Big Data and Analytics Explained (Video)

Big Data and Analytics are not just buzzwords, in this video, from Harvard Business Review you’ll get to know the meanings, definitions, types, and why they could be a key competitive advantage for your company.

What are the Big Data Analytics Definitions?

  • Big data: refers to the vast volumes and types of information that companies can collect from both internal and external then process it using high-tech systems and applications.
  • Analytics: is the use of math and statistics to derive meaning from data in order to make a better business decision.

What is Data Analytics Types?

  • Descriptive analytics: include for example dashboards, scorecards, and alerts. They describe what happened in the past.
  • Predictive analytics: those are more useful; they use past data to model future outcomes. This may be indicating how customers will spend within a certain period, or how sales would be affected with a certain promotion in question.
  • Prescriptive analytics: those are the most advanced ones; they use techniques like optimization or a/b testing to advise decision-makers on the best decision or act that can be done.

How can big data and analytics help your organization gaining a competitive advantage?

Organizations that succeed to combine big data with effective analytics could have one of the key competitive advantages for the current age. In fact, several requirements should be thought it is known as the DELTA model:

  • Data: the data must first be clean, common, integrated, and accessible in a central data warehouse.
  • Enterprise: an enterprise-wide focus with key data systems and analytics resources available to the whole firm, not just isolated teams.
  • Leadership: leaders should fully embrace data analytics and lead the company’s culture toward fact-based decision making.
  • Targets: data and analytics teams must be dedicated for very specific targets (such as marketing or supply chain), over time, the use of analytics and analytical decision making will expand across the whole organization. But the start should be always with a targeted project that can display real results on the bottom line.
  • Analysts: organizations can’t become more analytical without analysts located throughout the company and across all functions.

Want to know more about data analysis execution techniques?

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