When it comes to the structure of big data, you can consider it a collection of data values, the relationships between them together with the operations or functions which can be applied to that data.

These days, lots of resources (social media platforms being the number one) have become available to companies from where they can capture massive amounts of data. Now, this captured data is used by enterprises to develop a better understanding and closer relationships with their target customers. It’s important to understand that every new customer action essentially creates a more complete picture of the customer, helping organizations achieve a more detailed understanding of their ideal customers. Therefore, it can be easily imagined why companies across the globe are striving to leverage big data. Put simply, big data comes with the potential that can redefine a business, and organizations, which succeed in analyzing big data effectively, stand a huge chance to become global leaders in the business domain.

Structures of big data

Big data structures can be divided into three categories – structured, unstructured, and semi-structured. Let’s have a look at them in detail.

1- Structured data

It’s the data which follows a pre-defined format and thus, is straightforward to analyze. It conforms to a tabular format together with relationships between different rows and columns. You can think of SQL databases as a common example. Structured data relies on how data could be stored, processed, as well as, accessed. It’s considered the most “traditional” type of data storage.

2- Unstructured data

This type of big data comes with unknown form and cannot be stored in traditional ways and cannot be analyzed unless it’s transformed into a structured format. You can think of multimedia content like audios, videos, images as examples of unstructured data. It’s important to understand that these days, unstructured data is growing faster than other types of big data.

3- Semi-structured data

It’s a type of big data that doesn’t conform with a formal structure of data models. But it comes with some kinds of organizational tags or other markers that help to separate semantic elements, as well as, enforce hierarchies of fields and records within that data. You can think of JSON documents or XML files as this type of big data. The reason behind the existence of this category is semi-structured data is significantly easier to analyze than unstructured data. A significant number of big data solutions and tools come with the ability of reading and processing XML files or JASON documents, reducing the complexity of the analyzing process.

Conclusion

While data analytics aren’t new, the emergence of big data has dramatically changed the nature of work. It’s important for businesses looking to make most out of the big data to try to adopt advanced tools and technologies to keep up with the pace at which the data is growing.

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While the concept of big data has been around for a significant number of years, everything has started to change with the emergence of big data analytics. This process allows businesses to perform analytical procedures efficiently and quickly, giving them a competitive advantage over competitors. Here’re some of the most prominent real-world examples of how big data analytics is being used.

1- Healthcare industry

The entire healthcare industry is getting transformed with the help of big data analytics. The ability to provide hyper-personalized patient treatment, improve the quality of life of the patients, as well as, discover medical breakthroughs – all have been impacted by big data analytics. In this industry, big data analytics isn’t performed with the focus of finding new product opportunities or increasing profits. Instead, it’s all about applying and analyzing big data to offer a better patient-centric approach. For instance, healthcare providers are analyzing historical big data to analyze and identify certain risk factors in patients, which is extremely useful for early detection of diseases, enabling both the patients and doctors to take action sooner.

2- Retail industry

Probably the maximum implementation of big data analytics can be observed in the retail industry. As the industry has gone digital, the customers have also started to expect a better and seamless experience. With the help of big data analytics, retail companies have become in a position to understand their customers more and thus, to provide a variety of personalized services. From creating product recommendations based on a customer’s past searches to demand forecasting to performing crisis control – everything is being taken care of through big data analytics.

3- Media and entertainment industry

The media and entertainment industry is one of the biggest users of big data analytics. As the number of users of different digital gadgets is increasing rapidly, media and entertainment companies are leveraging the power of big data analytics to a great extent. Some of the biggest benefits that are being experienced by the industry include on-demand or optimized scheduling of media streams, getting actionable insights from customer reviews, predicting the actual interests of audiences, successful targeting of the advertisements, and many more.

Final Thoughts

For any business, big data analytics is a crucial investment that can help to optimize the real-life situations where common people are involved to a great extent. Implementation of big data analytics not only helps businesses to achieve competitive advantage but also drives customer retention and reduces the cost of operation. And as technological advancements steadily continue to emerge, big data analytics will become even more important to businesses across industries.

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1- Hadoop

One of the most popular big data analytics tools, Hadoop is an open-source framework and provides massive storage for all types of data. With its exceptional processing power and ability to deal with numerous tasks, Hadoop keeps professionals from worrying about hardware failure.

2- OpenRefine

This big data analytics tool lets professionals clean up data for analysis. It comes with cells under columns which is similar to relational database tables. With this tool, you’d be able to perform things like cleaning messy data, the transformation of data, parsing data from websites etc.

3- RapidMiner

RapidMiner is one of the big data analytics tools that offer machine learning procedures together with data mining techniques like data visualization, processing, predictive analytics etc. Apart from business and commercial applications, this big data analytics tool is used for application development.

4- Apache Spark

It’s an open-source and powerful big data analytics tool that comes with a huge number of high-level operators which make it easy to develop parallel apps. It not only offers lightning-fast processing but also comes with lots of abilities including helping in running an application in Hadoop cluster, offering built-in APIs in Python, Scalar, or Java, being able to integrate with Hadoop etc.

5- MongoDB

This big data analytics tool is a contemporary alternative to databases. Its best application can be found when it comes to working with databases that change or vary frequently or the ones which are unstructured or semi-structured. Some of its best uses include product catalogs, content management systems etc.

6- Lumify

You can consider this big data analytics tool as a big data analysis, fusion, and visualization platform. It helps professionals to explore relationships and explore connections in their data through a suite of analytic options. It’s built on scalable big data technologies and comes with interface elements for images, videos, and textual content.

7- Tableau

This is one of the leaders in big data analytics tools and a viable option for non-data scientists engaged in different organizations. A big benefit of using this big data analytics tool is that professionals can reuse existing skills when it comes to big data. Tableau uses a standardized SQL to query, as well as, interface with big data systems and thus, makes it possible for companies to use an existing database to identify the insights they’re looking for, from a massive dataset. It’s also equipped with the VizQL data visualization technology that allows for data visualization without organizing the data first.

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