ERP Implementation
Bring operational records into dashboards and reports your team can use. We define the measures, connect the sources and make the figures traceable, with predictive analysis assessed where the data and decision support it.
The starting point
The numbers exist across four systems and get assembled by hand into a deck that is out of date by the meeting.
Each one pulled from a different source on a different day with a different definition of what counts.
What the work includes
We begin with the decision the report must support, agree each metric and trace it to the source records. Data preparation, refresh intervals and access are planned before the charts. Dashboards then present the views each role needs for its work.
Explore the delivery processWhat counts as a completed order, an active customer, a delayed batch. Agreed once, so two dashboards cannot disagree later.
Scheduled extraction and transformation from ERP, CRM, Tally or in-house systems into a reporting store.
Built per role, so a floor supervisor and a director see the views their decisions need rather than the same wall of charts.
Every summary number can be opened to the transactions behind it, because a figure nobody can verify does not get trusted.
Reports delivered on a schedule to the people who will not log in to a dashboard, where scheduled delivery fits their work.
A completed order may mean dispatched to one department and paid to another. Resolve that definition and trace it to the source records before building the dashboard. Missing values, duplicate records and refresh intervals affect how the figures can be used.
ERP implementation can provide a shared operational foundation. Where records remain in separate tools, system integration establishes how they reach the reporting layer.
Predictive analysis needs a defined decision, suitable historical records and a way to evaluate the result. Agree who reviews unusual outputs and how the team will compare forecasts with what happened. The underlying figures should remain traceable.
In AI-Powered Healthcare CRM & Monitoring, patient tracking and AI insights supported clinical review through dedicated dashboards. The data, review needs and intended use shape each reporting engagement.
We walk your current workflow with the people who run it and record where work stalls, duplicates or waits.
A fixed price and a dated plan, issued after discovery. Payment runs against milestones.
Review the proposed workflow, interfaces and data requirements before implementation. The design work follows the scope of the project.
Implement the agreed scope in short cycles, review the changes and test the behaviour with representative records and exceptions.
Test the required connections, confirm acceptance with your team and release according to the agreed rollout plan.
The team that built it handles updates, integrations and support calls after go-live.
Projects here typically run between USD 7,500 and USD 15,000 or above, depending on scope. We quote a fixed price after discovery and bill against milestones.
Fits when: Scope is clear and agreed after discovery
Billing: Milestone payments against a dated plan
Fits when: Scope will evolve, and you want capacity reserved
Billing: Monthly, per named engineer
Fits when: The system is live and needs changes and cover
Billing: Monthly, with an agreed response window
Power BI where the business already runs Microsoft and licensing is straightforward, which covers most of our clients. Tableau where visual analysis is the primary use and analysts will build their own views. Both are capable, so the decision is usually commercial.
Not always. Where you have two or three sources and modest volume, reporting can read from them directly. A warehouse earns its cost when sources multiply, history matters, or queries start slowing the operational systems down.
It depends on the metric and what it costs to refresh. Production status often needs to be near live. Financial summaries are usually fine daily. We set refresh per dataset rather than running everything constantly.
That gets settled during definition work, before anything is built. It is the most valuable part of the engagement and frequently the most uncomfortable, because it surfaces disagreements the monthly deck was quietly hiding.
Yes, once the historical data is clean and long enough to be worth modelling. We are direct about when it is not. Predictions on eighteen months of inconsistent records produce confident numbers with nothing behind them.
Most data analysis lifecycle guides are written for data scientists. Here's the shorter version built for a business still running on spreadsheets.
A vendor cannot price data analytics services until you hand over seven inputs, and each one is easier to prepare before the first call. Selection guides spend their pages on how to judge a vendor.
Inventory management system benefits aren't really about the scan itself. They're about what a barcode scan changes for the floor and the office.
Bring the report, the source records and the measures your team uses. We can identify the definitions and connections the dashboard needs.