Analytics and Data Science
Big Data and Data-Driven
big data is a concept that refers to the large amount of data, structured and unstructured, that is generated daily, from different sources, such as social networks, mobile devices, sensors, images, audio, external market and competition data, among others.
The concept of data-driven refers to a data-driven approach to decision making. This means that companies use data collected from diverse sources to direct their strategies and actions, rather than relying on intuitions or assumptions.
The advantages of adopting a data-driven approach are diverse. Among them, the following stand out:
- Better understanding of the target audience and their needs, allowing the creation of more relevant and effective products and services.
- Identification of market and competition opportunities, enabling the creation of more efficient strategies.
- Reduction of costs and waste, through the optimization of processes and resources.
The relationship between these concepts is that Big Data provides the raw material for the Data-Driven approach.
Importance of data science, analytics and machine learning technologies to support company decisions
Data science, analytics and machine learning technologies are fundamental for companies that want to make the most of all the different data sources available. These technologies allow companies to analyze large amounts of data quickly and efficiently, identifying patterns and trends that can be used to make more informed and strategic decisions.
Data science is an area that focuses on developing methods and techniques to collect, process, analyze and interpret data. Machine learning is a subfield of data science that focuses on developing algorithms that can learn from data. With an Analytics platform, such as Oracle Analytics Cloud, you will be able to view this information in a more friendly, fluid and interactive interface for decision makers.
MPL can support you on your “Data-Driven” journey because we specialize in Oracle Cloud technologies, which cover all phases of a modern data project:
- Integration with all data sources of interest (internal and external)
- Governance and storage
- Data analysis and interpretation
- Artificial Intelligence/Machine Learning
- Data visualization
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