A major concern with real-time monitoring networks is the accuracy and reliability of data. In 2017, SWAN surveyed 23 global water utilities about their Big Data management practices as part of a Water Research Foundation study, including their barriers to adoption. Based on these survey results, “Data quality” and “Lack of talent to implement Big Data” with the biggest impediment factors. One potential change agent to addressing these pain points is the “Data-as-a-Service” (DaaS) model, an outsourced approach to data collection, delivery, and verification.
What is DaaS?
DaaS is an innovative business model transforming the way organisations gather, share and interpret data by accessing data on demand.
DaaS can be characterised as a framework for designing and developing a set of reusable data services. In recent years, DaaS has gained considerable momentum as enterprises across all sectors are moving towards a service-orientated architecture.
In essence, DaaS enables users to only pay for the final outcome they wish to receive instead of purchasing and maintaining the equipment themselves. Thus, there are no sunk costs for hardware, data collection, storage or support with these risks remaining with the Data Provider. DaaS also relieves the obstacles involved in training and retaining staff to oversee the operational status of a network. Any type of service involves providing a clear value to customers and facilitating successful outcomes the customer wants to achieve, while managing associated risks.
There are notable DaaS challenges, such as concerns over data ownership, data security, and affordability. However, one of the main features of DaaS is that the cost of data collection, cleansing, and analytics is known, making it easier to forecast budget expenses and plan ahead.
DaaS Guiding Principles
- Architecture not technology. DaaS is an architectural framework, beyond a mere technology or application. Its underlying foundation is typically based on the concept of service reuse, enabling users to utilise common, standardised services over the web, the Cloud, and related technology for multiple purposes within an organisation.
- Focus on data quality. For any DaaS service provider, the quality of published data is the primary strategic asset that distinguishes them in the eyes of their service consumers. Therefore, it should be viewed as a key differentiator that must be exploited to drive market share by the data provider. The information fed to DaaS subscribers has to be consistent, timely and accurate and meet all the SLA (service-level agreements) specified by business stakeholders with regards to quality and fitness for use.
- Data governance challenge. Data governance is often the most challenging aspect of a DaaS program due to the high degree of coordination required to gain consensus among multiple stakeholders on major governance issues. This is impacted by several items including local data laws (e.g. if the data must be encrypted), the support of data quality assurance, security and privacy compliance, data classification, information lifecycle, and auditing features that a DaaS system can support. Anyone considering a DaaS program should be aware that data governance is a critical success factor to the long-term growth and sustainability of data services across the organisation.
DaaS in Water
As a relatively new business model for the water sector, there is limited, public information on the impact of DaaS on utility operations. However, there are several successful water and wastewater applications, with utilities outsourcing the operation and maintenance (O&M) of different services to outside private companies, such as for smart metering, leak detection, water quality monitoring, combined sewage overflow (CSO) monitoring, or industrial pollution detection.
Within the water sector, DaaS can be defined as a “model in which a Technology Supplier is outsourced to operate and maintain certain hardware equipment to measure, collect, store, and transmit data and the utility only pays for the final delivered results.”
In different cases, a utility may choose to purchase the equipment themselves (e.g. flow sensors, level sensors, remote stations), rent the equipment, or only pay for the data they wish to receive. The expected outputs can range from acquiring only the data itself, a summary report, or predictive analytics.
As part of his PhD research, Amir Cahn at the SWAN Forum conducted a global utility survey to understand (1) what motivates or prevents utilities from adopting DaaS, (2) how do different water and wastewater DaaS applications compare, and (3) what knowledge can be gained from the manufacturing sector, where DaaS, or “servitisation” is more common.
SWAN Members will have access to the results of the study.