Definition:
Big data analytics is the often complex process of examining big data in order to uncover information – such as hidden patterns, correlations, market trends, and customer preferences – that can assist organizations in making informed business decisions. Data analytics technologies and techniques, on a broad scale, enable organizations to analyze data sets and gather new information. Queries in business intelligence (BI) provide answers to fundamental questions about business operations and performance. Big data analytics is a type of advanced analytics that involves complex applications powered by analytics systems that include elements such as predictive models, statistical algorithms, and what-if analysis.
How it work/uses:
There are four steps in the big data analytics process. Data professionals collect, process, clean and analyze growing volumes of structured and unstructured data. Many different types of tools and technologies are used to support big data analysis processes. Common technologies and tools used include:. Hadoop, which is an open source framework for storing and processing big data sets. Data lakes are large storage repositories that hold native-format raw data until it is needed. Big data mining tools enable businesses to mine large amounts of structured and unstructured big data. Cloud platform vendors have made it easier to set up and manage Hadoop clusters in the cloud. Streaming analytics applications are becoming common in big data environments. Big data expands data sets for increased analysis that goes beyond traditional ERP and SCM systems. Also, big supply chain analytics implements highly effective statistical methods.Personalization engines like those used by Amazon, Netflix, and Spotify can improve customer experiences and foster customer loyalty. Predictive analytics models can aid in the proactive replenishment of B2B supplier networks. Big data analytics can detect new risks based on data patterns, allowing for more effective risk management strategies.
Application:
Government: The National Security Administration (NSA) monitors the activities of the Internet constantly in search for potential patterns of suspicious or illegal activities. Civil registration and vital statistics (CRVS) collects all certificates status from birth to death. CRVS is a source of big data for governments.
International development:
Big data technology can make important contributions but also present unique challenges to international development. Advancements in big data analysis offer cost-effective opportunities to improve decision-making in critical development areas. The challenge of “big data for development” is currently evolving toward the application of this data through machine learning.
Advantages:
Rapidly analyzing large amounts of data from various sources in a variety of formats and types. Making better-informed decisions more quickly for effective strategizing, which can benefit and improve the supply chain, operations, and other strategic decision-making areas. Cost savings that can be realized as a result of new business process efficiencies and optimizations.A better understanding of customer needs, behavior, and sentiment can lead to better marketing insights and product development.Improved, more informed risk management strategies based on large data sample sizes.
Disadvantages:
With more data coming in from a variety of sources and formats, big data data quality management requires a significant amount of time, effort, and resources to properly maintain. Organizations must understand how to select the best tool for their users’ needs and infrastructure. Addressing security concerns in such a complicated ecosystem can be a difficult task.
Reference:
Hilbert, Martin; López, Priscila (2011). “The World’s Technological Capacity to Store, Communicate, and Compute Information”. Science. 332 (6025): 60–65. Bibcode:2011Sci…332…60H. doi:10.1126/science.1200970. PMID 21310967. S2CID 206531385. Retrieved 13 April 2016.
Breur, Tom (July 2016). “Statistical Power Analysis and the contemporary “crisis” in social sciences”. Journal of Marketing Analytics. London, England: Palgrave Macmillan. 4 (2–3): 61–65. doi:10.1057/s41270-016-0001-3. ISSN 2050-3318.
boyd, dana; Crawford, Kate (21 September 2011). “Six Provocations for Big Data”. Social Science Research Network: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. doi:10.2139/ssrn.1926431. S2CID 148610111.
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