Radiant Advisors, a leading industry advisory firm, advises their clients and gives them blueprints to create a modern analytics lifecycle and analytics architecture. In this paper, Radiant Advisors defines the modern analytics lifecycle, shows how Datameer Spotlight fulfills this promise, and offers a blueprint architecture using Spotlight to deliver faster analytics.
Many existing data and analytics initiatives were put to the test during the economic volatility of the first year of the global pandemic. As a result, leaders realized how much they relied upon empowered front-line business people working within self-service data analytics tools—and the cloud—to accelerate analytics work for quick, critical decision-making. After a tumultuous period of navigating sudden and potentially profound business impacts, there is now renewed clarity and vigor in the “need for real agility” in data analytics and digital transformation strategies.
While data-driven companies continue to focus on their business data enablement and cloud-first strategies to leverage self-service data analytics platforms, many are also involving business analysts to accelerate other enterprise data and analytics development processes that provide much-needed business intelligence (BI) reporting and machine learning (ML) predictions.
You learn about the modern analytics lifecycle and how it delivers agility
A framework for enterprise analytics capabilities and how to organize these in your organization.
An approach and architecture to deliver self-service analytics and scale this capability within your organization with Datameer Spotlight.
A process and blueprint to create agile business intelligence with the modern analytics lifecycle and Datameer Spotlight.
While many organizations have been working on sound data and analytics strategies for some time, over the past year many existing and planned long-term strategies were de-prioritized or placed on hold as the focus shifted away from the long-term and to ways to immediately enable people with business knowledge to work with data. Self-sufficiency and agility made their way to the top of data and analytics strategies. However, the old mantra of self-service as enabling more business users to work with data through leveraging key tools and capabilities has gone beyond empowering business users to be the solution for mitigating IT bottlenecks and has earned a new context: to be the catalyst for both business agility and innovation.
Most companies regard self-service data analytics for those business customers capable of taking care of data analytics needs that BI/DW doesn’t highly prioritize. This mindset reinforces the notion that BI/DW is a bottleneck for customer data needs, or worse, that these data needs aren’t important. As a result, many business groups are left to support themselves with tools and data access without a formal business initiative to drive self-service data analytics and data-driven culture.
Past agile methodologies have attempted to achieve iterative development for delivering business value to transition away from traditional waterfall-oriented doctrine. However, this has been challenging for BI/DW teams as they design and build based on assumptions of data quality and customer requirements that are not typically fully imagined or documented. Furthermore, the notion of a “pre-defined” data warehouse with well-understood subject area data models, integration rules, and metrics definitions is rare and usually limited to application-oriented data warehouses, like ERP.