You need controlled data exchange through available APIs.
API integration →OXIAE / INTEGRATIONS & DATA
Data integration.
Bring scattered data together with clear definitions and quality checks.
Discuss your projectConnecting systems: where does your question start?
Your question starts with customer identity, contacts and updates between CRM and other systems.
CRM integration →You need consistent, complete and usable data from several sources for analysis.
Data integration (this service)UNDERSTAND THE SERVICE
How do you bring data from different sources together reliably?
Data integration makes information from multiple sources usable together by aligning meaning, structure and freshness. An illustrative chain imports customer details from the CRM, orders from scheduling and payments from accounting into a reporting environment. Shared customer and order IDs connect the records; ownership rules identify the source of truth for each field. The pipeline checks missing values, duplicates and differences from the source before publication. An API integration may supply the data. Software integrations focus primarily on carrying out transfers between applications.
When it is a good fit
This is useful when teams calculate the same figures differently, combine information from multiple applications or cannot tell how current a report is. I start with a specific information need and work back to sources, definitions and checks. For fragmented customer records, CRM integration can provide a bounded starting point.
When to take a different approach
Central storage does not resolve unclear definitions or missing source data by itself. If one supported export answers the question, a continuously running data platform or real-time architecture may be unnecessary.
PRACTICAL DECISION GUIDE
Data integration needs to preserve meaning and provenance
Data integration requires consistent meaning across sources. I check identifiers, units, time zones and record granularity. An order line and an order total can both contain revenue; combining them without explicit rules can easily double-count it. [1]
We document transformations and provenance and separate invalid records from usable data. Loading includes paths for late corrections, reloading and schema changes. For reporting, the data model follows the intended analysis so a new filter does not silently introduce a different definition of the same metric. [2]
Questions to settle first
- What exactly does one record represent in each source?
- How are late corrections and deletions detected and processed?
Make acceptance measurable
- A data dictionary with identifiers, units, provenance and transformations.
- Reconciliation of counts and totals after loading and subsequent corrections.
Compare your options
Choose based on the task, the people involved and what you can maintain.
| Approach | Useful when | What to consider |
|---|---|---|
| One-off import or consolidation | A bounded analysis or migration needs a checked snapshot. | Results become outdated after the chosen cutoff; repetition and corrections need separate arrangements. |
| Scheduled batch processing | Reports need to refresh daily or at other agreed intervals. | There is a known delay; checks must use the same period and source selection. |
| Event-based processing | A process needs current data soon after a source changes. | Requires handling ordering, late messages, missed changes and reconciliation afterwards. |
What I need to start
- An information need with definitions, such as eligible order statuses and whether amounts include tax or reversals.
- A source register with owners, permissions, available history, refresh times and retention requirements.
- A data model and mappings for identifiers, dates, time zones, currencies, units and missing values.
- Agreements on field ownership, duplicate records and corrections to previously imported data.
- Access limited to the required permissions and an owner for rejected data, alerts and source schema changes.
How to assess the result
- A published record or figure can be traced to its sources, mapping and processing period.
- Completeness checks compare counts and relevant totals for the same source selection; differences remain visible.
- Invalid and duplicate records are visibly rejected or handled according to agreed rules, without silent data loss.
- Reprocessing or correcting data does not create unwanted duplicates and replaces the intended records.
- The latest successful source processing and agreed freshness are visible. Failures trigger an alert and a documented recovery route.
SCOPE & INVESTMENT
What determines the cost?
A useful estimate starts with the scope. I distinguish development work from recurring usage and operating costs. These are the main drivers:
- Number of sources, data models and agreements needed for definitions and quality.
- A pilot for one information need differs from production with historical reprocessing, multiple consumers and recovery checks.
- Data volume, freshness requirements and the extent of incomplete, duplicate or changing source data.
- Recurring storage, processing, monitoring and maintenance when source fields, definitions or permissions change.
APPLICATION AND SCOPE
Your challenge.
A practical application.
Illustrative scenario; not a published client case.
Departments report different figures because their sources and definitions differ.
For analysis, exchanging data is not enough: definitions, completeness and traceability must also be addressed. A business intelligence consultant helps define the calculations the data must support. A dashboard then displays the result, with checks on its source and refresh time.
THE QUESTION I ASK FIRST
Which source is authoritative, and what exactly do the key fields mean?
Agree the first step
Use the prerequisites, acceptance checks and cost factors above to define your assignment. Agree who can approve the outcome and who will maintain the solution. The project approach explains discovery, delivery and handover.
Read the project approachQUESTIONS ABOUT THIS SERVICE
Your questions.
Clear answers.
How does data integration differ from a software integration?
A software integration carries out agreed transfers between applications. Data integration also aligns sources using common definitions, identifiers and quality rules. A data chain may require both.
How do you check that all data has arrived?
We compare source and destination using the same selection, period and cutoff. Checks may include record counts, unique IDs and relevant totals. Differences are investigated; matching counts alone do not prove that the content is correct.
Can existing reports and systems stay in place?
They can if they provide suitable access and output. A first step may be a checked export or one pipeline for an existing report. Replacement follows only when the limitations of that environment require it.
How do you handle later corrections and failures?
We define which changes must be imported again and which source remains authoritative. A pipeline retains its processing position and exposes errors. Operating agreements describe recovery, reprocessing and checks of the resulting data.
Sources and technical basis
These primary sources explain the technical principles behind this guide. The project recommendations are OXIAE’s interpretation; they do not imply endorsement by the publishers. Product documentation applies to the named platform.
- Data on the Web Best PracticesW3C
Principles for provenance, versioning and data quality.
Link and relevance checked: - Understand star schema and the importance for Power BIMicrosoft Learn
Explanation of granularity and fact/dimension models.
Link and relevance checked:
YOUR NEXT STEP
Prepare your project conversation.
Exploring Data integration? Use the project checklist to describe your task, systems and intended outcome. Collect open questions and download a brief for your conversation.
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