From Big Data to Fast Data: How to be Fast Data ready
Big Data was yesterday’s buzzword. Many businesses now are looking for data agility as a primary factor for business success – which is why sustainability and adaptability have become more relevant. The benefits of big data can be reaped only when the data is processed at high speeds. This is where Fast Data comes to the rescue. Based on a finding by Cisco, the number of connected sensors, devices, and objects is set to reach 50 billion by 2020. This kind of proliferation of sensory and connected data is preparing business leaders to look for improved ways to streamline business operations, thereby leading to efficiency in business performance.
What is Fast Data?
With copious amounts of unstructured data growing as a result of increased storage capabilities, the need to stream real-time information from numerous end points becomes critical in making quick decisions. Fast data allows for real-time recommendations as opposed to big data that provided seasonal insights. Consider the world of online financial transactions. Typically characterized by high volume and high sensitive data that is vulnerable to data thefts, it is important to adopt sophisticated approaches to process such data sets. It is in this case that responsiveness of fast data comes in handy. The power to compute data in real-time through local devices instead of a huge cloud, fast data allows for better leveraging of data within shorter time frames.
The pre-requisites for rapid processing of data include the following: a system that is equipped to comprehend data as quickly as it arrives and a data warehouse that can make sense of the data received to make in-the-moment decisions. This process constitutes what is known as real-time decision making and stresses the importance of time-to-insight.
Such rapid processing of data not just offers businesses a competitive advantage, but also precludes the need for huge infrastructural capabilities, thereby saving huge costs.
Let us look at a few use cases of how fast data can be potentially useful.
Financial applications and payment systems: Since financial transactions tend to be batch-oriented, end of day processing is a must, with reports mandated to be done on an hourly basis as well. With the need for faster turnaround, fast data enables real-time processing that further allows for faster feedback and quicker analytics. For payment systems, decisions must be made in split seconds. Therefore, it is important for real time processing of data. It’s key to keep in mind that fast data is streaming into the organization at wire speed, and just pushing data directly into a long-term analytics or storage engine simply delays acting on the data in real-time. Determining in real time whether your portfolio is losing money, or if there is fraud in your system means that you can foretell disasters and prevent them before any damage is done. For a more accurate view of the market along with more accurate actions for maximized profit, it is important to correlate multiple sources from the market in real time.
Healthcare: While big data is particularly useful for historical analysis of large data sets that constitute medical records, fast data provides doctors with comprehensive insights enabling specific care recommendations. Fast data enables acquiring in-the-moment patient data based on predictive models of analytics.
Retail: By taking a fast data approach to business, retail marketers seek to leverage the power of real-time analytics. Typically, a consumer’s buying decision does not follow a straightforward logic. Therefore, making sense of data on consumer behavior across multiple levels of the buying cycle requires both high-speed processing abilities as well as good integration capabilities.
Let us also explore how organizations are rising to the challenge of being fast data ready.
Improved IT infrastructure capabilities
In order to be fast data ready, organizations have to ensure that their IT infrastructure is scalable, so much so that all data is shared and accessed across all departments of the organization. With increasing volume of information, IT infrastructure also needs to scale up to a reasonable level in order for exploiting opportunities to expand insights by combining data sets. Bigger and better data give companies more panoramic and granular views of their business environments. Considering that legacy IT structures pose a challenge of hindering new types of data sourcing, storage, and analysis, it is important to re-examine infrastructure capabilities.
Greater analytical skills and literacy
By ensuring that employees understand the significance of data analytics, organizations must ensure that it is part of their larger goals. Organizations could channel their efforts in the direction of building models that deliver optimum results. If an organization is built on a model that does not offer a bird’s eye view of the business at any given moment, this is likely the result of poor data management. Without the necessary insight for planning and taking action, a business will experience obstructions in its decision-making process, business performance, and the ability to predict and forecast.
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