Building data platforms and deliverying advanced analytical services in the new age of data intelligence can be a daunting task. It’s not really helping with all the tools and methodologies that we know we can use. Therefore, a reference architecture is needed to provide guidelines for the process design and best practices for advanced analytics, so we can not only meet the business requirement, but also bring more value to the business.
1. Architectural Guidance
- The architecture should cover all building blocks including the following: Data Infrastructure, Data Engineering, Traditional Business Intelligence, and Advanced Analytics. Within Advanced Analytics, we should include machine learning, deep learning, data science, predictive analytics, and the operationalization of models.
- One of the first steps should be finding the gaps between current infrastructure, tools, technologies and the end state environment.
- We need to create a unified approach to both structured and unstructured data. It’s perfectly fine to…
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