Note: No official product updates or articles were provided this week, so this roundup focuses on practical guidance, patterns, and reminders drawn from recent customer conversations and field experience rather than new feature announcements.
Oracle Analytics (OAC & OAS)
1. Hardening OAC / OAS Data Sources and Connections
TL;DR: Spend time tightening data source security in OAC/OAS: use dedicated service accounts, restrict network paths, and audit connection usage regularly. It’s one of the fastest ways to reduce risk without changing reports.
Even when there are no new product features to talk about, security and governance work in Oracle Analytics never really stops. A recurring pattern we see in OAC and OAS projects is that data source connections are created “temporarily” during development and then quietly become permanent, often with overly broad privileges.
Some practical steps you can review this week:
- Use least-privilege service accounts for each major data source (Autonomous, on‑prem Oracle DB, ADW, etc.). Avoid using DBA-level accounts for BI connections.
- Segregate connections by environment (DEV / TEST / PROD) and ensure credentials are rotated in line with your security policy.
- Audit unused connections in OAC / OAS and remove or disable anything that is no longer referenced by subject areas or data sets.
- Lock down network paths (VPN, private endpoints, or restricted IP lists) so that only the analytics service can reach the database, not the wider internet.
These changes are invisible to end users but materially improve your security posture. If you haven’t done a connection review in the last 6–12 months, schedule one as a mini “security sprint.”
Source: internal field experience and common customer hardening patterns (no public link provided).
2. Performance Housekeeping for OAC / OAS Dashboards
TL;DR: Before blaming the platform, profile your slow dashboards: check filters, aggregation levels, and data model joins. Most performance wins come from model and query tuning, not infrastructure changes.
Slow dashboards are still one of the top complaints in both OAC and OAS environments. In many cases, the underlying cause is not the server size or cloud resources, but the way analyses are built:
- Overly broad prompts (e.g. “All time” or “All customers”) that generate huge result sets.
- Complex logical table sources with unnecessary joins or multiple fact tables at different grains.
- Excessive calculated columns in the analysis instead of pushing logic into the RPD or database.
Some quick checks you can perform:
- Turn on query logging and inspect the physical SQL generated for your slowest dashboards.
- Confirm that filtering happens as early as possible (ideally in the database, not in the BI server after retrieval).
- Consider aggregate tables or summary views for common high‑level dashboards, especially on large transactional data sets.
Often, a single well‑placed summary table or a reworked prompt can cut dashboard load times from minutes to seconds.
Source: internal performance tuning checklists and customer engagements (no public link provided).
Fusion Data Intelligence (FDI)
3. Treating FDI as a Product, Not a Project
TL;DR: Successful FDI rollouts behave like products: they have owners, roadmaps, and feedback loops, not just a one‑off implementation plan.
Fusion Data Intelligence is still relatively new in many organisations, and we see a common anti‑pattern: it’s approached as a one‑time implementation project (“stand it up, build some dashboards, move on”). That mindset usually leads to shelfware.
Instead, teams that get value from FDI tend to:
- Assign a product owner responsible for the FDI roadmap, backlog, and stakeholder alignment.
- Run regular release cycles (e.g. monthly or quarterly) where they add or refine data products, KPIs, and dashboards.
- Maintain a feedback channel (surveys, office hours, or a dedicated Teams/Slack channel) so business users can request enhancements and report data quality issues.
- Align FDI content with Fusion application changes (new modules, new processes, or reorganisations) to keep analytics relevant.
If your FDI environment has gone quiet after go‑live, consider rebooting it with a product mindset: define a vision, pick a small set of high‑impact use cases, and iterate.
Source: implementation patterns observed across multiple FDI customer rollouts (no public link provided).
4. Data Trust and Lineage in FDI
TL;DR: FDI’s value depends on trust. Document where key metrics come from, who owns them, and how they map back to Fusion transactions.
One of the most frequent questions business users ask of any analytics platform is: “Can I trust this number?” With FDI, that often translates to understanding how a KPI relates to underlying Fusion ERP or HCM transactions.
Some practical actions:
- Create metric cards or catalog entries that describe each key KPI: definition, owner, calculation logic, and data refresh frequency.
- Maintain a simple lineage view (even if just in documentation) showing which Fusion objects feed which FDI subject areas and dashboards.
- Agree on authoritative sources for critical metrics (e.g. revenue, headcount) and avoid duplicating similar but slightly different definitions across multiple dashboards.
Even lightweight documentation can dramatically reduce confusion and support tickets, especially around period‑end reporting.
Source: best practices from analytics governance and FDI adoption work (no public link provided).
Autonomous Data Warehouse / 23ai
5. Housekeeping Jobs in Autonomous Data Warehouse
TL;DR: ADW largely manages itself, but you still need a few housekeeping tasks: statistics checks, partition maintenance, and archiving cold data.
Autonomous Data Warehouse (and the 23ai generation of services) removes a lot of traditional DBA work, but not all of it. We still see performance and cost issues when basic housekeeping is ignored:
- Statistics: While automatic statistics gathering covers most cases, large bulk loads or structural changes may warrant manual checks on key tables and partitions.
- Partitioning: Time‑based partitioning on large fact tables keeps queries fast and makes it easier to drop or archive old data.
- Storage management: Review which tables are rarely queried and consider moving them to cheaper storage tiers or summarising them.
Even in an autonomous world, a short monthly review of growth, query patterns, and partition health can prevent surprises later.
Source: ADW operations experience and common customer run‑book tasks (no public link provided).
6. Connecting OAC / FDI to ADW and 23ai Safely
TL;DR: When wiring analytics tools to ADW/23ai, prioritise private connectivity, strong authentication, and clear separation of roles between reporting and ETL accounts.
As more customers standardise on ADW or 23ai as their analytics backbone, the integration patterns with OAC, OAS, and FDI become critical. Some recurring best practices:
- Use private endpoints or restricted network access where possible, avoiding open public IP exposure.
- Separate ETL/ELT accounts (for loading and transformation) from reporting accounts (read‑only, least privilege).
- Leverage database roles aligned with subject areas or business domains, rather than granting wide‑ranging privileges to a single BI user.
These patterns keep your data warehouse flexible while maintaining a clear security boundary between ingestion, transformation, and consumption layers.
Source: integration patterns from OAC/OAS/FDI & ADW projects (no public link provided).
SQL Tip of the Week
7. Using MATCH_RECOGNIZE for Simple Time‑Series Gaps
TL;DR: Oracle’s MATCH_RECOGNIZE clause is not just for complex pattern detection; it’s also a neat way to spot missing dates or breaks in time‑series data.
Suppose you have a daily sales table and want to find gaps in your date sequence for each product. In Oracle Database (including ADW/23ai), you can use MATCH_RECOGNIZE like this:
SELECT product_id,
start_date,
end_date,
gap_days
FROM sales_daily
MATCH_RECOGNIZE (
PARTITION BY product_id
ORDER BY sales_date
MEASURES
FIRST(sales_date) AS start_date,
LAST(sales_date) AS end_date,
(LAST(sales_date) - FIRST(sales_date)) - (COUNT(*) - 1) AS gap_days
ONE ROW PER MATCH
PATTERN (seq)
DEFINE
seq AS 1 = 1
)
WHERE gap_days > 0
ORDER BY product_id, start_date;
What this does:
- Treats each product’s time series as a single pattern (
PATTERN (seq)). - Calculates the difference between the first and last date and compares it to the number of rows.
- Any surplus days indicate missing dates (
gap_days > 0).
This is often simpler and more efficient than generating a calendar table and left‑joining, especially when you’re just looking for gaps rather than listing every missing date.
Source: Oracle SQL pattern matching features in production use (no public link provided).