Whatever activities we undertake in the digital world, we all leave digital traces. Examples of these traces are our social media activities and visitor behavior on a website. But you also leave traces in the offline world. You can think of the tracking of traffic flows or the use of smart meters within the energy sector. The amount of data is increasing more in both structured and unstructured form. The challenge for organizations now is to analyze this data and then use it strategically. Thanks to Big Data, organizations get a 360-degree view of their customers. This allows companies to better respond to the needs of their consumers. But what is Big Data exactly? And what are the characteristics? Below, we give an overview.
What is Big Data?
Big Data has many different definitions. The definition we use is that of Bernard Marr, Big Data expert and writer of the bestseller Big Data:“Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance”(2014): Big Data refers to our possibilities to use and to analyze the ever-increasing amount of data. But this definition is of course very general. That is why we specify Big Data based on the Five V’s.
The Five V’s of Big Data;
- Volume
Organizations are increasingly receiving data from both internal sources (e.g. website) and external sources (e.g. social media). This amount of data is so large that according to Big Data guru Bernard Marr you should think of zettabytes or even source bytes. Traditional database technology is usually not suitable for Big Data, because the data sets are often too large to be stored and analyzed. But even for this there is a solution. New distributed systems, such as Hadoop, ensure that data can be stored and analyzed across various databases. - Variety
Variety refers to the nature of the data. This can be structured, unstructured or semi-structured. Moreover, the data comes from many different types of sources. You can think of video, audio and click streams, but also data that comes from social media and emails. In theory, 80% of the data is unstructured. Thanks to the new Big Data technology we can bring together and analyze different types of data (e.g. social media messages, images and sensor data). - Velocity
First of all, this term refers to the speed with which new data is generated (e.g. Twitter messages that go viral). In addition, it refers to the analytical processing of data. In the case of fraud in particular, it is important to act quickly, as it may be too late to intercept within a few minutes. But also, with regard to signaling trends, GPS data and real-time available information about consumers and the market, the data must be able to be processed with great speed.
By working quickly with Big Data, you can benefit fast by optimizing the use of the data. - Veracity
This refers to the credibility of the data. With Big Data, different sources with different reliability are combined. The different types of data differ in terms of quality and accuracy and are therefore less controllable (e.g. typos, colloquial language, abbreviations). Thanks to new Big Data technologies, it is possible to use this “messy” data to achieve valuable results during the analysis process. - Value
This term refers to the great value that Big Data can deliver. It’s nice if you have access to all your data, but Big Data only really makes sense if you can get value from it. Analysis techniques such as predictive analytics and data mining help you to get value from Big Data. This allows you, for example, to make predictions and discover patterns and relationships that are present in Big Data.
Analytics of Big Data
Who would not want to predict the future? With enough data, the right technology and a dose of math, that comes within reach. This called business analytics, but there are many other terms that are used, such as data science, machine learning and, yes, Big Data. Although this mathematics has been around for a long time, it is still a relatively new field that until recently was only available to specialized companies with a lot of money.
Yet, we are acutely already using it without even knowing it; speech recognition on our phone, virus scanners on our PC and spam filters for email are based on concepts that fall into the domain of business analytics. The development of self-driving cars and all the steps to get there (adaptive cruise control, lane departure system, etc.) are only possible through machine learning.
How does analytics differ from business intelligence (BI) now? In fact, analytics is data-based decision support. BI shows what happened on the basis of historical data presented in predetermined reports. Where BI provides insight into the past, analytics focuses on the future. Analytics tells what can happen by making estimates with ‘what if’ scenarios and predicting risks and trends based on the daily changing data flow.
What can Big Data do for the maritime world?
Ships and shipping are full of systems, software and automation. The collection of data has become one of the core activities of the systems. A large transport vessel can produce more than two billion data per month with all systems on board, if that data is combined with external data, such as route information, the weather forecast and logistical movements. This creates a Big Data set that can lead to fuel savings, or more abstractly, more efficiency in business operations. An example of the possibilities for controlling activities and ship performance by Big Data is the system called Condition Based Monitoring (CBM). CBM can monitor all operational parameters of a ship in varying operating conditions. A CBM system offers the possibility to translate the parameters entered ashore to on-site warnings and settings of systems on board. This may, for example, be a recommendation for longer maintenance intervals that ultimately reduce operational costs.
To conclude, Big Data is about to have an even greater impact on the way we live, think and work. That is why it is important to be aware of the role it plays in our life and especially our organizations. Knowledge is not enough anymore to take us to the future. We need to learn to analyze the data we gathered with the knowledge we have to be able to move forward.
Just as with many other promising technologies and developments, Big Data will not solve all global problems. It is not magic. We still need people to perform the data analyzes and people to write the algorithms.
As we use more and more technology in our lives, the statistical software keeps getting better and the costs keep getting lower, Big Data will play an ever-greater role.
Are you ready?
