Data Analysis
Explore datasets, build dashboards and monitor prediction engines across every TotalApp module — with CRM as the worked example.
Overview
Data analysis in TotalApp is not a single screen — it is a set of capabilities that runs through every module. Whatever the data source (a file you upload, records already stored in HR, Finance, Sales or E-commerce, or live service metrics), you can explore it, turn it into a dashboard, ask questions about it in plain language, and act on what you find.
What you can do
- Composable dashboards — 40+ widget types with real-time refresh, cross-module data in one view and AI-written summaries of your metrics.
- Data exploration — multi-column sort, nested filters, pivot tables and instant statistics (mean, median, mode, standard deviation, percentiles).
- Graph analytics — view customers, products, transactions and workflows as an interactive network and drill into any node.
- Machine-learning scoring — churn-risk and lifetime-value predictions written back to your records (see the CRM example below).
- Natural-language questions — ask your data in plain language; trends, outliers and root causes surface automatically.
- Observability, alerts & exports — latency and health monitoring, threshold alerts by email, Slack or webhook, scheduled digests and one-click PDF, Excel, CSV or JSON export.
Analytics across modules
| Where | What you analyse |
|---|---|
| CRM Dashboard | Pipeline value, win rate, close rate and revenue projections. |
| CRM Funnel Analytics | Stage conversion and cohort retention heat maps. |
| Sales Dashboard | Sales performance and revenue trends. |
| RevOps Forecasting | Quota attainment, ARR/MRR growth and at-risk accounts. |
| Finance Reporting | Cash flow, profit and loss and budget tracking. |
| HR Reports | Headcount, turnover, leave and payroll reports with an AI assistant. |
| Project Analytics | Sprint velocity, burndown and cycle-time charts. |
| E-commerce Reports | Orders, revenue and customer analytics. |
| Graph View | Interactive network exploration of your data. |
| Data Miner | Structured extraction from web pages into datasets. |
| Analytics / Query Agent | Natural-language questions translated into queries. |
| Observability | Latency, health and invocation monitoring. |
| Usage Analytics | Module usage and adoption across your tenant. |
CRM as the worked example
The rest of this page uses the CRM module as its worked example: how raw deal data is inspected, cleaned and scored by a machine-learning engine. The same explore → visualize → predict → share pattern applies to the other modules listed above.
Screen previews
A quick look at the screens. Use the toggle in the corner of each preview to switch between the dark and light themes.
Reports dashboard
Data analysis table
Pivot analysis with statistics
What Is Data Analysis for CRM?
The Data Analysis inside the CRM module works as a machine-learning engine that scores your CRM deals. It sits between raw data (CSV/JSON files, or the deals already stored in your CRM) and the rest of the platform — clean a dataset here, then send it into Graph View or another module, or monitor how the prediction engine is scoring your customer base over time.
Two screens make up this group today: Data Inspector, a general-purpose dataset explorer and AI-assisted cleaning tool, and AI Predictive Models, the configuration and monitoring console for the churn-risk / lifetime-value scoring engine that also powers the Customer Segmentation screen in Marketing.
Runs In Your Browser
Data Inspector parses and analyzes uploaded files entirely client-side — nothing is uploaded to a server until you explicitly export or send a dataset elsewhere. AI Predictive Models reads and writes through TotalApp's own CRM data, scoped to your tenant.
Screens in This Group (CRM)
Data Inspector
Upload a CSV or JSON file (or load the built-in sample dataset) and explore it across five tabs: a paginated data table, per-column statistics, a Pearson correlation heatmap, per-column distribution charts, and a data-quality summary (duplicates, missing values, outliers). An AI-generated cleaning recipe suggests fixes you can approve individually or all at once, and the cleaned dataset can be exported as CSV/JSON/PDF or sent directly into Graph View.
AI Predictive Models
The configuration and monitoring console for TotalApp's RFM-based churn-risk and predicted-LTV scoring engine. Toggle CRM auto-sync, tune the default Recency/Frequency/Monetary weight split, trigger a recalculation on demand, and review a run history table showing deals scored, whether CRM records were mutated, and total revenue covered by each run.
Quick Start
- Open TotalApp and switch to the Data Analysis mode from the mode switcher, or navigate directly to a screen from the sidebar.
- To explore a dataset: go to Data Inspector, upload a CSV/JSON file or click Load Sample to try it with a demo employee dataset.
- Use the five tabs (Data Table, Column Stats, Correlation, Distributions, Data Quality) to understand the shape and quality of your data.
- Click Run AI Analysis to generate a cleaning recipe, then approve and apply the steps you want.
- To review or tune the prediction engine instead, go to AI Predictive Models, adjust the RFM weights or CRM Auto-Sync toggle, and click Recalculate Now.
Two Different Audiences
Data Inspector is a general-purpose tool for anyone working with a raw dataset. AI Predictive Models is aimed at admins/ops who configure and monitor the scoring engine — day-to-day marketers who want to run an ad-hoc segmentation should use Customer Segmentation in the Marketing group instead, which uses the same underlying engine with a simpler, campaign-focused interface.