What is Self-Service BI
Self-service business intelligence is simply the empowerment of business owners/users to generate business insights on the fly by allowing them to create their own reports. Often times, Self-service BI refers to the ability to generate reports from published data sets but it also can mean extension of this ability to create datasets to business users.
Why you need Self-service BI?
Business users are best placed to make the right data-driven decisions provided they have access to relevant insights and underlying data; but this is often not the case. Self-service business intelligence (BI) tools allow them to analyse business data themselves and present the information from that analysis without approaching IT or BI teams. The two most important benefits of Self-service business intelligence are:
Quick Validation of Analysis – It helps in optimizing the decision-making capability.
Reducing the load of IT Department – By leveraging Self-Service BI tools, business users get the best out of filtering, sorting, analysing and visualizing data without reaching out to IT as well as BI teams.
What factors/risks to consider?
- Data Quality: The basis of any successful BI project is the quality of underlying data. Therefore, accuracy of data matters more prior to using data for analysis.
- Data Security: Business units handling their own data marts lose track of protecting data. Since data could lie in Spreadsheets, BI tools and other devices, an organization may find it difficult to gain control over data lineage.
- Data Democracy: Self-service business intelligence necessitates democratization of data. Business users need a flexible and a customised environment to exchange, collaborate and evaluate data presented in a manner that is relevant to them.
- Volume of Data: Business users should be aware of how to handle data marts when there are changes in data. When data becomes huge to deal with, business users are pushed to seek help from IT in order to override problems with data.
- Investing in Business User: Self-service BI tools come with a lot of in-built features and business users ought to get trained to use the tool in an effective manner.
- Tool Evaluation: Picking the right tool is another concern to be addressed to promote successful Self-service business intelligence adoption. Some key features pertaining to the tool worth considering are:
- Augmented analytics-enabled: Features such as the chatbots, intuitive wizards and NLP capability emerge as intelligent assistants to BU guiding them to acquire as well as explore data, reuse published dataset or even offer suggestion to provide insights.
- Data driven alerts: These render threshold breaches on mobile as well as e-mail, and in turn, serve to reduce the need for constant monitoring.
- Sophisticated data management capabilities: Support Data lineage, sharing of data sets etc.
Understanding Traditional vs Self-service BI
| Traditional BI | Self Service BI |
|---|---|
| Fully centralized reporting | Business Users own reporting |
| Tightly controlled & Governed | Full access to data |
| IT owns design & development | Metrics development hinges on their understanding of data |
| Business gives requirement | Usually designed for quick reporting |
| PROS | PROS |
| Single-version-of-truth reported & distributed | Less dependent on IT |
| Data Consistency | Quicker data analysis and reporting |
| Business can focus on their job | |
| Performance & load tested fully | |
| CONS | CONS |
| Delayed time-to-market (Project Approvals, SDLC Process etc.) | Data Consistency issues |
| Potential Business opportunities lost | Data Marts could be in the form of multiple spread- sheets |
| Poorly written / Generated queries impact production work loads |
Role Distribution in Self-service BI environment:
| Designated Area | Business Users | IT Experts |
|---|---|---|
| Data Cleansing | | |
| Data Transformation | | |
| Data Integration | | |
| Report Creation | | |
| Security | | |
| Data Analysis | | |
| Application Training | | |
| Infrastructure | | |
| Application Performance Tuning | |
Common pitfalls while Implementing Self-service BI:
Poor adoption by business users: Business users often try to avoid adoption of Self-service BI initiatives owing to lack of technical skills necessitated to work with it. This can be overcome by providing training to business users.
Data level issues: Data quality and security is the main challenge faced while adopting Self Service BI. Without a series of data governance rules, data security and quality could be compromised. Implementation of data governance framework through data management systems add real value to the quality of enterprise data, ensures data protection and eliminates access control issues thus enabling data democratization.
Disorganisation of calculating measures: Since there may be no direct ownership for data marts, business units follow their own definitions and metrics, which causes problem in comparing reports from different business units.
The organizations today understand the value of data and the need for data-driven decisions on the fly. Business users have the business knowledge to make these decisions and Self-service business intelligence is the means to enable them in doing so. With Self-service BI tools continuously improving and innovating, rapid adoption of Self-service business intelligence is a common trend today.
