Big data analytics is often a complicated process of exploring massive and diverse datasets to uncover information such as underlying relationships or unknown patterns. The objective of these data analytics is to help companies make better decisions for their business.
Data analytics technologies and techniques typically provide a solution for analyzing data sets and drawing conclusions from them. Big data analytics refers to a highly sophisticated form of advanced analytics that includes predictive models, statistical algorithms and hypothetical analyses supported by advanced analytics systems.
What Is Big Data?
There has been the idea of big data in existence for many years. In particular, the mathematical concepts and statistical methods are very old but can only be applied with today’s technology and hardware power. Today, most companies have realized that they can apply analytics to big data sets and reap significant benefits. However, in the 1950s, decades before the term “big data” were coined, companies were already manually applying basic analytics to discover insights and trends.
What Are Big Data Analytics?
Big data analytics helps companies make the most of their data and uncover new opportunities. This leads to smarter business, more efficient processes, higher profits and happier customers. In a recent survey shows that the Big Companies use big data in the following ways:
1. Cost Reduction
Big data technologies such as Hadoop and cloud analytics can bring significant benefits to storing big data and unlock more efficient business processes.
2. Faster And Better Decision-Making
The speed of Hadoop and in-memory analytics technologies, together allied to the ability to examine new data sources, enables enterprises to instantly assess information and make insightful decisions.
3. New Products And Services
By using analytics to measure customer needs and satisfaction, you can ensure that customers get what they want. Using big data analytics, an increasing number of companies can develop new products that meet customer needs.
Examples Of Big Data Analytics
The best examples of big data analytics lie in both the public and private sectors. They range from targeted advertising, education and industries to real-life scenarios, guest services or entertainment. Considering that by 2020 there will be 1.7 megabytes of data per individual in the world every second, the potential for data-driven organizational growth in the hospitality industry is enormous.
Education
In education, the analysis of big data sets enables, among other things, personalized and dynamic learning programs and curriculum redesign. Big data has also transformed career assessment and forecasting systems.
Insurance Sector
The insurance sector is important not only for individuals but also for companies. The insurance sector is important because it supports people in times of hardship and uncertainty. The data collected from these sources come in many forms and changes at an enormous pace.
Public Sector
Like many other sectors, big data can greatly impact the government sector – at local, national and global levels. With so many complex issues on the agenda today, governments have a lot of work to do to make sense of the information they receive and make important decisions. Governments are confronted with a very large amount of data almost every day. They have to maintain many records and databases on their citizens. The analysis of this data helps governments in many ways. Examples include social programs and cyber security.
Banking Sector
In the banking sector, the amount of data is growing by the second. Exploring and analyzing big data for banking sector can help to uncover:
- credit card fraud
- debit card fraud
- bad credit risk management
- business cleanliness
- changes in customer statistics
- Money laundering
- Risk mitigation
Business Opportunities
Big data analytics offers several business benefits through advanced analytics systems, software, and powerful computing systems. These benefits include
- New revenue opportunities
- more effective marketing
- better customer service
- improved operational efficiency
- competitive advantages over competitors
Big Data Analytics Applications
Big Data analytics applications enable analysts, data scientists, statisticians, and other analysts to analyze increasing amounts of structured transactional and other forms of data that are not typically used by traditional BI and analytics software. Among these is a combination of semi-structured and unstructured data-for instance, web clickstream data, web server logs, social media content, customer email text and survey responses, cell phone logs, and hardware data collected by sensors in the Internet of Things (IoT).
Cloud Computing Clusters
The first big data systems were deployed primarily on-premises, particularly in large enterprises that collected, organized and analyzed large amounts of data. However, Hadoop vendors such as Cloudera-Hortonworks, which supports the big data framework AWS and Microsoft Azure for distribution in the cloud, have also made it easier to create and manage Hadoop clusters in the cloud. Users can now create clusters in the cloud, manage them as long as needed, and then remove them from the network, all for a pay-as-you-go price that does not require ongoing software licenses.
Analysis Of Large Supply Chains
Big data is becoming increasingly useful for supply chain analysis. Supply chain analysis uses big data and quantitative methods to improve decision-making throughout the supply chain. In particular, big data supply chain analysis extends advanced analytics datasets beyond traditional internal data from enterprise resource planning (ERP) and supply chain management (SCM) systems.
Informed Decision-Making
Big Data analysis of supply chain data also applies powerful statistical methods to new and existing data sources. The resulting data enables more informed and efficient decision-making that improves and enhances the value of the supply chain.
Potential pitfalls of Big Data analytics initiatives include the lack of in-house analytics capability and the expensive to employ skilled data scientists and engineers to fill in the gaps.
Conclusion
A better understanding of consumers should be of interest to marketers and will continue to be in the future. Big Data offers many new and accessible sources. But for marketing to be more effective in our saturated markets, we need – as always – to be asking the smart questions, inquiring of the correct individuals, and explaining why they express themselves or behave the way they do. In this sense, in the future, marketers will work more closely with IT and statistical experts. But they must continue to cultivate the old virtues when working with consumer information.
