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Big data is a term that refers to large datasets that cannot be processed using standard database software. When big data involves the analysis of massive amounts of information to find patterns and meaning, it is often referred to as “analytics” or “analytical” data.
Data science is an interdisciplinary field involving the exploration and analysis of data in order to understand its properties and extract useful knowledge from it. Analytics is the process of exploring data sets for insights, which can lead to new discoveries and opportunities for growth. As a result, organizations are adopting analytical technologies to process big data and uncover insight from raw information.
TOP 5 Applications of Big Data
Table of Contents
1. Business Intelligence (BI)
Business intelligence (BI) is the process of collecting, analyzing, and distributing data within an organization. It’s a way of monitoring performance and is often used for decision-making and forecasting. BI is also referred to as “analytics” or “analytical” data. What’s the difference between analytical and BI data? In short, analytical data is sourced from a single source and is typically processed by individuals.
BI data can come from many different sources, both internal and external. BI data is used to identify patterns and make predictions about future outcomes. For example, if you’re running low on a specific inventory item, you can look at your BI data to determine the next best course of action. BI data can come from a variety of sources, including sensors in production lines, customer surveys, social media channels, and more.
2. Data Science
Data science involves the exploration and analysis of data in order to understand its properties and extract useful knowledge from it. Data scientists are responsible for exploring and analyzing data, discovering hidden insights, and crafting stories that communicate those insights to stakeholders.
Data scientists use tools and their experience to find patterns in data. They can use big data to find insights, patterns, and connections that an organization may not have otherwise noticed. Data science can be applied in a variety of industries, including healthcare, marketing, finance, and more.
3. Predictive Analytics
Predictive analytics uses a combination of algorithms and historical data to forecast future outcomes. For example, a retailer might use predictive analytics to determine what products customers are likely to purchase based on shopping history.
Predictive analytics can also be used for risk analysis by examining historical data to forecast the probability of a certain event or outcome occurring. Predictive analytics is often done in tandem with data visualization. Together, these two tools can help organizations better understand their data and make strategic business decisions based on the findings.
Predictive analytics is used in a variety of industries, such as marketing, finance, HR, and supply chain management.
4. Real-time Analytics
Real-time analytics involves collecting and processing data in real-time to provide insight as it occurs. This is different from historical data, which is typically collected and stored as a snapshot of data at a certain point in time. Real-time analytics is often used in industries like healthcare, retail, and finance.
For example, a retail store might use real-time analytics to track customer visits and purchases during the holiday season. This information can then be used to inform store employees about the best ways to handle the influx of customers. Real-time analytics can also be used to track system performance. For example, a utility company might use real-time analytics to ensure its grid is functioning properly after a storm.
5. Recommendation Engines
A recommendation engine uses historical data and machine learning algorithms to determine what products a customer is likely to purchase. A recommendation engine might take into account past purchases, search history, browsing history, and other factors to present customized recommendations for future purchases. Retailers like Amazon, Target, and Etsy use recommendation engines to suggest products to their customers.
These recommendations are typically based on what other customers with similar purchasing patterns have purchased. For example, if a previous customer purchased a blender and an ice tray, a recommendation engine might suggest a juicer next.
Summing up: Applications of Big Data
Big data refers to large amounts of data that cannot be processed using standard database software. Analytics is the process of exploring data sets for insights, which can lead to new discoveries and opportunities for growth.
Predictive analytics uses a combination of algorithms and historical data to forecast future outcomes, while real-time analytics collects and processes data in real-time to provide insight as it occurs. Recommendation engines use historical data and machine learning algorithms to determine what products a customer is likely to purchase.