Three Big WHATs to Identify Big Data Challenges

Tuesday, April 17, 2012 | comments

Big data means both big opportunities and big risks for organizations, at Three Big Why for Big Data, we clarify why Big Data is such a big deal, also why Big Data may cause big distraction, here we further describe the three big WHATs to identify the potential challenges facing organizations when manage Big Data.

1. What are characteristics of Big Data?

From wiki, Big data has the following characteristics: Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible:
  • Petabytes/exabytes of data,
  • Millions/billions of people,
  • Billions/trillions of records,
  • Loosely-structured and often distributed data,
  • Flat schemas with few complex interrelationships,
  • Often involving time-stamped events,
  • Often made up of incomplete data,
  • Connections between data elements that must be probabilistically inferred
  • Applications that involved Big-data either Transactional or Analytics

 2. What are the biggest challenges to manage Big Data?

  •  Increasing volume of unstructured data
  • Range/variety of data types/sources
  • Complexity of individual data types
  • Speed/velocity of data
  • Providing analytics from a range of data sources
  • Cost of managing data
  • Data storage
  • Security of data
  • Data governance, regulatory compliance, risk management
  • Energy/power consumption of data
  • Providing business insight from massive amounts of data

3. What are the biggest barriers to facilitate Big Data project?

  • Lack of the right mindset and culture
  • Lack of management support and executive sponsorship
  • Lack of talent, expertise & experience,
  • Cost and availability of training
  • Lack of knowledge of big data framework and tool implementation
  • Lack of knowledge of available big data management
  • Resources & Budget constraints
  • Lack of other resources (IT, fiscal, etc)

Based on the three Big WHATs, organization can well understand the challenges and better evaluate Big Data Life Cycle Management solutions, to achieve the expected business results and goals.Twitter: @pearl_zhu
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