01
Data-source integration
Connect supported ERP, CRM, finance, sales, operations, e-commerce,
database, spreadsheet, cloud, API, and third-party sources. The
integration layer brings relevant data together and documents how
records are matched, refreshed, and controlled.
02
Data preparation and modeling
Clean, standardize, transform, and model the approved data so reports
use consistent dimensions, relationships, measures, and time periods. A
well-designed model improves performance and creates a reliable base for
multiple dashboards.
03
Data warehouse and analytics architecture
Design a warehouse, data mart, lakehouse, or fit-for-purpose
analytics layer when direct reporting is not sufficient. The
architecture considers history, scale, update frequency, security,
ownership, and the organization’s existing technology.
04
Power BI dashboards and business intelligence
Develop interactive Power BI reports or another suitable BI
experience with filters, drill-down, trends, targets, comparisons,
alerts, and mobile views. Workspace, sharing, licensing, and access
design are included according to the implementation scope.
05
Executive and KPI dashboards
Give leadership a focused view of strategic indicators, targets,
trends, and exceptions. Each KPI receives a clear definition,
calculation, source, owner, frequency, and validation method.
06
Operational and departmental analytics
Build dashboards for sales, finance, inventory, procurement, HR,
projects, customer service, marketing, fleet, or other functions.
Operational views can highlight queues, aging, workload, service levels,
status, and exceptions.
07
Automated reporting
Replace repeated spreadsheet assembly with scheduled data refresh,
governed report generation, approved distribution, and consistent
business definitions. Automation reduces manual effort while preserving
review controls where needed.
08
Real-time and near-real-time monitoring
Create lower-latency operational views when source systems and
infrastructure support them. The architecture defines the actual refresh
requirement based on the business decision rather than using “real time”
as a general label.
09
Predictive and advanced analytics
Use historical data to explore forecasting, segmentation, anomaly
detection, risk indicators, demand, maintenance, or another validated
use case. Models are evaluated and monitored; predictions are presented
with context rather than as guaranteed outcomes.
10
Data governance and self-service BI
Define ownership, metric catalogues, access roles, workspace
standards, refresh responsibilities, and publication controls. A
governed self-service model allows approved users to explore data
without creating a new version of the truth in every department.