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Business automation → Chat-with-Data

Chat-with-Data

Chat-with-Data: conversational access to CRM and reports, a business glossary of metrics, answers with tables and sensitive data controls. Implementation in Kazakhstan.

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Overview

Chat-with-Data: the service in brief

Gotomarket implements Chat-with-Data for companies in Kazakhstan. The system turns a natural-language question into a verified query against approved data and returns an answer with a table and its source. As a result, routine figures are available without waiting in the analyst's queue or pulling each dataset by hand.

Launch
2–4 weeks
Languages
Russian and Kazakh
Channels
Telegram and web interface
Data
approved sources only
In plain words

What it is and why it matters

What it is
A chat where managers ask about the numbers in plain language, such as sales or leads, and get an answer with a table and the data source.
Who it is for
Companies where managers keep asking the analyst for the same figures, and the analyst is stuck in a queue of manual data pulls.
What you get
Connected approved data sources, agreed metric definitions, a team chat in Telegram or a web interface, restricted access to sensitive fields and a test set for checking figures.
Where to start
Tell us which questions about numbers your managers ask the analyst most often and where the data lives. We will suggest a trial run on one or two sources.

Solution architecture

How we implement Chat-with-Data with the 5D methodology

We start with management's key questions and connect only data whose definitions and quality can be verified.

01

Discover

Collect questions

We record typical requests to the analyst, data sources and key metrics.

02

Design

Build a metrics glossary

We agree on metric definitions and rules for safe data queries.

03

Deploy

Connect sources

We set up approved databases, access rights and an answer format that cites the source.

04

Drive

Check the numbers

We test the system on real questions and compare results with reference calculations.

05

Dominate

Expand coverage

We add new metrics, sources and channels after confirming quality.

Unit economics and ROI

How Chat-with-Data pays off

The system takes repetitive data pulls off the analyst's plate and shortens the time from question to decision. In a hypothetical example, the analyst spends about 15 hours a week on ad hoc requests.

If Chat-with-Data covers 50–60% of routine questions, that frees up about 30 hours a month. Actual economics depend on data quality, the number of users and query frequency.

Manual requests in the example
about 15 hours a week
Share of routine questions
50–60%
Time freed up
about 30 hours a month
Response speed
seconds after asking

Result

What you get

The pilot includes a validated set of questions, a metrics glossary and secure access to selected sources.

  • Priority data questions list
  • Business glossary of key metrics
  • Connection of approved sources
  • Conversational interface for the team
  • Answers with a table and source
  • Role-based restrictions on sensitive fields
  • Test set for verifying figures
  • Plan for adding metrics and users

Comparison of approaches

Chat-with-Data vs. a standard dashboard

Dashboards and conversational interfaces answer different kinds of analytical questions.

ApproachStrengthLimitationBest for
Manual data pullFlexible complex analysisAnalyst queue and costsCustom research
DashboardOngoing view of metricsOnly predefined viewsRegular monitoring
Chat-with-DataQuick questions in plain languageNeeds clean data and a glossaryMany routine requests

Limitations and risks

When you don't need Chat-with-Data

The system works where data is structured and metric definitions are agreed.

The data contains errors

Duplicates and discrepancies in sources lead to wrong answers. Clean the data first.

No common definitions

If departments calculate revenue or define a customer differently, you need a shared glossary first.

The request is rare

For one quarterly report, a manual data pull may be simpler than implementing a system.

A good fit

When this service is a good fit

  • Managers regularly ask for the same data views
  • The analyst is overloaded with manual data pulls
  • You have structured data and metric owners
  • You need role-based access

Not a fit

When to choose another approach

  • Sources contain many duplicates and discrepancies
  • Metric definitions are not agreed
  • You need one occasional report
  • The main task is complex research, not routine questions

Terms in plain words

Business glossary
shared metric definitions and calculation rules
Data source
CRM, database or table where the figure comes from
Role-based access
data restrictions based on who the user is
Test question
a query with a pre-verified correct answer
Conversational analytics
getting metrics by asking in plain language
Data warehouse
A separate database that collects data from the CRM, billing and other systems so it can be combined without loading live systems with queries.
Metric owner
The person responsible for how a specific metric is calculated, who confirms the figure when an important decision depends on it.
API
A way for software to exchange data directly; through it the system pulls figures from the CRM without manual exports.

Frequently asked questions

Chat-with-Data: FAQ

Where does the system get the numbers from?

From connected, approved sources. The answer shows the source and the calculation logic used.

Could the system expose data it shouldn't?

Not if permissions are set up correctly: access is restricted by role, and sensitive fields are hidden.

Do we still need an analyst after implementation?

Yes, for complex research and evolving the data model. The system takes over routine, repetitive requests.

Which languages can I ask in?

Russian and Kazakh. The question is turned into a verified query against approved data.

How much does Chat-with-Data implementation cost?

The cost depends on the number and quality of the data sources, the number of metrics in the glossary, the number of users and the access-control requirements. We start with a pilot covering the key management questions over one or two sources. We provide an estimate after reviewing the typical requests sent to analysts.

Can Chat-with-Data be connected to Twenty or amoCRM?

Yes, the CRM is one of the most common sources: pipeline, deals, lead sources and sales team activity. We connect through the API or an export to a data warehouse and record metric definitions in the business glossary. For questions about deadlines and team workload, data from Jira or Plane task trackers can be added.

Is Chat-with-Data suitable for a SaaS company?

Yes, for questions about MRR, churn, activation, trial conversion and the sales funnel, provided these metrics are already calculated in the company's data. Metric definitions must be agreed in advance so that answers match the reports for management and investors. Sources can include the CRM, billing and product analytics.

How can we check the accuracy of Chat-with-Data answers?

Each answer shows the source and the calculation logic, so the figure can be double-checked. A test set of questions is regularly checked against reference calculations, and discrepancies are fixed in the metrics glossary or in the queries. For critical decisions, the metric owner also confirms the figure.

Do we need a data warehouse for Chat-with-Data?

Not always. For a pilot, a direct connection to the CRM or to a database with approved data is often enough. A warehouse becomes necessary when there are many sources, data has to be combined, or production systems must not be loaded with queries. We make this decision at the design stage.

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