Smart Water Management: Data Visualization Solutions for Digital Transformation

Introduction

The water industry is critical infrastructure for livelihoods and economic development, yet faces persistent pain points: low water resource management efficiency, aging supply systems, inaccurate and untimely water quality monitoring, and insufficient sewage treatment capacity. Smart water management — essentially the digital transformation of water enterprises — aims to address these issues through a practical, effectively implemented solution that reduces costs and increases efficiency.

Construction Background

Policy Drivers

National policy has evolved from "developing intelligent water" to "developing smart water" and "building a smart water conservancy system," continuously emphasizing digital transformation. Key documents include:

The 14th Five-Year Plan and 2035 Vision Outline: strengthen water infrastructure, improve water resource optimization.

Notice on Smart Water Conservancy Pilots: promote 5G, IoT, AI, big data, cloud computing to build perception networks for water supply and drainage facilities, real-time monitoring of pipe network status.

Technology Drivers

IoT sensors deployed on water facilities enable real-time data collection (flow velocity, pressure, water quality) transmitted to control centers. Big data analytics then mine this data: macro-level analysis of regional and temporal water usage patterns for resource allocation decisions; micro-level pinpointing of potential pipe network faults for preventive maintenance.

Industry Demands

Accelerating urbanization increases water demand and pipe network expansion. A major issue is high pipe network leakage — some cities exceed 20% leakage, wasting water and raising costs. Simultaneously, rising industrial and domestic sewage volumes outpace traditional treatment capacity, constraining sustainable development.

Overall Construction Approach

Construction Goals

Efficient Data Connectivity : Build a data warehouse to integrate ERP, SCADA, NRWS, NGIS, WDSS, RMMLS and other business systems, enabling real-time data integration and heterogeneous data processing.

Visual Data Decision-Making : Construct a water business analysis framework covering four themes — production operation, comprehensive management, customer service, pipe network safety — to support daily management decisions.

Intelligent Data Analysis : Replace manual Excel analysis with BI auto-extraction, filtering, and analysis, dramatically improving reporting efficiency and uncovering previously overlooked insights.

Data Sorting

Based on current and planned business systems, organize data assets:

Structured data : user water consumption records, water bills, equipment operating parameters.

Unstructured data : water quality test reports, pipe network inspection images and videos, customer service feedback.

Define indicators across multiple dimensions to build a water indicator system:

Water resource dimension: total water resources, available water, utilization rate.

Water quality dimension: compliance rates of quality indicators, trend changes.

Water supply dimension: coverage, reliability, supply volume fluctuations.

Pipe network dimension: leakage rate, aging degree, repair frequency.

Data Building: 1+4+N Digital Financial Analysis Framework

1 — One core financial analysis framework oriented to enterprise financial goals (profit, cost control, capital turnover), covering financial statement analysis and key ratios (net profit margin, debt-to-asset, current ratio).

4 — Four main modules: Revenue Analysis (water supply, sewage treatment, value-added services), Cost Analysis (production, operation, management costs), Asset Analysis (fixed, current, intangible assets quality and appreciation), Cash Flow Analysis (inflow/outflow stability and adequacy).

N — Multiple auxiliary modules customized to business needs, e.g., water source cost-benefit analysis for multi-source enterprises, pipe network asset return analysis for heavy pipe investors.

Data Landing: From "Wall Charts" to Digital "On-Screen Operations"

Traditional wall-chart command relies on manually drawn charts and experience, suffering from delayed updates and inaccuracies. Digital on-screen operations display real-time data and analysis on electronic screens. The figure below shows a FineBI smart water management dashboard for June 18, 2024, including water plant production, monthly supply statistics, 7-day water quality analysis, plant overview, DMA pipe network monitoring, revenue data, and patrol maintenance.

Data Foundation Architecture

The architecture comprises four layers:

Front-end data sources.

Data layer : data warehouse, ODS (source-aligned layer), data mart area, thematic domains (DWD, DWS), and ADS (application data service).

Application layer : visualization dashboards, PC, mobile (APP/H5) via FineReport and FineBI.

System management : decision display, platform management, login authentication, user management, permission management.

Scenario-Based Solutions

Dashboard Example: Water Sales Theme

Visualizes key indicators — sales volume, user categories, meter count, receivables — with multi-angle, panoramic views. Drag-and-drop configuration via extended charts and rich plugins enables professional visualization without coding.

Cockpit Example: Water Group

Four modules — production, revenue, service, monitoring — each drillable for rapid operational insight and strategic decision-making. Drill-through linkage allows tracing root causes behind surface phenomena.

Application Value

Decision Support for Digital Operations : Four modules (production, pipe network, customer service, management) cover all core group businesses, providing strong data support for strategy formulation and refresh.

Rational Resource Allocation : Smart pipe network module enables optimized resource allocation, production-sales difference optimization, and effective leakage control.

Rapid Business Response & Effective Inspection : Frontline — solves delayed inspection info, enables real-time anomaly alerts (e.g., equipment temperature warnings via maintenance platform). Management — cockpit provides holistic operational view, accelerates decision-making, enhances competitiveness.

Smart Service Module : Addresses long water bill collection cycles and default risks; marketing account analysis enables targeted reminders for overdue or at-risk users, reducing marketing costs.

Effective Data Asset Management : Resolves data distortion; automates repetitive collection, collation, and analysis for frontline staff, improving personnel efficiency and managing enterprise digital assets.

Conclusion

Smart water management is an inevitable trend. Digital technologies enable efficient data connectivity, visual decision-making, and intelligent analysis, solving industry pain points and elevating operational efficiency and management levels. The presented data visualization construction plan offers a feasible path to comprehensive digital management. Water enterprises should actively advance smart water construction, continually improve digital maturity, and better serve livelihoods and economic development.