China
Africa
Explore chapters and articles related to this topic
Building Data and Process Strategy and Metrics Management
Published in Rajesh Jugulum, Robust Quality, 2018
In any organization, data and process strategies play an important role, as they help in understanding the impact of data and processes across the organization and in planning and governing these assets. Generally, data and process strategy requirements should include the following: Data valuation: Data valuation focuses on the idea of estimating the dollar value for data assets. Many companies are interested in monetizing data. So, data valuation should be an important requirement for overall data and process strategy.Innovation: Data innovation deals with the systematic use of data and process efficiency techniques to quickly derive meaningful insights and value for the company. Data innovation also helps to provide intelligence about customers, suppliers, and the network of relationships.Risk management and compliance: Risk management and compliance deals with risk aspects by quantifying the risk of exposure for all legal, regulatory, usage, and privacy requirements for data and processes at various levels.Data access control: This is needed to ensure that data access, authentication, and authorization requirements for data are met at all levels.Data exchange: The concept of data exchange helps in understanding internal and external data using standard data definitions and in ensuring the data are fit for the intended purpose.Monitoring, controlling, and reporting: This is an important requirement for overall strategy, as it helps to provide a real-time reporting mechanism with monitoring and controlling aspects to understand the end-to-end process- and data-related activities by performing real-time analytics to support business decisions.Build-in quality: This emphasizes the need to institutionalize quality practices and embed them into processes for business self-sufficiency to achieve standardization across an enterprise.Data as service: This requirement helps in providing seamless, business-friendly access to data services and inventory through enabling technologies.
Spatial data trusts: an emerging governance framework for sharing spatial data
Published in International Journal of Digital Earth, 2023
Nenad Radosevic, Matt Duckham, Mohammad Saiedur Rahaman, Serene Ho, Katherine Williams, Tanzima Hashem, Yaguang Tao
As data valuation frameworks are usually application-specific, a general-purpose solution can be challenging to build. A big data value chain framework for end-to-end data monetization is presented in Faroukhi et al. (2020). Deloitte presented a data valuation framework (Deloitte 2020) with four key components: (i) identifying current data assets, (ii) identifying attributes from current data assets, (iii) identifying use cases and corresponding data value exploration, and (iv) exploring alternative or future use cases if the current use cases and valuation are not satisfactory (Figure 10).