With the recent advancements made in Big Data technology, there is no looking back for the days of analysis through various traditional data sources. It is believed that the human mind is capable of processing unbelievable amounts of information. The only issue now lies on the accuracy and reliability of the information which is being processed. With the availability of Big Data technologies, analysts can process this information accurately and easily. In essence, Big Data analytics is a way of understanding data from all available sources and formulating new ways of action for the betterment of an organization or a business.
What is Big Data Analytics?
Meaningful insights into the relationships, trends and patterns that exist in huge unprocessed big data storage can be very hard to get otherwise. However, the new techniques and tools used in big data analytics allow analysts to learn much more from big data sets. This includes real time data from any source, size and format.
Why is Big Data Analytics important?
Some of the benefits which have accrued from big analytics include the following:
- Improved collaboration
- Better decision making
- Better product or service design
- Improved understanding of customer trends
- Easier tracking of product life-cycle
- More accurate forecasts
- Easy analytical solutions to complex problems
- Real-time insights.
In essence, big data analytics helps in providing immediate insights which can help make decisions in short notice and improve performance. For instance, if one part of your team is facing trouble, you can access the data store to find out the root cause of the problem and carry on with the necessary corrective measures. In addition, with improved insight and prediction of market trends, organizations can make decisions that lead to growth in markets where they are weak and expand markets where they are strong.
Role of Big Data in Businesses
Big data has also led to the development of many analytics tools and infrastructures that are designed for big data analytics purposes. These tools include: spark, and, elastic, and many others. spark has been the most popular amongst all the other analytics tools, thanks to its simple and flexible architecture. It has the ability to support a wide variety of operational functions such as graphs, data filtering, and range of linear and non-linear transformations. Big Data analytics tools make heavy use of the Hadoop framework, which makes it highly efficient and effective in applications related to Map-reduce, metropolis-sphere, and social web.
Data mining and natural language processing technologies have made big data analytics incredibly efficient. The process involved in data mining involves finding a piece of information which is relevant to the user and then using this information to build an argument against a particular solution or for a particular policy. Data mining uses large scale databases and is usually implemented through supervised or unsupervised mode, with some degree of input from users.
Big Data analytics and data warehouse development involve an improved model of business processes. Traditional data warehouse techniques involve storing all customer data in structured formats (RDBMS’s). In order to utilize the benefits of Big Data and to ensure better performance and scalability, unstructured data is stored in a relational database or an object-oriented database. Big Data analytics helps in improving overall business performance by making decisions about strategic alignments between the various functional departments based on actual customer interaction. Big Data analytics thus refers to a collection of data and analytics technologies which have proven useful for businesses dealing with large amount of data, unstructured or structured.
Another emerging concept in the field of Big Data analytics is machine learning. Machine learning involves the use of databases, either structured or unstructured, in order to generate future recommendations by using a mathematical algorithm. Examples of machine learning technologies that are commonly used for analytics are neural networks, deep learning, decision trees and natural language processing technologies. With the help of these technologies, businesses can use the collected data in order to make reliable and accurate predictions and also make choices for their next strategic move.
AI-driven systems enable smart workflow for businesses as they are built to handle Big data effectively. This creates a considerable customer base resulting in wide interaction. With customer data in hand, how is that one is going to deal with them? Not all require the same thing. Developed with AI technology, ONPASSIVE has come up with various products that help businesses grow. An effective AI-driven tool, O-Lead gathers customer information based on their preferences and are given responses accordingly.
Apache Spark is one of the most widely used data analytics applications in the world today. It was developed by the Lawrence Journal-World Online to support distributed systems. Apache Spark includes several different components namely, the Pigtail network, the Hadoop Distributed Computing framework, and the Spark ML. The Spark software is written in Java and is therefore compatible with the various JAVA platforms. Apache Spark is written in Java script and can be easily run using the Apache Tomcat application server. To enhance its performance, Apache Spark includes memory usage monitoring tools like the Memory Monitor.