Continution to previous post - How to Address Big Data Challenges (PART 2 of 6)
Big data technology evaluation and selection
Data management teams have a variety of big data technologies to select from, and the capabilities of the various solutions frequently overlap.
Starting with current and future needs for data from streaming and batch sources, such as mainframes, cloud applications, and third-party data services, it is advised that the teams assess current and future needs for data from streaming and batch sources.
Enterprise-grade streaming technologies, for example, allow data to flow seamlessly between cloud, on-premises, and hybrid cloud systems.
The next step is for teams to assess the advanced data preparation capabilities needed to feed AI, machine learning, and other advanced analytics systems. It's also crucial to consider how the data will be processed.
Teams must examine how to run analytics and AI models on edge servers in situations where latency is an issue, as well as how to make it easy to update the models. These capabilities must be weighed against the cost of deploying and managing equipment and applications that run on-premises, in the cloud, or at the edge.
Generating business insights
It's easy for data teams to get caught up in the technology of big data rather than the results. In many cases, the question of what to do with the data receives far less attention.
Creating KPI-based reports, recognizing relevant predictions, and providing various types of recommendations are all situations that must be considered when generating significant business insights from big data applications in enterprises.
Business analytics professionals, statisticians, and data scientists with machine learning experience will all be needed to contribute to these efforts. When that group is paired with the big data engineering team, the ROI of putting up a big data environment can be significantly improved.
To be continued... How to Address Big Data Challenges (PART 4 of 6)
Thank you for taking the time to review this post. 
