Big Data Analytics: Information Management Challenges and Considerations

Related Topics: Data & Analytics

by Sai Yammada – 

Businesses want insights and insights require data.

Typically, IT Organizations with a technology focus setup Big Data infrastructure with in-house expertise and bring in data manually through native tools.

When the business is ready to look into the data, it does not know where to look. Why? Because there is little or no metadata management planned. And, the business needs reassurances and answers to their questions:

  • Is the data trustworthy?
  • Were data quality rules implemented and enforced?
  • How can they access the data?
  • Were all the possible data access patterns during the design?

To effectively operationalize the data lake, Big Data requires good data lake architectures and solid strategies for governing. Before deciding upon technologies and tools, the data lake architecture should incorporate best practices for:

  • Data Lineage
  • Metadata management
  • Governance
  • Data quality
  • Data flow strategy
  • Information lifecycle management

So if you are starting with your big data journey, make sure you have the proper business alignment for your Information management, make sure you have at least a basic data governance model to start with, and a solid metadata management strategy.

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