7 Vs of Big Data – what are they and why are they so important?
Share this post

Most companies these days are trying to make themselves more data-driven. One way to do this is by taking advantage of big data analytics, but it may be rather challenging. There are multiple types of big data analytics. Read about some big data use cases to learn how you can benefit from it.

A person interested in modern technologies can easily google examples of big data projects and get acquainted with cases of big data being used. The thing is that big data has become a buzzword. Everyone uses it, but except for data engineers and data scientists, nobody really knows what hides under this term and what the most common challenges are in this field. Before you decide to get into it and try leveraging the potential of enormous amounts of data, you should learn more about what big data is. 

Big data examples in real life

Are you wondering what types of digital data are used in big data? Business information comes in different volumes and formats. You would be surprised how much diversity there can be in this domain. Big data includes an enormous variety of structured and unstructured data, but popular definitions don’t really provide the reader with a lot of information. In order to explain it better, over time professionals from the IT industry came up with the 7 Vs of big data – certain characteristics that describe big data’s complexity. The 7 Vs stands for volume, variety, velocity, veracity, variability, visualization and value. Experts also speak of many more dimensions of big data, but if you understand these seven, you are well on your way to understanding big data. 

Why should you consider leveraging big data analytics?

Different types of big data can fuel different kinds of useful business feedback. Any industry can benefit from access to big data analytics. The efficiency of any department in any company can be improved thanks to the tips you can get from the analysis of various datasets. Zettabytes of data are generated every day. Think of big data as a bottomless well – you can draw from it for an infinite amount of time. 

There are multiple big data analytics use cases available in which you can find inspiration. You can analyze huge amounts of various kinds of information to transform your company outside and in. Examples of using big data in marketing and retail show how advanced analytics can help you improve your products and customer service. You can also perform data analysis in order to learn how to increase the productivity inside your company or how to reduce operational costs.

How to analyze big data – types of analytics

Advanced technologies such as machine learning, deep learning and artificial intelligence generally allow us to perform the highest level analytics and find patterns and connections between large datasets that could never have been identified with other methods and tools. There are many types of big data analytics. These are some of them.

Prescriptive Analytics

The goal of prescriptive analytics is to create the best possible recommendations for dealing with a particular situation at present using the available data. Yes, this kind of solution powers the recommendation engines that are so popular, especially in the e-commerce sector. It can also be used to formulate recommendations on how to optimize your business processes, so it is certainly a form of analytics that any company can benefit from.

Descriptive Analytics

The methods leveraged in descriptive analytics (such as clustering, segmentation or summary statistics) allow data scientists to interpret events. They analyze data regarding some situation in order to describe what has happened. The goal is to uncover all the details of the researched occurrence in order to understand it better. 

Diagnostic analytics

Sometimes things don’t work out the way we planned. You might want to learn why something went wrong in order to prevent such mistakes in the future. Diagnostic analytics can help you learn why an error occurred. This technology involves techniques such as data mining, drilling down and others. 

Predictive analytics

Who wouldn’t want to see into the feature? This type of big data analytics provides companies with extreme flexibility and the capability to adapt to market changes. Of course, you don’t get 100% certainty that your forecasts are right. Still, if you effectively perform data cleaning before running analysis and have good business intuition, you can partially predict the future of your industry, or at least prepare for many scenarios.

Real-time analytics

Analysis can be performed on historical data or in real-time (or almost in real-time) – as new information flows through your systems. For some types of business, processing data in real-time is indispensable. For example, this type of analytics is applied in cybersecurity solutions. Thanks to analyzing data live, advanced software can spot suspicious activity and protect your systems against cybercriminals.

Big data analytics use cases

As we mentioned earlier, big data analytics can be applied to improve operational efficiency and reduce costs in any company and any industry. Here are some examples of big data analytics that present the potential of this business solution. 

1. Analyzing big data – marketing examples

In order to promote services, products and ideas among consumers effectively, you have to provide the right audience with the right content at the right moment. To do so, ad publishers leverage advanced technologies such as, for example, AI-based contextual targeting or behavioral targeting. Both of these techniques require powerful tools that will make it possible to analyze past and real-time data and display ads based on the results. 

Big data analytics doesn’t only power marketing software, but also allows marketers to monitor the effectiveness of campaign strategies. This way, they can check if they’re working and improve them if necessary.

2. Uses of big data in banking

The banking industry deals with a lot of risks, which is why organizations in the banking sector require the highest quality big data analytics solutions. First, they need to implement the most reliable fraud detection systems and cybersecurity software to protect their resources as well as the privacy of customers and employees. Second, banks have to perform risk assessments all the time. Big data analytics provides them with recommendations on particular investments. There are even systems that analyze if some customers should be provided with funding or not. Nothing is left to chance. And finally, banks use big data analytics just the same way as any other company, to optimize marketing campaigns, recommend products and services, etc. 

3. Examples of big data in healthcare

Big data analytics is slowly entering the healthcare industry, and it has great potential in this field, along with other modern technologies, such as, for example, IoT or wearable devices. Special sensors collect patient data (heart rate, calories burned and even stress level). Nowadays, simple applications working with these devices can give users health recommendations based on real-time analysis of data fed to the device. In the future, such products can be developed further to be capable of alarming a person or medical services about life threatening conditions or events (for example, a heart attack). Additionally, advanced analytics is also used nowadays in diagnostics. 


Are you wondering how your company could benefit from big data analytics? We cooperate with companies from many industries from all over the world. Contact us and ask about typical big data analytics applications for your industry. 


Check out our blog for more details on Big Data:

Data Engineering

Share this post

Send Feedback