Data analytics challenges: what they look like and what to fix first
The common data analytics challenges, the symptom each one shows a business leader, who should own the fix, and the order to fix them in.
The most common data analytics challenges are numbers that disagree between reports, data split across tools, poor data quality, metrics nobody has defined or owns, stale data, and an analytics team that cannot keep up with requests. Security, privacy and trust in AI-generated answers sit on top of all of them.
Every list of challenges names roughly these. What a list rarely says is how each one shows up for the person who has to decide, who should own the fix, and which to fix first, because the challenges are not independent: most of them end in the same symptom, two numbers for the same thing.
Note: Originally published in October 2024. Fully updated in October 2026 with a conversions comparison on demo data, a symptom-to-owner table and a fix order.
The challenge behind most others: numbers that disagree
The challenge behind most others: numbers that disagree
Leaders rarely see a data silo or a quality problem as such. They see two teams bring two different numbers for the same thing, with no way to tell which one to believe. That is where most challenges surface.
The clearest everyday case is conversions. On our [Demo] E-commerce project, which pairs real ad platform connector data with a generated store, the platforms claimed 933 conversions for July to September 2026 where the store recorded 573 orders from paid clicks, 489 of them completed. The single source of truth pipeline guide walks through that gap. What matters for a leader is how uneven it is:
| Platform | Platform conversions | Store orders from its paid clicks | Platform claims |
|---|---|---|---|
| 45 | 21 | 2.1× | |
| 383 | 202 | 1.9× | |
| TikTok | 232 | 142 | 1.6× |
| 111 | 81 | 1.4× | |
| Microsoft (Bing) | 144 | 117 | 1.2× |
| 17 | 9 | 1.9× | |
| X | not reported | 1 | – |
It changes budget decisions, not just totals. Divide each platform’s Q3 spend by each count:
| Platform | Cost per platform conversion | Cost per store order |
|---|---|---|
| $34 (1st) | $73 (2nd) | |
| $40 (2nd) | $76 (4th) | |
| TikTok | $43 (3rd) | $71 (1st) |
| Microsoft (Bing) | $62 (4th) | $76 (3rd) |
| $78 (5th) | $107 (5th) | |
| $242 (6th) | $457 (6th) |
On the platforms’ own numbers, Facebook is the second-cheapest channel. On the store’s orders it is fourth, and TikTok moves from third to first. A budget shift decided on the left column moves money in a different direction from one decided on the right.
No single discount turns platform numbers into store numbers. Each platform counts by its own attribution window (how many days after an ad it still credits a sale), often including people who saw an ad without clicking, and two platforms can claim the same purchase. A leader who compares channels on platform-reported conversions is comparing five different rulebooks. The size of the gap here is a property of the demo; the unevenness is the point.
Neither side is lying. Both are correct answers to different questions, and nobody wrote down which question the business is asking. That is the challenge in one table: not missing data, but missing agreement on what the number means.
The challenges, the symptom and who owns the fix
The challenges, the symptom and who owns the fix
| Challenge | What a leader sees | Root cause | Who should own the fix |
|---|---|---|---|
| Numbers that disagree | Two reports, two numbers, no way to choose | Each report defines the metric itself | A business owner per metric |
| Data silos | Questions that “need a project” to answer | Sources never brought into one place | Data engineering |
| Data quality | A number that changes after it was presented | Duplicates, broken joins, missing keys | The analyst who owns the dataset |
| Undefined metrics | Meetings spent arguing about definitions | Nobody decided what “customer” or “revenue” means | Business owner, with the analyst |
| Stale data | Decisions on last week’s picture without knowing it | Inputs refresh at different times, no “data through” date | Data engineering, with a visible freshness check |
| Team capacity | A backlog of report requests, weeks to an answer | Every question needs an analyst to write a query | Head of data |
| Trust in AI answers | Assistants answering with confident, unchecked numbers | AI writing its own SQL over raw tables | Analyst, by controlling what AI can query |
| Security and privacy | Hesitation to share data at all | Access not designed per role | Data owner and security |
The owner column matters more than the challenge names. A challenge with no named owner stays on every list, every year.
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A five-question check
A leader can test their own setup with five questions. Each “no” points to a challenge above.
- When two reports show different numbers for the same metric, can someone explain the difference within an hour? If not, definitions are not written down. See one source of truth for your numbers.
- Does every number you review show the date its data runs to? If not, you are reading stale data some of the time without knowing. See data freshness.
- Is there a named person for each key metric? Not a team, a person. See who is responsible for data quality.
- Can a manager get a standard number without filing a request? If every answer goes through an analyst, capacity is the bottleneck.
- If an AI assistant answers a business question, does it use the same definitions as your reports? If it writes its own SQL, it will not.
We ran the check on our own demo project
On 14 October 2026 we answered the five questions for the [Demo] E-commerce project in OWOX Data Marts, the project we build examples in:
| Question | Our answer | Evidence |
|---|---|---|
| 1. Explain a gap within an hour? | Yes | A Q3 revenue gap of $148,963 traced to one status filter in two minutes (data lineage) |
| 2. “Data through” date on every number? | Partly | Orders run to 14 October, ad spend stops on 10 October; the date is shown per Data Mart, not yet on every report |
| 3. A named person per key metric? | No | 16 connector Data Marts feed reports with no business owner |
| 4. Standard numbers without a request? | Yes | 115 reports on 70 published Data Marts that business users open directly |
| 5. AI on the same definitions? | Yes | Asked through MCP for Q3 orders from paid ads, the Orders Data Mart answered 573 orders and 489 completed, each with its definition, matching the warehouse SQL exactly |
Even a project built to demonstrate this setup failed one question and half-passed another. The failures are the cheap ones to fix (owners and a date label), which is why the order below puts them first.
What to fix first
The challenges depend on each other, so the order matters:
- Definitions and owners first. Without them, better data quality or faster delivery only produces wrong numbers faster. Start with five to ten metrics leadership actually uses.
- Bring sources into one warehouse. Silos stop being a problem when the joins happen in one place, done once.
- Checks on the datasets people read. Quality, freshness and joins, tested where reports read from. See common data quality issues.
- Self-service on top of the defined data. Once definitions exist, managers can get standard numbers without an analyst, and the backlog shrinks to the questions that need one.
- AI last, on the same definitions. An assistant is safe to use for business questions when it answers from the defined datasets, not raw tables.
Skipping straight to step 4 or 5 is an expensive mistake: it multiplies the number of people producing numbers before anyone agreed what the numbers mean.
Where OWOX Data Marts fits
OWOX Data Marts covers steps 1, 3, 4 and 5 on top of your warehouse. Analysts define each dataset once as a Data Mart, with a description, a data owner and a business owner, and relationships between Data Marts form the model. Checks run on those Data Marts, and each one shows when its sources were last updated.
Business users then get reports in Sheets, Microsoft Excel or Data Studio, and AI assistants connected through MCP query the same Data Marts instead of raw tables. When a manager and an assistant ask how many orders came from paid ads, both answers come from the same Data Mart and the same definition: on our demo, 573 orders through MCP, as the warehouse SQL returns.
You can try OWOX Data Marts on your own warehouse.
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Frequently Asked Questions
How should data security be handled in analytics?
Keep raw data in a warehouse you control, give each role access only to the datasets it needs, and keep personal data out of the published layer. Let AI assistants query defined datasets rather than raw tables, so they see only what reports already show.
Why do businesses struggle to combine data from several sources?
Because each source counts differently. Ad platforms report conversions by their own attribution rules, and a store counts orders once. Joining them takes a warehouse, agreed keys and a decision about which count the business means, made once rather than in every report.
How can a small team handle more analytics requests?
Define the most-used metrics once, publish them where managers can get them without filing a request, and keep analysts for the questions that need them. The backlog shrinks when standard numbers no longer go through a person every time.
In what order should analytics problems be fixed?
Definitions and owners first, then one warehouse for all sources, then checks on the datasets people read, then self-service on top, and AI assistants last, on the same definitions. Starting with self-service or AI multiplies the people producing numbers before anyone agreed what the numbers mean.
What are the biggest data analytics challenges?
Numbers that disagree between reports, data split across tools, poor data quality, metrics nobody has defined or owns, stale data, and an analytics team that cannot keep up with requests. Trust in AI-generated answers and data privacy sit on top. Most of them surface as the same symptom: two numbers for the same thing.
Which data quality problems hurt analytics most?
The ones that produce plausible numbers: a join at the wrong grain, one metric name with two definitions, a source that stopped loading while others continued. Duplicates and missing values are easier to catch because a rule on one table finds them.
