curation: All enterprise data of potential interest is maintained in a system available for automated transformation.
Transformation: A system that provides the automated ability to perform data unification and preparation of enterprise data (from Curation) for preparing data into usable private and public data collections. Transformation to an entity happens once.
Provision – Private: An automated system continually providing grouping/processing of data into purpose defined collections for private use. May contain confidential data obtained from external companies. Provision of an entity can happen multiple times.
Provision – Public: An automated system continually providing grouping/processing of data into purpose defined collections for public use. Will not contain confidential data obtained from external companies. Provision of an entity can happen multiple times.
Consumption: Systems that provide the ability to generate or review data (from Provision) and data summaries in various styles including BI tools, data visualization efforts, dashboards etc. May include private information for internal consumers.
Knowledge: Manual activity of gaining knowledge through the use of the provided consumption systems, possible future systems could support recording, sharing, and reproducing how data was extracted at later dates.
Decision/Action: The end desired outcome of making decisions using the provisioned data. Possible future systems could support recording, sharing, and reproducing how data was extracted and utilized, what decisions were made, etc.