Data is an information in an unorganized or raw form (such as numbers, symbols, or alphabets) that represents ideas, conditions, or objects. Data is unlimited and is present everywhere on earth. While data concept is normally associated with scientific research, data is gathered by many institutions and organizations, ranging from governments (e.g., unemployment rates, crime rates) and businesses (e.g., revenue, sales data, stock price, profits). Data could be classified as “Big Data” when it’s so complex or large that traditional processing applications are insufficient to deal with them.
Put simply, Big Data refers to a large volume of data that inundates businesses on a day-to-day basis. However, the amount of data is not important. What matters is what organizations do with that data. Big Data is analyzed for insights that lead to strategic business moves and better decision
Although the term “big data” is new, collecting and storing a large amount of data for eventual analysis is as old as 2000 years. In the early 2000s, the concept gained momentum when Doug Laney, industry analyst, articulated the current mainstream meaning of Big data as the 3 Vs:
- Volume — Organizations gather data from many sources, including social media, business transactions and information from machine-to-machine data. Previously, storing such data was a problem – but advanced technologies have eased the burden.
- Velocity— the speed in which data stream in is unprecedented. RFID tags, smart metering, and sensors are driving the need to handle the torrents of data in near-real time.
- Variety— Data comes in many types of formats: numeric data, text documents, video, audio, email, financial transactions and stock ticker data etc.
Analyzing data sets can result in finding new correlations to combat crime, prevent diseases, spot business trend and so on. People take data from many sources and analyze it to look for answers that enable new product development, time reduction, and cost reduction. When people combine high-powered analytics and Big data, they can undertake business-related tasks such as:
- Identifying fraudulent behavior that targets an organization.
- Determining causes of failures, defects and issues in near-real time.
- Recalculating whole risk portfolios in few minutes
- Creating coupons at the point of sale based on buying habits of the customer.
Data sets grow very fast because they are gathered using many information-sensing mobile devices, cameras, software logs, aerial, wireless sensor networks, radio-frequency identification (RFID) readers and microphones. Since the 1980s and the boom of the internet, the amount of data that is being generated and kept on a global level is inconceivable, and it keeps growing. The global technological per-capita capacity to keep information doubles every 40 months. Since 2012, 2.5 Exabyte of data is generated every day. A question for big enterprises is deciding who should be the owner of big data initiatives that affect the whole organization.
Relational database management systems and some data visualization software have difficulty in dealing with Big data. Therefore, this work needs “massively parallel software operating on tens, hundreds, or thousands of servers. Depending on the capabilities of the operators and their tools, what is counted as Big data varies, and expanding capabilities make it a moving target. Some organizations reconsider data management options when they face hundreds of gigabytes of data while others may take hundreds of terabytes before the size of data becomes a significant consideration.
At this point, I will like to believe you understand the meaning of Data and Big Data. Hence, we can say that the backbone of Fintech is innovation, but Fintech cannot fully attain this height or maintain the existing statuesque without proper data management. In fintech, a lot of complex financial information and user data are involved which need to be properly analysed and protected. With the help of the Big data strategies, Fintech businesses are able to harness and manage their business information properly and follow the compliance policies of data protection.
In the future, Big Data is expected to become an important basis of competition, underpinning innovation, and consumer surplus and productivity growth. So every fintech business MUST pay close attention to the trends of Big Data. We shall be publishing more information on Big Data, join us on Twitter and Facebook, or subscribe to our email updates .
