13 June, 2019 | Written by: Chris Nott
Categorized: Artificial Intelligence | Government
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In the first part, I described the motivations for government to be data driven and the challenges to achieving this at enterprise scale.
I see five major areas that need to change for Government departments to become data driven.
Skills and culture
- The most significant change comes when business leaders recognise the advantages of being data driven. Deepening and widening the skills to analyse data across the organisation is part of the cultural shift. It is worth noting that many organisations should spend more time exploiting the data they already have because it is enough. Leaders can facilitate an ecosystem that harnesses technology for impact throughout the business. For example, by adoption engaging, consumable visualisation tools.
- Capability is delivered by business-driven value streams. They focus on operational outcomes and are able to adapt the data pipeline to current need. The right incentives need to be established. The measurement system for everyone needs to comprise proportions of net promoter score and quality as well as business results.
Data access and governance
- Establishing enterprise information governance enables information to be used appropriately for the best outcomes. This includes the ethical use of analytics. Catalogue and access services make it easier to find and get hold of data within policy. Curation services give data meaning and sustain quality. Data can be traced to it origin to justify decisions taken. Policy enforcement aids compliance, such as GDPR. The ODPi Egeria project hosted by the Linux Foundation is an example of efforts to increase technical maturity and cost efficiencies in this area by managing open meta data across the enterprise.
- The architecture must overcome the limitations of being application and process focussed. This is done by making services available to data scientists and analysts that they understand and need to spend their time using their expertise, hiding the complexities of the underlying infrastructure, and providing mechanisms for them to share and reuse algorithms and results sets. The architecture is open and spans data ingest from sources, a governed data lake, published APIs, microservices assembly by business users, and mechanisms for measurement, feedback and change throughout. Provision should be made for exploiting fast data that has a shelf life.
Beyond the enterprise
- Co-creation, building new capability and accessing new sources of data benefits from an ecosystem. This spans providers of open data, commercial partners and other government departments. It also extends to the edge with the Internet of Things, and the emergence of 5G presents opportunities to change the way governments deliver services.
One way to begin is by selecting a business outcome and creating a value stream with a multi-disciplinary squad that cuts across today’s organisational silos. Then deliver user driven, end-to-end capability in pursuit of the outcome.