Self-service BI governance is critical for long-term success and for avoiding an enterprise reporting data mess. The goal of Self-Service BI Governance is to provide information that is reliable and timely for Self-Service Business Intelligence reporting.
There is also a balancing act with ensuring data availability vs data quality depending on the use case. For the data scientists that need to immediately explore information, there is less control.
For enterprise-wide KPI, the financial or compliance related reporting, that data should be cleansed and blessed before letting the business go wild building reports with it. Ultimately the governance will be uniquely structured to best serve the unique needs of varied organizations and data-driven cultures.
A Self-Service BI Governance program is more than just a collection of technical features. The Governance frameworks usually address people, process, and technology in balancing information value with organizational regulatory, compliance, data privacy and ethics needs. BI governance features are built into many Self-Service BI tools, both self-service and traditional BI platform offerings.
Self-Service BI Governance and Information Delivery Strategy
- Areas of the company and overall BI where the Self-Service Business Intelligence tool will be integrated/used
- Mix of accessible Self-Service BI tools, why and when it makes sense to use them
- Personal, Team and Enterprise ETL and data cleansing
- Personal, Team and Enterprise Reporting and Dashboards
- On-boarding, Communication, Training and Support.
- Deploying Self-Service Business Intelligence Server within the Enterprise
- Integration with exiting portals, applications and business processes
- Self-Service Business Intelligence Server Technical Architecture
- APIs, and Utilities
- Self-Service Business Intelligence Server Implementation Best Practices.
- Roles and responsibilities
- Define the types of users, skill levels, types of access
- Security Access, Authentication, Authorization
- Creating Reporting Sites, Groups and Projects
- Define centralized, shared data access management and monitoring
- Data Source Level Security
- The Data Level Security
- Data Loads and Refreshes
- Management Permissions
- Distributed Environments
- High Availability
- Back Up and Recovery
- Automated Services and Processes
Development and Deployment
- Semantic Model or Shared Data Sources or Shared Data Sets
- Metadata capture and delivery
- Process for combining the Semantic Models into a Single Enterprise View
- Dashboards and Report Version Control/Change Control
- Developing, Testing , Reviewing, Approving, Publishing and Distribution
- Data Quality, Issue Management and Triage Support
Enterprise Data Lakes
In global BI market, we are seeing a growing number of organizations adopt enterprise data lakes. A Enterprise Data Lake is a hyper-scale repository for big data analytics workloads. Enterprise Data Lake enables you to capture data of any size, shape, type and ingestion speed in one single place for operational and exploratory analytics.
Enterprise Data Lakes removes slow, old school ETL complexities by ingesting and storing all of your data while making it faster to get up and running with batch, streaming, and interactive analytics.
Enterprise Data Lake also brings new considerations and capabilities for delivering even faster self-service BI to top data science and power user talent. The modern patterns are quite different from the traditional data warehousing methodology.
- Collect everything: A Enterprise Data Lake contains all data, both raw sources over extended periods of time as well as any processed data
- Dive in anywhere: A Enterprise Data Lake enables users across multiple business units to refine, explore and enrich data on their terms
- Flexible access: A Enterprise Data Lake enables multiple data access patterns across a shared infrastructure: batch, interactive, online, search, in-memory and other processing engine
Enterprise Data Catalog
If you have a Enterprise Data Lake, then you will want a data catalog for self-service BI. Even if you don’t have a Enterprise Data Lake, it is likely that there are hundreds or thousands of data sources in your organization that your business users struggle to find or even know about that would be invaluable to include in the decision making processes.
Enterprise data catalog supports an ever growing array of the data source types including but not limited to Azure SQL DB, SQL Server, Analysis Services, Reporting Services Reports, Oracle, Azure Storage, Azure Data Lake, Apache Hive Tables, Teradata, MySQL and SAP Hana. Enterprise data catalog also supports a growing list of Self-Service BI tools that will not be limited to Microsoft offerings.
Self-Service BI Governance Features
- Data source publish and search
- Linked data subject matter experts
- Added data and metadata context
- Data previews and profiles
- Linked data source documentation
- Open in my self-service BI tool of choice
For self-service BI to be successful, you can’t just inform the business users where the data sources reside and expect them to figure out the rest on their own. Often the database column names make no sense to a business user. The ERP databases like Dynamics, Salesforce, Oracle Financials or SAP are impossible to navigate even with the best Self-Service BI tools.
Locus IT has a thorough knowledge of Self-service BI Governance and provides services like Self-Service BI implementation, training and support. For more information on how Self-service BI can help your business please contact us.