Standards Development

Scope

This document provides data management guidelines for smart water management relating to drinking water, wastewater and stormwater systems and services.

The following are within the scope of this document:

1 - Data management discipline: data as an asset This chapter will describe the main issues around data within a smart water management system and the usual points to tackle when a water utility wants to develop such a system

2 - Application to the main data sources for smart water management This chapter will propose some guidelines to apply a good data management discipline on the different main data sources in water operation

3 - Guidelines for data processing in smart water management systems This chapter will describe the main steps to observe on data processing when deploying a smart water system with a special focus on time series treatment

4 - Manpower and organization around smart water data Data is key on digitization but no success without a strong organization. This chapter will propose and describe the keys to meet success.

This document is applicable to all sizes of public or private water utilities, that want to design, develop, implement, operate and maintain smart water management system.

Purpose

For many years now, water utilities have been using more and more digital system to manage their operations and customers. The recent & constant evolutions of NTIC propose some new technologies using and producing a lot of data but also giving a powerful capacity of data treatment (big data, artificial intelligence, cloud computing, Internet of Things, robotics, etc..).

This “data explosion” allows IT People and data scientist to propose some new services that :

- increase operational efficiency of assets and networks,

- reduce or optimise Capex and Opex,

- allow a better human & industrial risk anticipation,

- decrease environmental footprint & insure regulation compliancy,

- support oversight and substantive accountability to local or national stakeholders.

So today, the issue is not the lack of data but the way to transform these data into valuable information.

If we consider that 80% of data scientist time is consumed in preparing data before treatment then we touch the purpose of this standard: How to reconciliate mass of data coming from existing and heterogeneous water systems with value creation.

Therefore, the standard will propose general guidelines to implement a good strategy of data management in order to get the best of these data.

At the end, the purpose is to give the keys for a strong data management discipline allowing water utilities to implement data traceability within their information systems and without prejudicing the “physical” implementation method. 

For many years now, water utilities have been using more and more digital system to manage their operations and customers.

The recent & constant evolutions of NTIC propose some new technologies using and producing a lot of data but also giving a powerful capacity of data treatment (big data, artificial intelligence, cloud computing, Internet of Things, robotics, etc..).

This “data explosion” allows IT People and data scientist to propose some new services that :

- increase operational efficiency of assets and networks,

- reduce or optimise Capex and Opex,

- allow a better human & industrial risk anticipation,

- decrease environmental footprint & insure regulation compliancy,

- support oversight and substantive accountability to local or national stakeholders.

So today, the issue is not the lack of data but the way to transform these data into valuable information.

If we consider that 80% of data scientist time is consumed in preparing data before treatment then we touch the purpose of this standard: How to reconciliate mass of data coming from existing and heterogeneous water systems with value creation.

Therefore, the standard will propose general guidelines to implement a good strategy of data management in order to get the best of these data.

At the end, the purpose is to give the keys for a strong data management discipline allowing water utilities to implement data traceability within their information systems and without prejudicing the “physical” implementation method.