Big data is a field that was developed for organizations to process, analyze, and extract information from datasets that are too large for traditional data collection methods. Today, we will be talking about the big data industry, including what kinds of industries use this data, real world examples of big data’s usage, and ways that machine learning is used in this field.
What industries use big data?
According to a 2020 report by Market to Market, the global big data market is currently worth $138.9 billion. This number is supposed to compound annually at a rate of 10.6%, leading to a $229.4 billion market value by 2025. The value of this industry comes from the highly sought-after nature of collecting and analyzing data in this form. An array of fields utilize big data to help dictate strategic initiatives, including retail, healthcare, education, manufacturing, supply chain management, professional services (transportation, law, accounting, etc.), government services, and more.
Big data examples
- In the retail industry, big data is used to optimize pricing strategies, forecast demand, and stay ahead of product trends.
- In the healthcare industry, big data helps to detect and prevent disease, predict epidemic outbreaks, and provide evidence-based medicine through the analysis of previous patients.
- In the education industry, big data is used to help refine course content, generate dynamic learning materials, and predict likely career paths for students.
- In the manufacturing industry, big data is used to improve the efficiency of machines and gather information about the performance/usage of the products they produce.
- In the supply chain management industry, big data is used to make ordering systems more efficient, optimize transportation routes, and improve vehicle maintenance processes.
- In the professional services industry, big data is used to predict customer demand, optimize pricing strategies, and automate transaction-based importing/tracking processes.
- In the government industry, big data is used to continuously improve the efficiency of public services (traffic patterns, recycling collection, etc.), identify suspicious claims or trading activity, and enhance emergency response processes.
Ways machine learning helps to capitalize on big data
Companies use big data to gain greater insights into their strategic objectives and initiatives. Machine learning amplifies this benefit by using decision-making algorithms to recognize patterns in large amounts of big data and efficiently collect/process this information. ML helps to provide a more complete understanding of what the data means and can even automate specific steps in the decision making process.
Machine learning + big data use case examples
- Market research
- Target audience segmentation
- Predicting user behavior
- Social network/streaming recommendations
- Predictive analytics
- Fraud detection
How to implement machine learning into your big data
Understanding the importance and usefulness of machine learning in big data analytics is only the first step in extracting valuable insights for your business. Next, you’ll need to implement a ML system into your own company. Algorithmia makes gaining big data insights fast and simple by hosting a serverless microservices architecture that allows enterprises to deploy, serve, and manage their own machine learning models at scale with ease. See how Algorithmia can help your organization in our video demo.