How and why participatory governance for modern BI?

Modern BI has made self-service analytics possible, and companies have seen tremendous benefits in democratizing data and in offloading new information into the group. Nevertheless, the idea of ​​an autonomous government still seems unimaginable.

Governance is required in modern analytics as data and dashboards are more commonly used but should allow access to data and content rather than confining it. These procedures and processes help the right people access the right data, ensure the accuracy of the data that influences their users’ decisions, and comply with internal or external regulations.

For the security and agility business users need, IT must transform their governance approach into a more collaborative model. Success means navigating the roles, responsibilities and processes that surround this shared responsibility and improving them through an agile approach through iterations.

A steering committee and a governance framework

While modern BI technologies can hide the complexity of your data architecture and simplify certain administrative tasks, companies and organizations need to be involved and involved in building and maintaining governance. A Steering Committee is a good way to ensure that the foundations are met by bringing together key stakeholders to create a clear vision and framework.

This group was to define the “rules of the road” and define expectations and processes related to user roles, permissions, training and certifications, and new performance indicators. Remember that different types of data require different types of governance. Therefore, your model needs to be flexible enough to support any type of data. Consider the roles and processes required to support data and content governance as your model adapts to changing business needs.

Work on a governance model based on clustering with advanced analysis

Modern BI environments are implemented and scaled to help analysts and enterprise users. Therefore, these users must be responsible for their overall quality. The role of IT in the analytics pipeline has evolved from vendor to actor – IT should enable the enterprise to become more involved in governance. Changing corporate governance does not require the IT department to take control, but to become more independent in a secure and centralized environment. Business analysts and users are the first line of defense in identifying data problems or irregularities in a governance model that IT and business agree on.

By paving the way for the definition of analytical goals and desired outcomes (the “what”), computer science is critical to determining the processes that achieve these outcomes (including the “how”), including ensuring integrity and data security. throughout the company. Remember that this is not an overnight transformation; it should be progressive. This often involves moving from a more traditional top-down approach to a self-service model, where responsibilities are transferred to appropriate users, with appropriate knowledge, training, and understanding of governance rules.

Training and Sorting – IT can take control by making governance a collaborative effort. However, he should also be responsible for solving the analysis problems. The involvement of companies should also include timely solutions to problems. Training and education are essential to reduce the governance risks of change management and to resist the provision of advanced analytics across the enterprise and help the company invest in governance. Then, when something goes away, IT can select specific tasks and the appropriate business users can join the process. That’s why information technology has to create a foundation and learn about governance – they know exactly what the model is supposed to be. Conversely, the company needs to establish a center of excellence to train analytical skills, best practices and resources.

New Data Sources and Content – Start with data management and content creation. An effective organizational framework for content management facilitates the search for secure data from experimental content, such as: B.: Certification of data sources or connection to external data for ad hoc analysis in sandbox projects. The next step in self-service can mean that the IT department still has the data. However, a small group of users can manage the publishing of new content, move the content of a sandbox to a production project, and promote it. , With clear standards and a checklist for publishing new content or certifying data sources, you can manage a scalable enterprise-sharing model.

Administrative Tasks – Some administrative tasks should be delegated to business users as your modern analytical practice evolves. IT always needs to manage security, permissions, group policies, and so on, but project managers and site-side administrators can take responsibility for adding new users, managing permissions, and monitoring interactions between users and content. This is usually organized by department. Enterprise-level administrators can use Active Directory or other advanced, on-premises, IT-enabled authentication to simplify authorization management and ensure compliance.

Lifecycle Management – Content engagement demonstrates the success of your advanced Analytics deployment and is often a key performance indicator. IT services must enable the company to monitor interactions with dashboards and reports for their services, identify content or unused or called fields, and even perform an impact analysis on the impact of changes on users. This gives the company more responsibility and responsibility for the content created. The IT department can focus on the security, performance, and capacity of the technology platform.

Find harmony with a modern governance approach

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