AI & analytics
AI on your company’s real data: where to start
How to connect AI to your company’s real data — with permissions, context and privacy — to query in natural language and generate real value.
Read articleKey takeaways
- NLQ lets you ask data in natural language, without knowing SQL.
- It democratises data access for non-technical profiles.
- It needs a prepared, governed data layer with context.
- Permissions ensure each user queries only what they should.
- It is only as good as the data layer behind it.
For decades, querying data required knowing SQL or depending on an analyst to translate the business question into a technical query. The result: bottlenecks and answers that arrived late. Natural-language querying changes that: anyone can ask data as they would ask a colleague, and get the answer instantly.
What it is
Natural Language Query (NLQ) lets you ask data in everyday language ("how much did we sell in March by region?") and get the answer without writing technical queries. The AI translates the question into a query over the governed data and returns the result.
Why it matters
Democratises
Business queriesno intermediaries
Speed
Instantanswers
Frees IT
For complextasks
- Democratises data: business queries without intermediaries.
- Speed: instant answers, not days.
- Adoption: fosters the habit of deciding with data.
- Less technical load: frees analysts for complex tasks.
What it needs to be reliable
NLQ is only as good as the data layer behind it. It needs clean data, a model that understands business context (what "margin" or "region" means) and governance so answers are consistent. Without that base, it can give plausible but wrong answers — worse than no answer, because they create false confidence.
Permissions and security
Opening data to more people makes access control critical: NLQ must respect each user’s permissions, so a question only reaches data that person is authorised to see. Well implemented on a governed layer, NLQ is one of the most powerful ways to put data at the service of the business.
NLQ is only as good as the data layer behind it: without governance, it gives plausible but false answers.
In summary
NLQ lets anyone ask data in natural language, without SQL, democratising access and freeing analysts. But it is only as reliable as the data layer behind it — needing clean data, business context and governance — and access control is essential so each user queries only what they should.
Sources & further reading
Frequently asked questions
What is natural-language querying?
The ability to ask data in everyday language and get the answer without writing SQL or technical queries.
Is it reliable?
It is if there is a clean data layer with business context and governance behind it. Without that base, it can give plausible but wrong answers.
How is it controlled who sees what?
NLQ must respect access control: each question only reaches data the user is authorised to query.
Who is NLQ useful for?
Non-technical business profiles who need data answers without depending on an analyst or knowing SQL — it democratises access.
Why does it need business context?
To understand what terms like "margin" or "region" mean in your company. Without context, it may misinterpret the question.
What is the risk of a poorly built NLQ?
Giving plausible but false answers, creating false confidence. That is why it needs a clean, governed data layer behind it.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.