Data analysis technicians, data scientists, predictive modelers, and statisticians work together to extract, process, clean, and analyze text-based data and transactional data from a growing number of sources.
1.Data professionals combine data from a number of different sources. Often, it is a mix of semistructured and unstructured data. Each organization will use different streams of data, but some frequently-used sources include semistructured and unstructured data.
Internet clickstream data
Web server logs
Social media content
Text from customer emails and survey responses
Mobile phone records and
Machine data captured by sensors connected to the internet of things (IoT).
2.Data is gathered, processed, and stored in a data warehouse or data lake. Data now stands ready for processing by data professionals. They arrange, configure, and partition the informational material into segments as needed to produce analytical reports in ways that are more effective and more scalable with high-performing analytical queries.
3.Data is cleaned up to make its quality better. Data specialists use scripting tools and data quality software to clean data. They search for flaws or discrepancies, such as duplication or formatting errors, and tidy up the data.
4.Data that has been collected, processed, and cleaned is analyzed using analytics software. This includes tools for:
Data mining, which sifts through data sets in search of patterns and relationships.
Predictive analytics, which builds models to forecast customer behavior and other future actions, scenarios and trends.
Machine learning, which taps various algorithms to analyze large data sets.
Deep learning, which is a more advanced offshoot of machine learning.
Text mining and statistical analysis software.
Artificial intelligence (AI).
Mainstream business intelligence software.
Data visualization tools.
Contemporary supply chain analytics has become increasingly valuable thanks to big data and quantitative methods. Big supply chain analysis techniques make use of big data and mathematical methods in the supply chain. Specifically, big supply chain analytics expands data sets for increased analysis that goes beyond the traditional internal data found on enterprise resource planning (ERP) and supply chain management (SCM) systems. Also, big supply chain analytics implements highly effective statistical methods on new and existing data sources.