Customer work · Logistics

DTDC centralized operational MIS and stakeholder reporting on Google Cloud

DTDC relied on manually assembled reports across sales, marketing, and operations systems. BluePi built a central Google Cloud reporting path with Dataflow pipelines, BigQuery, and Google Data Studio dashboards for MIS, bottleneck analysis, and senior-management KPIs.

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DTDC centralized operational MIS and stakeholder reporting on Google Cloud system diagram

Sales, marketing, and operations data moved into one Google Cloud reporting path

DTDC teams assembled and reconciled reports manually across separate systems. BluePi used Google Cloud Dataflow to collect, clean, and transform the data, BigQuery as the central warehouse, and Google Data Studio dashboards for MIS, bottleneck analysis, and senior-management KPIs.

01 · Dataflow collection and preparation

Dataflow pipelines collected records from sales, marketing, and operations systems, then cleaned and transformed them before loading the warehouse.

02 · BigQuery reporting warehouse

A central BigQuery repository gave departments secure access to assimilated data and a shared base for business KPIs and MIS reporting.

03 · MIS and KPI drill-down

Google Data Studio dashboards let users move from senior-management KPIs into the lower-level detail required to identify bottlenecks.

From siloed source data to drillable MIS reports

Sales, marketing, and operations records entered Dataflow collection, cleaning, and transformation pipelines. BigQuery stored the combined reporting data, and Google Data Studio served MIS dashboards, detailed bottleneck views, and senior-management KPIs.

Automate collection and preparation

Dataflow pipelines replaced repeated manual collection, cleaning, and transformation work across source systems.

Create one reporting warehouse

BigQuery held assimilated data from sales, marketing, and operations systems and made it available through controlled reporting paths.

Connect KPIs to operating detail

Google Data Studio dashboards let users move from high-level measures into the detail needed to identify and diagnose bottlenecks.

Where this pattern fits

This case fits logistics organizations that have reports but lack one dependable route from source data to MIS and management KPIs. BluePi can start with a bounded report set and trace each measure through source, transformation, quality, and owner.

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Case details

Open a section to review the customer problem, implementation, business change, and architecture.

01The starting point

Data remained spread across sales, marketing, and operations systems. Reports were assembled manually, large data volumes made consolidation difficult, and stakeholders could not reliably move from a KPI into the detail behind it.

Data silos: Without a central repository, teams could not easily access or analyze data across systems.
Manual reporting: Report generation was manual, slow, and error-prone.
Limited insights: Without timely, complete data, decisions relied on partial information.
Inaccessible dashboards: Stakeholder dashboards were static, with no way to drill into specific data points.

02System delivered

BluePi used Google Cloud Dataflow to collect, clean, and transform source data before loading BigQuery. Google Data Studio dashboards provided detailed MIS views, bottleneck analysis, and senior-management KPI reporting.

Data warehouse: A central repository stores and analyzes data from sales, marketing, and operations systems.
Dataflow pipelines: Google Cloud Dataflow pipelines automatically collect, clean, and transform data for the warehouse.
Data Studio dashboards: Interactive dashboards support management information system (MIS) reporting, trend analysis, and data exploration.
Business KPI dashboards: Tailored dashboards give senior management a current view of key performance indicators (KPIs).

03Outcomes

DTDC established a centralized reporting practice, reduced manual report preparation, reduced data issues introduced by manual consolidation, and gave stakeholders interactive access to current reporting data.

Centralized reporting: One platform now handles analysis and reporting, ending data silos and manual assembly.
Reduced reporting time: Automated report generation cut the time and effort spent on manual reporting.
Improved data accuracy: Removing manual data handling improved accuracy and reliability.
Improved stakeholder insights: Interactive dashboards give stakeholders a clear view of business performance they can act on.
Decision-making from current data: Departments now make decisions from current data.
Cost-effectiveness: The platform runs at low cost for DTDC's reporting workload.

04Delivered Google Cloud reporting path

Sales, marketing, and operations systems fed Dataflow collection, cleaning, and transformation pipelines. BigQuery stored the assimilated reporting data, while Google Data Studio served detailed MIS dashboards, bottleneck analysis, and senior-management KPI views.

05What changed for reporting and decisions

Teams spent less time assembling reports manually and introduced fewer data issues through consolidation. Stakeholders could move from management KPIs into the source detail required to investigate bottlenecks.