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Are you looking at the masses of data now available to your organization and wondering how to best store and model it to meet the nuanced and evolving business needs?
A comprehensive business intelligence and analytics strategy will require careful orchestration of the tools, data, people, and processes required to contribute valuably to your business objectives. In the second session of this four-part series, we will focus on considerations for data storage and modeling, with a focus on demonstrating the agility required to leverage data for the wide gamut of business needs, such as self-service, structured reporting, and advanced analytics.
What we will cover
Drawing from our own client experiences, we will demonstrate:
- Common pain points that organizations experience when trying to store and model data to meet business objectives in a timely and flexible fashion
- Key principles for delivering a data strategy that is robust and agile enough to meet changing business needs and technology landscapes
- Sample architectures for storing and modeling data to cater for large volumes, discrete types of data, and the agility required to ingest new data easily.
Is it for you?
- Are you a member of an IT organization that is struggling to provision data for the business in a timely and agile enough manner to meet their needs?
- Are you considering the role that data lakes could play in your architecture?
- Do you currently have a data strategy that prohibits new data from being ingested quickly enough to cater to time-sensitive business drivers?
- Are you struggling to provide data suitable for the needs of advanced analysts?
If you missed the first part of this Strategy series, here's a recording of the webcast Considerations for front end tool selection: