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According to independent research organization SINTEF (The Foundation for Scientific and Industrial Research), 90 percent of all the data in the world has been generated over the last two years. However, more and plentiful data has not led to better insights and better decisions necessarily. What’s the missing ingredient?

The Latin word, “datum”, which means a “thing given” is where the term data originates. In their book, Data Fluency, authors Zach and Chris Gemignani who founded Juice Analytics speak about the lack of data literacy as a social, and not a technological problem. This ‘last mile’ problem prevents organizations and decision-makers from understanding and using data. TechTarget defines Data Literacy as: The ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word.

What does this imply for data science and business analytics professionals?

The complexity of data analysis, especially in the context of big data, means that data literacy requires some knowledge of mathematics and statistics. However, data literacy goes far beyond statistical knowledge and applications. In the emerging context of data science and business analytics, data literacy would include collection, presentation, analysis, visualization, management and preservation of large collections of information (Stanton, J., 2012).

Therefore, the repertoire of skills it would require includes

  • Analytical skills e.g. requirements and workflow analysis, data modelling, data transformation needs analysis and data provenance needs analysis
  • Hacking skills including on data products, tools and data sources
  • Data management skills including metadata standards, encoding language, semantic standards
  • Technology and communication skills