When companies invest in strategic big data analysis approaches and effective big data analytics tools, they have the potential to deliver an impressive ROI and transformative results.
Gartner reports that business leaders who leverage data and analytics to set goals and guide priorities can increase the overall business value they generate for their organizations by a factor of up to 2.6x.
In this guide, we’ll go over everything you need to know about big data analysis, including what big data analysis is, examples of common business use cases for big data analytics, and the top benefits and challenges of big data analysis.
To answer the question “What is big data analysis?” we first need to answer, “What is big data?”
Big data refers to massive datasets, including both unstructured data and structured data, often used in data mining or machine learning. By reviewing large datasets, organizations can uncover insights and make predictions, using algorithms powered by either human or artificial intelligence, or a combination of both.
And it’s not just large companies that have to worry about using big data analysis to generate business intelligence to inform real-time decision-making.
Currently, small and mid-size businesses are amassing terabytes of data, while brands like Twitter manage hundreds of petabytes of data. As a result, companies must figure out what to do with data, learn from it, and take the best actions.
Data scientists and data analysts at companies of all sizes practice big data analytics to uncover trends and insights that can optimize their pricing strategies, product development roadmaps, supply chain management, and overall customer experience outcomes.