DATA — data analytics
Data analytics
Your database has been collecting sales, customers, inventory and production for years. With AI we extract two things from it at once: answers for decision-making — and a leaner, faster database that no longer drags dead weight along.
Years of data in the database: analysis and slimming
A typical situation: the system has been running for ten or fifteen years and the database carries everything that piled up along the way. Daily operations touch only a fraction of it — yet the rest slows down queries, inflates backups and occupies expensive storage. And nobody knows precisely where the line runs between "data we need" and "data we merely have".
This is where AI steps in: it works through what a human would spend weeks on. It reads how the data is actually used — which tables, partitions and periods are really queried, which applications touch them and what has been lying untouched for years — and prepares the ground for the decision on what stays within reach and what belongs in the archive.
What the business gains
Lower resource demands
Less data in the active database means less storage, less memory and possibly a smaller server — or room to grow without buying more.
Faster work
Queries and reports scan a fraction of the original volume. Nightly jobs, closings and everything that "doesn't finish by morning" speeds up too.
Shorter backups and restores
Backing up 400 GB is a different discipline than backing up 2 TB — and in a disaster, the difference is measured in hours of recovery.
Cheaper changes
Upgrades, migrations and cloud moves are far easier to plan and test over a lean database — with less risk.
- AI analysis of actual data usage — from audit, AWR and segment statistics
- Splitting data into hot, warm and cold — based on facts, not guesses
- Archiving cold data: still available for queries, no longer a burden on operations
- Partitioning and compression of the rest — order that survives years of further growth
- AI check before any change: nothing referenced by applications gets archived
- Retention rules aligned with legislation — accounting and tax periods, GDPR
The data foundation: model, quality, performance
An analysis is only as good as the data beneath it. Before we draw any charts, we put the data in order — so the numbers in the report match reality and queries don't take minutes.
- Analytical data models — star schemas, aggregations
- Materialized views for instant answers over large volumes
- Data quality: duplicates, gaps, inconsistencies — found and cleaned
- Consolidating data from multiple systems (ERP, e-shop, production)
- Partitioning and index strategies for analytical workloads
Reporting and dashboards
The numbers the business needs to see should live in one place and always be current — not in a spreadsheet someone assembles by hand once a month.
APEX dashboards
Interactive overviews right on top of the database — sales, margins, inventory, production. No extra infrastructure, no BI tool licences.
SQL analytics
Oracle SQL analytic functions — year-over-year comparisons, moving averages, rankings, shares. Fast, straight at the source.
Scheduled reports
Regular statements delivered by e-mail in an agreed structure — every morning, every week, after each closing.
Ad-hoc analyses
One-off questions like "what happened to our margin in March?" — answered from real data, not impressions.
Questions in plain language — Select AI
Oracle 26ai understands questions in natural language: you ask like a human, the database translates the question into SQL by itself and returns the answer. We set it up so it knows your schema — and sees only what it is allowed to see.
- Connecting Select AI to your schemas and code lists
- Access control — everyone queries only their own data
- Verifiability: the generated SQL can be shown and explained
- Suitable for management and sales alike — no SQL knowledge needed
Semantic search and RAG
AI Vector Search finds by meaning, not by exact word match — in contracts, service tickets, product descriptions or e-mail threads. And with RAG built on top, AI answers from your documents, not from the internet.
- Vector indexes over text right inside Oracle — no extra database
- Search by meaning: "complaints about the gasket" also finds "oil was leaking"
- RAG architectures: company documents as a knowledge base for AI
- Combined with classic SQL — semantic and exact conditions in one query
- Data stays in the database, under your access control
Predictions with in-database machine learning
Oracle Machine Learning trains models right where the data lives — nothing needs to be exported anywhere. From historical data, the database learns what comes next.
- Sales and demand forecasting — input for purchasing and planning
- Customer scoring: who is likely to leave, who to offer more
- Anomaly detection in transactions and operational data
- Segmenting customers and products by behaviour
- Results as ordinary SQL columns — instantly usable in reports and APIs
The entire analytics layer runs inside the database — your business data never leaves your environment, and the same access rules, encryption and auditing apply to it as to everything else.
You already have the data. Let's turn it into answers.
We start with a pilot over one area — sales, for instance. You will see the first dashboards and answers within a few weeks.
Monitoring · Tuning · Security · AI