Big Data is a collection of data which is huge in volume and is even growing exponentially with time. Such data is so enormous and complex that none of the conventional data management tool can easily store and process it proficiently with the help of data scientists.
Many companies across different industrial sectors are benefiting from Big Data and are thus promoting data-driven decision making. Big Data is no longer limited to mere tech industry and its prevalence has been extended to healthcare, education, finance, retail, manufacturing and supply chain management and logistics, etc. Be it micro or large, Big Data is being used by almost every organisation nowadays.
According to Gartner, “Big Data are high volume, high velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”
Core Features of Big Data
There are three core features of Big Data that describe its characteristics – high volume, high velocity, and high variety. The volume and complexity of Big Data is huge and is continuously multiplying with the rapid development of business tech (Artificial Intelligence, Machine Learning and Internet of Things), mobile data traffic, cloud computing traffic, etc.
Some examples of how rapidly Big Data generates:
- Around One Terabyte of new trade data per day is generated by New York Stock Exchange.
- 500+ Terabytes of new data is generated into the databases of social media site Facebook, every day.
- A Jet engine generates 10+terabytes of data in 30 minutes of flight time.
Characteristics Of Big Data
As the name itself suggests the volume of Big Data is gigantic. Whether a data could be called Big Data or not depends upon the size of such data.
Variety means different sources and the nature of data, this may include both structured and non structured data. In earlier times only spreadsheets and databases were considered to be a source of data. But with the evolution of time, emails, photos, videos, monitoring devices, PDFs, audios, etc. have added to this list and are even much bulkier in size. As mostly these data are unstructured, there arise many problems in storing, mining and analysing it.
This refers to the rapid generation of Big Data. The rapid generation and processing and in order to meet the demands determines real potential in the data. It deals with the speed in which the data continuously and massively flows in the sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc.
It means the variability of the data and the inconsistency present in the data, which makes it challenging to process and manage it.
How businesses can benefit from Big Data?
1. Make a Big Data strategy
At an elevated level, a major Big Data strategy is designed to assist you with regulating and improving the manner in which you obtain, store, manage, share and use data inside and outside of your organization.
A perfect Big Data strategy might prove to be a success for the business. And so it is required that the big data should be treated as a valuable asset of the organization rather than just an application.
2. Identify the sources of Big Data
Streaming data is the data that generates from the Internet of Things (IoT) and other connected devices that flow into IT systems from wearables, smart cars, medical devices, industrial equipment and more. It may be decided whether to keep this data or discard it. Read this article to know data cleaning techniques for IoT-generated data.
Social media and other websites data comes from social media sites such as Facebook, YouTube, Instagram, etc. and other websites. This includes mammoth number of images, videos, voice, text and sound – useful for marketing, sales and support functions. This data is often in unstructured or semi-structured forms, so it brings a challenge along for its consumption and analysis.
Publicly available data comes from massive amounts of open data sources like the Government websites and portals.
Other big data may stem from data lakes, cloud data sources, suppliers and customers.
3. Obtain, store, manage and share big data
Technologically advanced computing devices facilitates the competencies required to quickly obtain such huge amount of data. Besides safe access, organizations also need to integrate the data as well as provide and ensure the quality.
The data is then governed and stored with the help of such modern computing systems and prepared for analysis. The data is sometimes stored in conventional data warehouses, but nowadays we have low cost and effective cloud solutions options too for storage.
4. Analyze big data
There are many high-performance technologies like grid computing or in-memory analytics out there which the organization may opt for the analysis of their data. Companies may classify the data as relevant and irrelevant before analysis.
5. Make intelligent, data-driven decisions
When the data is managed and trusted well, it reaps into trusted analytics and trusted decisions. Organizations who function in a data driven way, make decisions accordingly on the basis of evidence and thus they perform way better and are more predictable and more profitable.
Salman Zafar is a serial entrepreneur, digital marketer, writer and publisher. He is the Founder of Techie Loops