The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s
Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more. In the past, storing it would have been a problem – but cheaper storage on platforms like data lakes and Hadoop have eased the burden.
Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time.
Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions. At SAS, we consider two additional dimensions when it comes to big data:
Use Of Big Data
The unprecedented times and highly risky business environment calls for better risk management processes. Basically, a risk management plan is a critical investment for any business regardless of the sector. Being able to fore see a potential risk and mitigating it before it occurs is critical if the business is to remain profitable. Business consultants will advise that an enterprise risk management encompasses much more than ensuring your business has the right insurance.
So far, big data analytics has contributed greatly to the development of risk management solutions. The tools available allow the businesses to quantify and model risks that they face every day. Considering the increasing availability and diversity of statistics, big data analytics has a huge potential for enhancing the quality of risk management models. Therefore, a business can be able to achieve smarter risk mitigation strategies and make strategic decisions.
However, organizations need to be able to implement a structured evolutionary so as to accommodate the broad scope of big data. To achieve this, organizations collect the internal data first so as to gain clear insights that will benefit them. More important is the integrated process of analysis that a company uses. A proper big data analytics system helps ensure that areas of weaknesses or potential risks are identified.
How it helps your business?
The customer is the most important asset any business depends on. There is no single business that can claim success without first having to establish a solid customer base. However, even with a customer base, a business cannot afford to disregard the high competition it faces. If a business is slow to learn what customers are looking for, then it is very easy to begin offering poor quality products. In the end, loss of clientele will result, and this creates an adverse overall effect on business success. The use of big data allows businesses to observe various customer related patterns and trends. Observing customer behavior is important to trigger loyalty. Theoretically, the more data that a business collects the more patterns and trends the business can be able to identify. In the modern business world and the current technology age, a business can easily collect all the customer data it needs. This means that it is very easy to understand the modern day client. Basically, all that is necessary is having a big data analytics strategy to maximize the data at your disposal. With a proper customer data analytics mechanism in place, a business will have the capability to derive critical behavioral insights that it needs to act on so as to retain the customer base. Understanding the customer insights will allow your business to be able to deliver what the customers want from you. This is the most basic step to attain high customer retention.