Beyond Volume, Variety and Velocity is the Issue of Big Data Veracity
Big data incorporates all the varieties of data, including structured data and unstructured data from e-mails, social media, text streams, and so on. This kind of data management requires companies to leverage both their structured and unstructured data.Big data enables organizations to store, manage, and manipulate vast amounts of disparate data at the right speed and at the right time. To gain the right insights, big data is typically broken down by three characteristics:
- Volume:How much data
- Velocity:How fast data is processed
- Variety:The various types of data
Big data implies enormous volumes of data. It used to be that employees created data. Now that data is generated by machines, networks and human interaction on systems like social media the volume of data to be analyzed is massive. The tons of information in any of the billions of network poured in a single day can make easily understand the ever growing volume of data. Further research on Big Data is focused on making this volume available for various kinds of analysis in enterprises or business organizations. For instance, billions transaction data in a retail chain can be subject to analyze buying trend or consumer’s buying frequency for select products or for example trillions of fuel bills can be subject to analysis for next vehicle fuel policy.
Variety refers to the many sources and types of data both structured and unstructured. We used to store data from sources like spreadsheets and databases. Now data comes in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. This variety of unstructured data creates problems for storage, mining and analyzing data. The widest possible variety of data types is one aspect corresponding to Big Data analysis that is going to pave the way for numerous benefits for big to small, all sorts of organizations. Big Data comprises any type of data, both structured and non-structured. It can be audio visual, graphic representation, spreadsheets and log files, 3D images to simple text to click links or simply anything. When these multifarious types of data are analyzed together they may provide great range of insights for particular researches. For instance tons and tons of text messages over a football match can be analyzed in contradiction to measurably small number of actual spectators which may indicate a necessity for changing marketing and publicity tactics for the event managers and organizers of the match.
Big Data Velocity deals with the pace at which data flows in from sources like business processes, machines, networks and human interaction with things like social media sites, mobile devices, etc. The flow of data is massive and continuous. This real-time data can help researchers and businesses make valuable decisions that provide strategic competitive advantages and ROI if you are able to handle the velocity. Big data has much more bigger implications in time sensitive business processes than others. It is always a hurried process to analyze to scrutinize maximum volume of data for a potential business objective like catching a fraud in transactions or locating the exact reason of why clients of a particular business process are not coming back. Only faster scrutinizing capability that can handle large volume of data in real time can translate into business benefits. Faster processing of time sensitive data is to give you an edge in fault finding or finding the hidden loop in the process, that is exactly one of the demands of Big Data that is increasingly becoming crucial.
Big Data Veracity refers to the biases, noise and abnormality in data. Is the data that is being stored, and mined meaningful to the problem being analyzed. Inderpal feel veracity in data analysis is the biggest challenge when compares to things like volume and velocity. In scoping out your big data strategy you need to have your team and partners work to help keep your data clean and processes to keep ‘dirty data’ from accumulating in your systems.Accuracy or trustworthiness of information is one aspect that challenges the use of data in business analysis or trend analytics. While many business managers and top decision makers are still skeptics about the accuracy and corresponding outcome of business analysis based on various sources of data, when this body of data grows enormously bigger to contain various contradictory trends and aspects it can as well be a good basis for determining the accuracy. As the volume grows bigger in Big Data analysis, the efforts motivated by partial observation becomes futile and thus exceptionally Big Data reserves when handled properly can render more accurate observations.
,Just in case you ever needed an infographic for the 4 V’s of big data, IBM has one for you,