Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.
Why is Big Data Analytics Important?
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits, and happier customers.
- Cost reduction. Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
- Faster, better decision making. With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
- New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. Davenport points out that with big data analytics, more companies are creating new products to meet customers’ needs.
Uses of Big Data Analytics across Different Industries:
Large amounts of information will be streaming in into banks, managing all this data and getting proper insights would be possible only with big data analytics. This is important to understand customers and boost their satisfaction, and also to minimize risk and fraud.
When government agencies are able to harness and apply analytics to their big data, they gain significant ground when it comes to managing utilities, running agencies, dealing with traffic congestion or preventing crime.
3. Health Care
Patient records, Treatment plans, Prescription information. When it comes to health care, everything needs to be done quickly, accurately. And, in some cases, with enough transparency to satisfy stringent industry regulations. When big data is managed effectively, health care providers can uncover hidden insights that improve patient care.
Educators armed with data-driven insight can make a significant impact on school systems, students, and curriculums. By analyzing big data, they can identify at-risk students, make sure students are making adequate progress, and can implement a better system for evaluation and support of teachers and principals.
Armed with insight that big data can provide, manufacturers can boost quality and output while minimizing waste – processes that are key in today’s highly competitive market. More and more manufacturers are working in an analytics-based culture, which means they can solve problems faster and make more agile business decisions.
Customer relationship building is critical to the retail industry. And the best way to manage that is to manage big data. Retailers need to know the best way to market to customers. The most effective way to handle transactions, and the most strategic way to bring back lapsed business. Big data remains at the heart of all those things.
Advanced analytics describes data analysis that goes beyond simple mathematical calculations such as sums and averages, or filtering and sorting. Advanced analyses use mathematical and statistical formulas and algorithms to generate new information, to recognize patterns, and also to predict outcomes and their respective probabilities.
Predictive analytics is a sub-division of advanced analytics and focuses on the identification of future events and values with their respective probabilities.
Advanced and predictive analytics can be applied to a variety of use cases, ranging from classic customer value and success forecasts, the prevention of contract termination or price, sales and demand forecasts to new tasks such as the prediction of machine failures, social media monitoring and evaluation, and predictive policing.
Advanced analytics enables both “optimization” and “innovation”. It can support the improvement of existing processes, for example, in the form of more precise sales planning (and therefore production and purchasing planning). Also, new insights from advanced analyses can highlight potential new business or even make new products and services possible.
The following tasks can be addressed using advanced analytics:
- Segmentation: creation of groups based on similarities between objects
- Association: identification of the frequency of joint occurrences and, if necessary, the derivation of rules such as “From A and B (usually) follows C”
- Classification: for example, of hitherto unclassified elements
- Correlation analysis: identification of relationships between element properties
- Forecast: derivation of future values.
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