Before jumping to BigData let’s understand what is data first. The computer performs any operation on symbols, quantities, or characters and store it in the form of electrical signals. The signals are recorded on optical, magnetic, or mechanical recording media, called data.
Bigdata is huge volume of structured and unstructured data. Bigdata is collection of huge data volume which is still growing significantly. The data volume is so huge that storing and managing it from traditional data management tools is being difficult.
With rapid technological advancements, the world is getting smarter and more connected which is generating huge amount of data and information. The data contains useful insights needs to be managed significantly.
Moreover, data comes in various forms such as social media posts, website posts, and many other unstructured forms of data which made the data management difficult. This is anticipated to boost the growth of advanced analytics programs. The primary goal of big data analytics is to generate insights which might create tangible business profits.
Types of BigData
Bigdata is segmented into three categories Structured, semi-structured, and unstructured.
Structured data can be defined as any set data which can be managed, stored, and processed in a fixed format. The computer science has developed several techniques to work with such kind of data. However, it has been anticipated that we might experience difficulties when size of such data grows to the maximum extent.
Any data with undefined form or structure is termed as unstructured data. As the size is huge in unstructured data, it poses several challenges in terms of processing and delivering insights through it.
The most common example of unstructured data is heterogeneous data source which consists the combination of videos, images, and simple text files. Now a days, organizations have huge amount of data but it’s in raw format as they don’t know how to derive value from it.
Semi-structured data might consist both structured and unstructured forms of data. Example of semi-structured data is a table definition in relational database management system.
Three Vs of BigData
Variety: Variety refers to different types of data available in the system. In tradition data system, data and information were likely to fit and structured by using relational database management system. With significant uptake in bigdata terminology, now data comes in new unstructured and semi structured data types such as video, audio, or simple text files. It requires additional processing to support metadata by deriving insights from it.
Velocity: In the data system, data is received and operation is performed at a specific rate which is termed as velocity. These days, with rising internet penetration, real time information is required to handle smart products which requires real-time evaluation and action.
Volume: Bigdata helps in processing high volume of low-density unstructured data. The data can be of unknown value such as click streams on a sensor-enabled equipment, mobile app, or a web page, or Twitter data feeds. For some organizations the volume might be hundreds of petabytes or for some it can be tens of terabytes of data.
Why BigData is Important?
Bigdata enables the end-user to address different business activities ranging from improving customer experience to analytics which helps in enhancing operational efficiency. Some of the use cases are state below:
Drive Innovation: Bigdata helps in studying inter-dependencies among humans, process, entities, and institutions and then finding new ways to utilize those insights and turn it into innovations. This can also help in improving decisions related to planning and financial considerations. Implement dynamic pricing by examining trends related to consumer insights. The possibilities are limitless.
Product Development: Several companies such as P&G and Netflix use big data to estimate consumer demand. They classify key attributes of current as well as pass product and services, relationship between those attributes, and commercial success of the offering and then create predictive models for new offerings.
Additionally, Procter & Gamble use data and analytics from early store rollouts, focus groups, test markets, and social media in order to create a roadmap the product development and marketing.
Operational Efficiency: Bigdata has shown major impact on enhancing operational efficient, though it may not always make the news. Big data helps in accessing and analyzing customer returns, feedback, and other factors in anticipating future demands which results in minimum outage. Also, it can be used in improving decision making in lined with current market scenario.
Predictive Maintenance: Factors predicting mechanical failures may be deeply suppressed in structures data, such as equipment model, manufacturing year as well as in unstructured form which covers huge volume of engine temperature, log entries, error messages, and sensor data. Organizations are able to deploy maintenance cost effectively by analyzing these potential issues. Also, it increases equipment and parts up-time.
Machine Learning: Machine Learning is gaining momentum in almost every sector. And bigdata is one of the reasons behind it. It is now possible to teach machines instead of programming them. Bigdata makes it possible to implement machines learning models.
Read more about Machine Learning…
Fraud and Compliance: Security landscape is now not restricted to just a few hackers, you need to deal with entire expert teams, and this is constantly evolving. Bigdata enables organizations to identify data patterns which help in fastening regulatory reporting by indicating fraud and aggregate large volumes of information.
Customer Experience: Enhancing customer experience has become feasible than never before. Bigdata helps enterprises with huge data volumes gathered from call logs, web visits, and social media in order to improve user experience and enhance the value delivered. Also, it helps in handling issues proactively, delivering personalized offers, and minimizing customer churn.
In spite of significant advantages, organizations are facing some challenges with bigdata implementation.
Although, different innovations are made and several technologies are introduced for data storage, data volume is increasing rapidly. It is doubling in size in couple of years. Companies are still struggling in finding effective ways to store and process it.
Storing data is not only enough, it needs to be utilized in valuable form, depending upon the curation. Data cleansing which means presenting the data in a relevant form which ass some meaningful insights to your client requires lot of work. Data scientists spend almost 50-80% of time in cleansing and preparing it before the actual operation starts.
Although, several R&D activities are taking place. Initially, Apache Hadoop was the most popular technology to handle bigdata analytics. In 2014, Apache spark was introduced. At present, these two frameworks are used in combination for bigdata analytics and considered as the best approach.
What will be the future scenario for big data analytics? Let me know in the comment box 😊