Big Data Analytics Infrastructure

Big Data Analytics Infrastructure

Recent surveys suggest the number one investment area for both private and public organizations is the design and building of a modern data warehouse (DW) / business intelligence (BI) / data analytics architecture that provides a flexible, multi-faceted analytical ecosystem. 

The goal is to leverage both internal and external data to obtain valuable, actionable insights that allows the organization to make better decisions. Unfortunately, the amount of recent DW / BI / Data Analytics innovation, themes and paths is causing confusion.

The "Big Data" and "Hadoop" hype is causing many organizations to roll-out Hadoop / MapReduce systems to dump data into - without a big-picture information management strategic plan or understanding how all the pieces of a data analytics ecosystem fit together to optimize decision making capabilities. 

This has resulted in the creation of a new word: Hadump - meaning data dumped into Hadoop with no plan. There are two schools of thought about data collection and storage strategy:
1) Start big data analytics project with a specific use case or problem to solve
2) Start dumping data to store and analyze later We strongly suggest using both strategies. One is short term for quick results and other for long term value. Consider only about 30% of all collected data will be valuable.
The problem is you do not know what 30% will indeed be valuable. Thus, it is prudent to collect and store all data: structured and unstructured as well as internal and external. The cost of collecting and storing the data - and data analytics technology - has been significantly reduced and will get cheaper and cheaper.

The cost of analyzing the data for valuable, actionable insights is very high. While machine learning and automation will reduce cost in future, the formula of cheap, abundant data and expensive data science and business analytics will likely remain for some time. Thus, start a data analytics project to solve a specific problem or to take advantage of an opportunity to demonstrate value. Yet understand the long term value of saving any and all data for future analysis - as the specific use case arises. More importantly, it is crucial to spend time and resources to develop both an information management strategic plan and decision optimizing processes.

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