Data Governance Software and Tools
This month, we discuss the use of software and tools in the data governance process.
As we indicated previously, data governance is an organizational transformation process, it is a cultural change process, and its success relies largely on the collaborative efforts of stakeholders. Nevertheless, the introduction of a software package with a set of tools to support the data governance process, when implemented appropriately and effectively, will make the process much more acceptable and enjoyable for the key stakeholders involved.
There are many software packages available from vendors, including: Collibra, Informatica, SAP, Prodago, SAS, IBM, Oracle, Alation, and Talend. In higher education, the software that seems to have a higher market share is Data Cookbook by iData Inc. Different universities have different programmatic objectives and needs for their data governance programs. Therefore, their choice for data governance software should be based first and foremost on their needs. The other factors that should be considered include cost, implementation support and after-implementation support provided by the vendor, peer recommendations, vendor experience with the ERP, and data warehouse systems in your organization.
From a needs assessment standpoint, the following are essential features and functionalities to consider when developing a profile for the data governance software you may need:
- Metadata management and data lineage mapping: Metadata is generated whenever data is created, acquired, or updated. Metadata helps locate and define a data set. Data Lineage describes data origins, movements, characteristics, and quality. A Data Lineage solution stitches Metadata together providing “understanding and validation” of data usage and risks that need to be mitigated.
- Data governance workflow management: A workflow consists of a coordinated and repeatable pattern of Data Governance decision and monitoring activities. It generally defines a sequence of tasks for the data governance team, and once approved can be automated.
- Data cleansing and quality control: Data cleansing is the process of detecting and correcting problematic records from a table or database. A good data governance tool should provide the capability to apply data rules and specifications to validate and identify problem areas.
- Data documentation: Data documentation should be provided at point of service in data reporting. A good data governance software should be able to embed data definitions and other documentations to help users understand and use various reports (i.e., Tableau or PowerBI).
Based on our experience, it is very important to collaborate with your peers before selecting a vendor. Investing some time to visit or have a long conversation with schools that have already implemented a particular software may give you a much more realistic view of what to expect before, during, and after implementation. When possible, always ask for an experienced implementation project manager with a track record of successful implementation. Vendors often consider an implementation schedule from the project portfolio management standpoint. Don’t let their schedule drive your implementation. Insist on an implementation schedule that is most convenient for your school and your collaborators.