In the context of SAP BusinessObjects (BO) reporting, which practice is a key technique for tuning BO reports to improve their performance?

Introduction / Context:
SAP BusinessObjects reports often sit on top of large data warehouses or transactional systems. Poorly designed queries or universes can cause slow response times and heavy load on the database. Tuning BusinessObjects reports is therefore an important skill for Business Intelligence developers. This question asks you to identify a core performance tuning strategy rather than a cosmetic or counterproductive change.

Given Data / Assumptions:

• We are working with SAP BusinessObjects or a similar Business Intelligence tool that uses universes, queries, and report objects.
• The database underneath may contain millions of rows.
• Users require responsive reports that return results within reasonable time.
• We can adjust query design, filters, and database indexing.

Concept / Approach:
The main idea behind performance tuning in reporting tools is to reduce the amount of data processed and transferred and to leverage database strengths. This means designing universes and queries so that only relevant rows and columns are requested, using filters and prompts to restrict data, and ensuring that conditions align with indexed columns. Performing heavy aggregation on the database side rather than in the report can also help. These steps reduce I or O, network traffic, and processing time inside the reporting engine.

Step-by-Step Solution:
Step 1: Identify the tuning levers available in BusinessObjects: universe design, query filters, prompts, and optional aggregate tables. Step 2: Recognize that improving performance usually comes from reducing data volume and using indexes effectively. Step 3: Examine option a, which describes optimizing queries and universes, applying filters, and relying on database indexes to support conditions. Step 4: Compare with other options that suggest adding heavy visuals, disabling indexes, removing where clauses, or refreshing constantly, all of which would hurt performance. Step 5: Conclude that option a is the correct performance tuning technique.

Verification / Alternative check:
Performance best practices from vendors and consultants emphasize minimizing result sets, designing selective filters, using pre aggregated tables where appropriate, and making sure that join paths and indexes are efficient. They warn against retrieving wide tables or entire datasets when only a small subset is needed. This advice is consistent with the approach described in option a and contradicts the practices described in the other options.

Why Other Options Are Wrong:
Option b increases visual complexity but does nothing to reduce query time. Option c, disabling indexes, would force full table scans and slow down queries dramatically. Option d suggests ignoring where clauses, which almost guarantees extremely large result sets. Option e wastes resources by refreshing constantly, regardless of user requirements, which can overload servers instead of improving performance.

Common Pitfalls:
A common mistake is to focus only on front end formatting while ignoring the underlying SQL and universe design. Another pitfall is to add multiple derived objects and complex formulas inside the report when the same calculations could be pushed down to the database. Keeping performance in mind during universe and query design helps prevent slow reports and user frustration.

Final Answer:
A key tuning technique is to optimize queries and universes so that only necessary data is retrieved, using efficient filters and supporting database indexes, as stated in option a.