FINAL WORD
Ugo Ciracì , Data Strategist & BU Lead , Agile Lab
In addition , computational governance allows organisations to leverage their existing data silos without additional consolidation , ensuring that every data-related project meets relevant standards at all times . In these circumstances , data can ’ t be applied to production environments until predefined governance policies have been satisfied , giving organisations the guardrails they need .
What practical benefits can computational governance deliver ?
Computational governance enables teams to build and implement data governance specifications , standards and policies with much greater speed and effectiveness than using traditional processes . This has a positive knock-on effect for those working on new data-led projects or who need to apply effective governance to legacy projects . least because users with no technical background can be empowered to identify new opportunities .
Armed with these capabilities , organisations can transform their ability to leverage data for business advantage , safe in the knowledge that work is being carried out within relevant governance parameters . This can directly translate into the kind of organisational agility that has become so sought after in the modern business environment .
How can organisations ensure data is available to the right people at the right time ?
Making the most effective use of data often depends on getting it into the hands of the right people , who have the experience and expertise required to maximise its potential value or help others do the same .
In this situation , computational governance aligns very effectively with the data mesh concept , whereby accessing and managing data is based on a decentralised approach that views data as a product owned by relevant business domains . In contrast to traditional centralised architectures , it operates on the basis that domain experts are best placed to understand their own data and that it is made to work for the user instead of the user working to address data complexities .
Organisations looking to exploit the capabilities offered by data mesh can also integrate computational governance to deal with any consistency and control issues that can stand in the way of its decentralised approach . Here , it can ensure that organisational compliance guardrails are wrapped around any relevant rules and regulations that may impact their exposure to legal , financial and reputational risks .
Computational governance enables teams to build and implement data governance specifications , standards and policies .
This includes the use of intelligent templates , which help automate data-related technologies and processes to radically improve the user experience . In practical terms , this can play a major role in dramatically reducing project delivery timescales , not
Computational governance also ensures that the compliance and security standards required to implement the data mesh approach meet relevant regulations and legislation . It does this while maintaining the organisation ’ s architecture framework , facilitating system interoperability and integration .
Ultimately , computational governance delivers the guardrails that organisations need to break down data silos and gives domain experts autonomy to unlock the full potential of their data assets . When balanced with a self-service approach , they can more effectively drive innovation , agility and the ability to quickly adapt to evolving business needs . p
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