Why it’s time to rethink traditional data governance frameworks
For organizations everywhere, data has become a vital strategic asset, essential to drive rapid decisions, increase customer satisfaction, and drive operational efficiency and profitability. Data provides the essential intelligence organizations need to develop long-term strategies, optimize processes, and empower frontline workers, delivering the contextual insights and evidence needed to support informed decision-making across every function and area of the business.
In today’s digital age, there is no shortage of organizational data to perform in-depth analyses thanks to the proliferation of connected devices and the rise of AI. Yet, many organizations struggle to harness these data assets and use this information effectively and efficiently. And that proves to be a major stumbling block in creating a data-driven culture that delivers the business value the organization needs.
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Harnessing a complex and growing data ecosystem
The potential benefits of diving deep into large data sets to make better decisions are well documented. However, many companies struggle to kickstart their data-driven roadmap despite investing heavily in data management technology and tools. As a result, they remain stuck collecting vast amounts of data in the hope that they can eventually unlock its latent value.
To become a truly data-driven business, a number of key challenges need to be addressed, including aligning objectives with strategy to deliver the insights you want, and making better use of data lakes and warehouses that are brimming with untapped potential to deliver actionable business intelligence.
Most importantly, data governance has emerged as a critical enabler for facilitating the effective management and use of data across the enterprise. Yet, many organizations struggle to align their data governance efforts with real business goals and ensure that the right people have access to the right data at the right time. In an increasingly data-driven world, organizations will need to rethink their governance frameworks if they are to manage and use data effectively.
The Urgent Need to Redefine Data Governance
Today’s large enterprises have a huge data footprint, but to make better business decisions, business leaders need to know what insights their data can reveal. Key to this process is good governance that ensures data is trusted, secure and available. And therein lies the rub.
Many organizations find it impossible to enforce strict governance frameworks that ensure data is consumed and produced in accordance with internal standards for quality, integrity, architecture, compliance, and security. A situation that is further complicated by disparate systems, formats, and locations that make it difficult to access, consolidate, process, and apply consistent governance standards without significant manual intervention.
After deploying a combination of sophisticated tools and highly skilled staff to extract meaningful intelligence from their assets, enterprises are still finding themselves with painfully long lead times that can stretch into months and years. Not surprisingly, frustrations are growing over the difficulties of leveraging data assets and pushing insights to users.
To close the gap between data governance goals and effective implementation, organizations must move beyond standardized processes and define a new approach to data governance.
Regaining Control with Computational Governance
What is needed is a reliable way to enforce enterprise-wide governance rules that, like guardrails, remain in place throughout the data lifecycle, regardless of where it resides. This is where the concept of distributed computational governance comes into play.
Unlike data management tools that create, copy, and move data, computational governance is an approach that enforces a consistent, automated governance framework across the enterprise. It is a revolutionary approach that enables enterprises to enforce internal standards and security controls, while empowering data consumers and producers to accelerate data discovery and project development.
Computational governance enables enterprises to quickly realize the potential value of their existing data without the need for further consolidation. Computational governance oversees all data tools and technologies, rather than replacing them. It is a technology-agnostic game-changer that automates governance processes and ensures compliance with policies and regulations.
Built-in customizable guardrails ensure that every project meets relevant global and local standards and cannot go into production unless predefined policies are followed. This includes everything from data quality, integrity and architecture to compliance and security. Because bypassing the system is not an option, relying on trust for compliance is a thing of the past.
In addition, computational governance enables organizations to maximize their data-driven potential in the future, by implementing new tools as needed and ingesting structured and unstructured data as business needs change.
A revolution in business performance and flexibility
With a computational governance approach, data professionals can eliminate time-consuming tasks such as finding and validating data integrity before starting projects.
Data teams can easily create and customize specifications that capture all required data practices, internal policies, compliance rules, and architecture standards. Intelligent templates help data professionals automate each technology and practice, reducing the delivery of new and existing projects from years to months.
In addition, a user-friendly interface means that users with the right permissions can search and retrieve business-relevant information without technical assistance from data managers. All this supports faster time-to-market and radically improves the responsiveness of companies to new opportunities or market changes by distributing data ownership across business domains. But that’s not all.
Unleashing the Data-Driven Business
By addressing the shortcomings of traditional frameworks and paving the way for a more flexible, reliable, and cost-effective governance model, a computational governance approach simplifies the way enterprises move to distributed data mesh architecture models that treat data as a product and organize data based on specific business domains.
Computational governance provides all the consistency and control needed to implement a fully functional mesh model that frees domain experts to work with the data they know best. It addresses multiple enterprise needs: domain-centric ownership; self-service data infrastructure as a platform that democratizes the ability to access and act on data; federated governance; and data as a product.
Computational governance provides the protection today’s enterprises need to break data barriers, ensure compliance and security standards, and empower domain experts with the autonomy to leverage the full potential of their data assets.
By enabling the balancing of governance, control, and performance so that data becomes a true business enabler, computational governance addresses the shortcomings of traditional frameworks that stand in the way of a truly data-driven enterprise.
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