The demand for tracking and analyzing data keeps increasing. Each time we use the internet, some data is generated. These data are structured, semi-structured, or unstructured and are referred to as big data.
Hence, analyzing these data is critical to help understand trends and reveal information in the use of technology.
What does big data analytics mean?
Big data analytics is the process of perusing big data to get information such as correlations, hidden patterns, market trends, and user preferences to help organizations make informed business decisions.
Big data, in a nutshell, is a bundle of data with enormous data size and records exponential increases. This means the data set has high complexity, and none of the traditional data processing tools can store or process it efficiently.
Examples of big data include the New York Stock Exchange and social media. The New York Stock exchange generates about one terabyte of new trade data every day, while the Facebook site data size increases by over 500 terabytes of data daily.
This data is generated from messages exchanged and comments passed on the site, photos and videos uploaded. Big data is usually characterized by the following:
- Volume
- Variety
- Velocity
- Volume: This refers to the data size, and it is the most important criterion for classifying data as big data.
- Variety: This refers to the heterogeneity of the various sources of the data and the nature of the data.
- Velocity: The speed of data generation can also contribute to its size. How fast the data is generated and processed to meet the demands determines the real potential of the data.
What does a prominent data analytics consultant do?
A data analytics consultant is a professional who provides guidance and direction to those who want to improve their data analytics processes. Usually, data analytics consultants are independent or self-employed.
However, they can work for consulting firms and in corporate settings. The responsibilities of prominent data analytics consultants vary depending on where they work and their clients. Some of their duties include:
- Managing and developing databases and data maps
- Evaluating data processing to discover potential problems
- Developing data models and optimizing data processes
- Recommending possible data use improvements
- Helping teams navigate data management procedures
- Assisting with data integration, transfer, and warehousing
- Creating data analysis reports and dashboards
- Developing and following confidential data management protocol
- Advising groups on proper data cleanup and storage
Big Data Analytics Tools
With increasing demand to track data, it has become crucial for businesses to maintain consistency and relevance by collecting and analyzing meaningful data. There are numerous data analytics tools, but selecting the right one depends on your needs and goals. Below are some popular big data analytics tools.
Apache Hadoop
This tool is the best big data analytics tool used by many giant tech companies like Microsoft, IBM and Amazon. Apache Hadoop is a java-based open-source platform that processes and stores big data.
It can process data, whether structured or not, from one server to several other local devices. Furthermore, Hadoop also offers cross-platform support for its users.
Other features of Hadoop include:
- It offers quick access via its file system, HDFS (Hadoop Distributed File System)
- It can work on small commodity hardware like JBOD or some other type of disk.
- It is highly scalable and can distribute a large amount of data in smaller segments.
R Programming
R is a programming language that data analytics is used for statistical computing and extensive data analysis. It provides various statistical tests, including matched pairs and association tests.
Other features provided by the R programming language include
- R produces plots and graphics that are ready for publication
- R is integrated with the Python programming language giving it faster, up-to-date, and accurate analytics
X Plenty
X Plenty is an analytic data software for building data pipelines. The significant features of Xplenty include
- Data Security
- Rest API
Apache Spark
Spark is an analytical framework used to process data and perform multiple tasks on a large scale. It is also used to process data via various computers with the help of distributing tools. Spark allows users to work using their preferred programming languages, such as java and python.
Rapid Miner
Rapid Miner is a fully automated visual workflow design software for data analytics. A unique perk of Rapid Miner is that it is a no-code platform, and it requires little coding experience by the user to segregate data.
Big Data Analytics in Healthcare
One notable application of big data analytics is in healthcare. Big data analytics has helped predict outbreaks of pandemics, avoid preventable diseases and deaths, reduce the cost of treatment and improve the standard of living.
Furthermore, other applications include Patients prediction for improved staffing, Electronic medical records, Aids in managing and tracking mass diseases and improved drug prescription process.
Big data analytics aids governmental and non-governmental organizations in strategic planning and policy formulation that addresses specific health issues.
As stated above, it is precise data analytics in healthcare proves that medical applications will save numerous lives.
Conclusion
More companies are turning to big data; hence, there is increased demand for analytics consultants to develop, manage and oversee the smooth running of the database.
Big data analytics is being utilized more in every industry. However, its role in healthcare might have the greatest impact on our lives.
