AI tools for data analysis
Start with the place where colleagues already do their analysis. Power BI Copilot and Tableau Agent add assistance to familiar BI environments. Hex accommodates SQL and Python work; ThoughtSpot Spotter helps people explore modeled data through conversation. Databasin implements the data and workflow as well as providing AI answers. The choice should make a useful question easier to answer, including the time spent checking the calculation.
Quick picks
| Option | Best for | Setup |
|---|---|---|
| Power BI Copilot | Questions about existing Power BI models | Prepare the models and configure Copilot access. |
| Hex | Investigations combining conversation, SQL and Python | Connect the data and document its analytical context. |
| ThoughtSpot Spotter | Business users exploring modeled company data | Model the connected sources for business questions. |
| Databasin One | Business analysis with implementation and support | We connect sources and implement the business workflow. |
| Tableau Agent | AI assistance within existing Tableau work | Set up supported Tableau access and analytical sources. |
Selection criteria
We selected five tools for recurring business analysis, with different starting points and operating responsibilities. The weights describe our editorial priorities, without implying a benchmark score. Run the same known questions through a shortlist, then inspect the calculations and follow-up answers. Include a changed definition and an incomplete input. Record how much work the recipient spends checking the result, and who maintains the data after rollout. These tests reveal more about fit than a polished demonstration alone.
Business workflow fit
30%
Evidence and definitions
30%
Implementation and continuing operation
25%
Fit with existing systems
15%
What happens next
We would like to hear where your team wants to make progress with data and AI. We can discuss a project you have in mind or explore where Databasin could help with the work.
What happens next
Book a demo
Book a conversation about your organization and the work you want to improve. Questions about where to start are welcome.
Explore the opportunity
We show a working example that answers your business questions, then discuss what it would take to deliver it for your team.
Get the first result
After agreeing the first use case, we implement it with your team. We train the people using it and measure how it performs.
FAQ
How should we test a calculation?
An accepted earlier analysis gives the test a known result. Inspect the dates, population and joins in the new calculation. Then change one condition in a follow-up question. Record any corrections and the time needed to check them; that work will recur after the pilot.
What should happen when the question is ambiguous?
A choice that changes the answer should be visible to the person asking. For "orders last month," does the date refer to creation, acceptance or shipment? A documented business definition can supply the answer. Without that definition, the conversation needs to establish which date the person means.
How should we test incomplete data?
Delay a required extract in the test environment, or remove access to one of the inputs. Ask the intended recipient to read the output. They should be able to identify the missing input and see which conclusions it affects. Route the failure to someone responsible for restoring the input or resolving the unanswered question.
Can uploaded files support recurring business analysis?
A defined set of records in a file can support an isolated question. A daily answer introduces another requirement: the right file must arrive before the analysis starts. Its refresh and correction process need an owner. Test a changed column and a replacement file, and check that the result identifies which version it used.
Are free data analysis tools enough for an evaluation?
An isolated task can reveal whether the interaction is useful. Before expanding that experiment across a company, test it with the intended recipients. Their source connections and permissions determine what they can actually use. A colleague may be unable to open the same records, or may need more evidence than the person who built the analysis.
Who should take part in a pilot?
Include the recipient alongside the people who understand the data and maintain the workflow. Ask the recipient to assess usefulness and the data specialist to check the calculation. The operator can test the response to a failed input. An implementation team can bring these reviews together around the decision the pilot is meant to support.
When should analysis become a scheduled agent?
Consider a schedule after a recipient has used several completed investigations. The next step is to define when the work starts, where findings go and how a run stops. An agent is useful when a finding determines which check comes next. A known calculation with a dependable sequence can often run as a fixed workflow.
How can we tell whether adoption is working?
Watch someone use the answer to complete the original business task. Record what they must check or correct before making a decision. This can reveal that frequent use has created more checking work than the team expected. Repeat the review after rollout, when people are using it without close help from the pilot team.
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Talk about better business answers
Tell us where analysis slows the work or where a recurring question remains difficult to answer. We can discuss what would help, how it could fit your systems and how our team could implement it.