A holistic approach for planning analytics in the cloud is facilitated by asking relevant, comprehensive questions as well as being familiar with components that are essential for implementing cloud analytics.
As explained by Lakshmi Randall, Research Analyst, Gartner, these essential components (analytics platform components plus supporting components) are listed below:
Analytics Platform Components:
• Business Intelligence and Analytics, data discovery, advanced analytics, industry-oriented prepackaged analytical applications
Supporting Components (not intended to be an exhaustive list):
• Data Integration supporting analytics solutions
• Analytical data stores (data warehouse, data mart, data lake etc.)
• Data Preparation supporting analytics solution
• Data Quality supporting analytics solutions
• Identity and Access Management
Key questions to ask when planning a Cloud analytics solution are offered below; this list is not intended to be exhaustive, explains Randall.
These planning questions are predicated on placing the analytics platform components in the Cloud; the locations of supporting components are then determined by the strategists, planners and architects based on considerations such as location of data sources, an enterprise’s investment in existing on-premises architecture, and budget and staffing constraints.
“When used in conjunction with business objectives - modernisation, reduction in cost or time to market, reduction of on-premises infrastructure footprint, support of innovative business use cases - Randall believes these questions can serve as a starting point for developing a holistic approach to Cloud analytics.
“Note that it is imperative to consider the cost and staffing (skillsets, maintenance, services) ramifications of the responses to each question,” Randall adds.
Requirements planning:
1. Have you identified your business requirements (e.g., analytical model, KPIs, measures, reports, and dashboard requirements)?
2. Have you identified the end-users (publishers and consumers) of the cloud analytics solution?
3. What analytic capabilities are you planning to implement (such as Data discovery, Business Intelligence and Analytics, Industry-oriented prepackaged analytical applications, Advanced Analytics)?
4. Is your cloud analytics solution internal facing or external facing?
5. Are you planning to leverage relational, non-relational (NoSQL, Hadoop), or both data stores in the cloud platform in support of the analytics solution?
6. Have you identified governance, quality and integration requirements?