Beginning of Big data to Fast data race
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.
Big data can be described by the following characteristics:
1) Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not.
2) Variety: The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
3) Velocity: In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
4) Variability: Inconsistency of the data set can hamper processes to handle and manage it.
5) Veracity: The quality of captured data can vary greatly, affecting accurate analysis.
Big Data uses specialized technologies to process vast amounts of information in bulk. But most of the focus on Big Data so far has been on situations where the data being managed is basically fixed—it’s already been collected and stored in a Big Data database.
This is where Fast Data comes in. Fast Data is a complimentary approach to Big Data for managing large quantities of “in-flight” data that helps organizations get a jump on those business-critical decisions. Fast Data is the continuous access and processing of events and data in real-time for the purposes of gaining instant awareness and instant action. Fast Data can leverage Big Data sources, but it also adds a real-time component of being able to take action on events and information before they even enter a Big Data system.
Fast data: The next step after big data:
The big data buzzword has thrown light on a number of issues in business intelligence. First is the growing volume of data: data growth is exponential, since all digital events (transactions, logs, M2M interactions, etc.) can be recorded as data points. Then there is the growing variety of data. Unstructured content, whether internal or external to the organization, is a rich source of information that requires new analysis techniques and of course, there is the question of value: none of the above is useful if it does not translate into a business outcome at reasonable cost. But an under-discussed aspect of big data, in my view, is how the velocity of data can create value.
Traditional BI is heavily criticized by both CEOs and business users for being unable provide the right information to the right person at the right moment. Business intelligence must be agile and swift if it is to participate in value creation. If the CIOs fail to provide that, users will keep buying personal or departmental tools, potentially creating a headache for data administrators and a number of reports within the enterprise with inconsistent data. The vertical applications of what we call “fast data” are many and varied. For example, in manufacturing, it can improve quality, reduce waste, make production and replenishment more effective by analyzing shop floor data in real time; in banks/ insurance or public sector providers, it can help prevent fraud by analyzing transactions and personal connections in real-time; In retail, it can improve and personalize customer experience, and to increase loyalty and spending by analyzing customer behavior and purchases and issuing special offerings while customer is still in the shop or on the website.
Fast Data is set to get a jump on the power of Big Data. Fast Data is fast becoming one of the top requirements for organizations trying to keep up with the information coming from various sources and make real-time decisions and serve the need of their customers. Ultimately, in order for Fast Data to be successful, a tightly integrated environment from device to datacenter is required and it is imperative that businesses start looking at Fast Data solutions if they want to keep up with their competitors and run their operations efficiently.