{"id":113479,"date":"2024-08-08T15:42:05","date_gmt":"2024-08-08T14:42:05","guid":{"rendered":"https:\/\/www.intelligentcio.com\/eu\/?p=113479"},"modified":"2024-08-08T15:42:07","modified_gmt":"2024-08-08T14:42:07","slug":"data-guardrails-essential-for-governance","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/eu\/2024\/08\/08\/data-guardrails-essential-for-governance\/","title":{"rendered":"Data guardrails essential for governance"},"content":{"rendered":"\n

Ugo Cirac\u00ec, Data Strategist & BU Lead at Agile Lab, explains the growing necessity of data guardrails for effective governance.<\/em><\/strong><\/p>\n\n\n

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Ugo Cirac\u00ec, Data Strategist & BU Lead, Agile Lab<\/strong><\/figcaption><\/figure><\/div>\n\n\n

Enterprises everywhere are heavily focused on driving value from data, but what does this actually mean, and what are the main challenges they face?<\/strong><\/p>\n\n\n\n

Driving value from data is a widely discussed business priority. Generally speaking, this is the process of using data to achieve a range of objectives, from informing decision-making and improving efficiency to optimising operational capabilities and the customer experience. The list of potential benefits goes on, but given the huge volumes of data modern businesses collect and retain, being data-driven is now synonymous with growth and success.<\/p>\n\n\n\n

Turning the concept into something tangible, however, is fraught with challenges. Part of the problem is that many organisations are in a hurry to deliver demonstrable impact or uncover the kind of insight that will give them genuine competitive advantage. This can create a potentially serious disconnect between their data-led objectives and the governance frameworks they apply. In this context, issues such as data integrity, quality, architecture, security, and compliance can combine to derail efforts to leverage data effectively.<\/p>\n\n\n\n

For example, organisations will often find that their legacy data strategy means datasets reside across disparate and siloed systems, incompatible formats and even different countries. This can add a huge level of complexity to the tasks associated with data governance, often requiring significant and time-consuming levels of manual human intervention.<\/p>\n\n\n\n

While there is a whole range of technologies out there designed to help address these issues, many organisational leaders have also found that expensive data management systems and analytics platforms can\u2019t bridge the gap between objectives and outcomes. As a result, budgets are swallowed without any coherent impact or holistic governance strategy in place.<\/p>\n\n\n\n

What can organisations do to address these issues?<\/strong><\/p>\n\n\n\n

Ideally, enterprise-wide data governance should apply throughout the data lifecycle. It should work irrespective of where data is hosted and provide a set of guardrails that give organisations the consistent framework they need to remain focused and compliant.<\/p>\n\n\n\n

Think of it this way: the traditional approach to data management relies on the creation, storage and management of data, often at huge scale. Employees can often spend a significant amount of time focused on non-revenue-making activities, such as finding and validating data integrity, before they can move on to other activities that might deliver tangible value.<\/p>\n\n\n\n

What can be missing, however, are the additional capabilities provided by a computational governance approach, which is designed to oversee existing data tools and technologies instead of replacing them. In doing so, it automates governance processes to ensure organisational compliance with policies and regulations.<\/p>\n\n\n\n

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\u2019t be applied to production environments until predefined governance policies have been satisfied, giving organisations the guardrails they need.<\/p>\n\n\n\n

What practical benefits can computational governance deliver?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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 least because users with no technical background can be empowered to identify new opportunities.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

How can organisations ensure data is available to the right people at the right time?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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\u2019s 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>\n","protected":false},"excerpt":{"rendered":"

Ugo Cirac\u00ec, Data Strategist & BU Lead at Agile Lab, explains the growing necessity of data guardrails for effective governance. Enterprises everywhere are heavily focused on driving value from data, but what does this actually mean, and what are the main challenges they face? Driving value from data is a widely discussed business priority. Generally […]<\/p>\n","protected":false},"author":18,"featured_media":113490,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[13207,93],"tags":[21022,21021,21020],"class_list":["post-113479","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership","category-top-stories","tag-agile-lab","tag-data-guardrails","tag-ugo-ciraci"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/113479","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/comments?post=113479"}],"version-history":[{"count":1,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/113479\/revisions"}],"predecessor-version":[{"id":113491,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/113479\/revisions\/113491"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media\/113490"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media?parent=113479"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/categories?post=113479"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/tags?post=113479"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}