Disparate Data Sources: Why Connecting Them Is Not Combining Them
Disparate data sources hold related data in incompatible forms. Integration gives you access — here's what still has to be agreed before you can combine them.
Data sources are disparate when they hold related data in forms that cannot be directly combined: different systems, schemas, identifiers, update cadences or definitions for the same thing. The usual remedy is integration — connect them, land them together, query across them.
That solves access. It does not solve combination. Two sources can be perfectly connected and still refuse to join, because one calls a campaign Q3_BRAND_NA and the other calls it q3-brand-northamerica, and nothing in the pipeline knows they are the same. Connectivity is an engineering problem with engineering answers. Comparability is an agreement problem, and it has to be settled before the data is created.
What makes data sources disparate?
Related data held in forms that cannot be combined directly.
Salesforce’s definition of disparate data (accessed 2026-09-11) covers the category. Underneath the label, five distinct things can differ, and they are not equally hard to resolve:
| What differs | Example | How hard to resolve |
|---|---|---|
| System | Ad platform vs CRM | Easy — a connector |
| Format | JSON vs CSV vs a database table | Easy — transformation |
| Schema | One has campaign_name, the other cmp | Moderate — mapping, done once |
| Cadence | Real-time vs nightly vs monthly | Moderate — a design decision about currency |
| Definition | Both have campaign, filled with different values | Hard — requires agreement between teams |
The first four are solved by technology and they are what the market sells against. The fifth is solved by people agreeing something, and it is the one that stops a combined view being usable.
Systems vs sources vs data
A system is where data lives; a source is what you pull from; disparate data is what you get.
The three words get used interchangeably in vendor material and mean different things, which matters when you are diagnosing where your problem actually is. Solutions Review’s treatment of what “disparate” denotes (accessed 2026-09-11) makes a similar distinction.
- Disparate systems is an architecture observation: you run many platforms. Almost every enterprise does, and on its own it is not a problem.
- Disparate sources is an access observation: the things you pull from are heterogeneous. Connectors address this.
- Disparate data is a meaning observation: what arrived does not line up. This is the one that survives the other two being fixed.
Teams often describe themselves as having a disparate-systems problem when they have a disparate-data problem. The distinction changes who should own the work — the first belongs to engineering, the third belongs to whoever can make two teams agree on a vocabulary.
What an example looks like
An ad platform, a CRM and a web analytics tool describing the same campaign three ways.
One Q3 brand campaign in North America:
| Source | How it records the campaign | What it is good at |
|---|---|---|
| Ad platform | Q3_BRAND_NA, its own numeric campaign ID | Spend, impressions, platform-attributed conversions |
| CRM | Q3 Brand North America, typed from the brief | Pipeline, revenue, deal stage |
| Web analytics | q3-brand-northamerica, from the UTM on the link | Sessions, on-site behavior, assisted conversions |
Each source is internally consistent and correct. Each holds something the others do not. Combining them is exactly the point of having all three, and there is no field on which they join.
This is the ordinary case, not a pathological one. Nobody made a mistake. Three teams recorded the same campaign in three reasonable ways, at three different moments, with no shared list to draw from.
Connected is not combined
Integration grants access; it does not make two records about the same thing recognizably the same.
“They connected everything and then discovered that connecting was the easy half.” — Mariela Sanchez, Product Manager, Claravine
The two properties are independent, and separating them is the most useful thing on this page:
- Connectivity is whether data can move between systems. Engineering owns it, tooling solves it, progress is visible, and a project can be declared finished.
- Comparability is whether records from different systems describe the same entities in the same terms. Nobody obviously owns it, no tool solves it alone, progress is invisible, and it is never declared finished because it was never chartered.
An integration program raises connectivity to complete and leaves comparability where it was. What the business then experiences is a combined data environment that still cannot answer combined questions, which reads as a failed integration and is not one.
Across Claravine’s enterprise customer conversations, cross-agency and multi-instance data fragmentation requiring a single source of truth is raised by 38 accounts. The framing is telling: teams ask for a single source of truth, which is a statement about agreement, while the projects they fund are about a single source of data, which is a statement about location.
The silo integration does not fix: Data silos explained — technical, organizational, and the third kind.
What has to be agreed first
The identifier, the allowed values, and who owns each.
Three agreements, and they are smaller than they sound:
- A shared identifier for each shared entity. One campaign identity that every system carries, issued once rather than typed three times. This alone resolves most combination failures in marketing data.
- Permitted values for the shared dimensions. Channel, region, business unit and audience drawn from the same lists, so two sources cannot legitimately hold different spellings of one value.
- A named owner per dimension. Someone who can add a value when the business needs one. Without this, lists ossify and people route around them, which is worse than free text because the workaround looks compliant.
One team described the result on the other side of that work.
“Claravine unified our campaign tracking strategy so we could make media decisions with consistent, richer data that spanned digital channels, helping us dramatically improve our campaign performance.” — unnamed, Fortune 500 hospitality company
The load-bearing word is spanned. The data was already present in each channel before; what changed was that it became comparable across them.
The standards layer: Explore data standards — agreed fields and permitted values, applied where records are created.
What integration is still for
Everything comparability does not cover: transport, latency, access and format.
Worth stating plainly, because the argument above could be misread as an argument against integration, and it is not.
Duality’s account of integrating disparate sources (accessed 2026-09-11) covers the real challenges in that work, and none of them go away. You still need data to move reliably, arrive fresh enough to act on, be reachable by the people who need it, and land in a queryable shape. Those are genuine problems with genuine solutions, and no amount of agreed vocabulary substitutes for a working pipeline.
The correct reading is sequencing, not substitution. Integration and comparability are both required; only one of them is usually scoped. Doing the agreement work first is cheaper, because retrofitting identifiers onto records already landed in a warehouse means reprocessing history rather than recording it correctly once.
Frequently asked questions
What are disparate data sources?
Sources whose data is about the same things and will not line up. Five things can differ — system, format, schema, cadence and definition — and the last is the one that stops a combined view working.
What is an example of a disparate system?
An ad platform and a CRM that both record campaigns under different identifiers. Both are correct, neither is wrong, and no field joins them.
What are the four main types of data sources?
Internal systems, third-party platforms, public data and partner data. The combination difficulty rises across that list, because you control the vocabulary of the first and none of the last.
Is a disparate database the same thing?
That is the DBMS sense — separate database instances rather than incompatible business definitions. Related word, different problem: the DBMS version is solved by architecture, this one is not.
If we integrate everything, is the problem solved?
No. Integration gives access; combination needs agreement on identifiers and values. A fully integrated environment can still be unable to answer a cross-source question.
Sources
- Salesforce, “What Is Disparate Data?” (accessed 2026-09-11) — the category definition.
- Solutions Review, “Defining the ‘Disparate’ in Disparate Data” (accessed 2026-09-11) — what the term denotes.
- Duality, “Integrating Disparate Data Sources: Challenges and Solutions” (accessed 2026-09-11) — the integration challenges that remain.
