First, it’s important to have a clearly defined vision. This doesn’t mean a formalized plan for achieving long-term goals, but rather a sound understanding of the immediate business case, the measurements for success and cadence to evaluate them, and the roles and responsibilities involved in the modern analytics workflow.
There’s nothing wrong with starting small—successful deployments often start with a single department or a use case present within multiple departments. Key data sources can help you estimate of the relevant audience size for your deployment because user engagement will drive server scalability and sizing decisions. Server scalability and sizing, in turn, inform hardware and licensing requirements, which align with budget planning and procurement requests.
IT retains the setup tasks that enable the business, including software installation, user provisioning, access rights, governance oversight, and some development tasks related to content and data sources. Business users may fall into different roles depending on their degrees of skill and interactivity with the platform. Some may need to perform data preparation, analytical exploration, and content creation; others may be suited for simpler interactions with visualizations and only needs to consume curated dashboards and reports.
IT should delegate access and responsibilities over time to data stewards who are familiar with the data, governance processes, and business needs to be trusted to connect to new data or publish and certify metadata models for other business users. Authoring capabilities based on existing certified data sources can be delegated to business users to create new content or answer ad-hoc questions. This may also apply to how users onboard, train, receive support, and foster a data-driven community across the organization.
Your planning for physical (or virtual) infrastructure should be just as flexible and iterative. Given that analytics are often mission-critical and modern BI solutions often see fast growth, you should consider reassessing server utilization and user needs more frequently than with other technology solutions. You may need to change your topology to scale more frequently than other enterprise platforms you’ve managed.
Proactive planning and monitoring helps you better prepare, support, and scale. A “set it and forget it” deployment can be met with inadequate resources that fail to support the workload of highly-engaged users. Similarly, you shouldn’t wait for a spike of performance issues or support tickets to address possible expansions or implementing new technology.
Deployed with agile methodologies, modern business intelligence grants as little or as much change as the organization decides it’s ready for. With a defined vision, careful planning, monitoring, and measurement, agile analytics deployment helps an organization navigate change management and see more stable growth toward data-driven enterprise transformation.
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