Guide to big data analytics
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Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Guide to big data analytics
- 1. Guide to Big Data Analytics BY GAHYA PANDIAN
- 3. What is Big Data Data analysis is nothing new. Even before computers were used, information gained in the course of business or other activities was reviewed with the aim of making those processes more efficient and more profitable. These were, of course, comparatively small-scale undertakings given the limitations posed by resources and manpower; analysis had to be manual and was slow by modern standards, but it was still worthwhile. Opinion polling, for example, has been carried out since early in the 19th century, almost 200 years ago. The first national survey took place in 1916 and involved the publication Literary Digest sending out millions of postcards and counting the returns. As a result, they correctly predicted Woodrow Wilson’s election as president. Since then, volumes of data have grown exponentially. The advent of the internet and faster computing has meant that huge quantities of information can now be harvested and used to optimise business processes. The problem is that conventional methods were simply not suited to crunching through all the numbers and making sense of them. The amount of information is phenomenal, and within that information lies insights that can be extremely beneficial. Once patterns are identified, they can be used to adjust business practices, create targeted campaigns and discard ones that are not effective. However, as well as large amounts of storage, it takes specialised software to be able to make sense of all this data in a useful way.
- 4. Big Data Big Data’ is the emerging discipline of capturing, storing, processing, analysing and visualising these huge quantities of information. The data sets may start at a few terabytes and run to many petabytes – far more than traditional data analysis packages can handle. In 2012 Gartner defined it as, ‘high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.’ This ‘3V’ classification has been built on since (particularly with the addition of veracity), such that Big Data is often described in terms of the following characteristics:
- 5. Big Data Volume. Terabytes or petabytes of data are analysed. An estimated 2.5 quintillion bytes of data (2.5 trillion gigabytes) are created every day, an amount which will only rise in the future. However, the size of the dataset is not the only variable that characterises Big Data. Variety. The dataset may contain many different forms of data – not simply a large amount of the same type. The profusion of different kinds of mobile device and the variety of content consumed on them on a wide range of platforms, for example, means that companies can harvest data from an enormous array of sources, each telling them a different part of the same picture. Velocity. Data may change on a constant basis. For example, modern cars may have 100 or so different sensors that continually monitor different aspects of performance. Markets change on a moment-to-moment scale. Data is highly fluid, and snapshots are not always enough. Veracity. The data acquired may not all be accurate, or much of it may be uncertain or provisional in nature. Data quality is unreliable, especially when there is so much of it. Any system of analysis must take this into account.
- 6. Big Data In addition to the 4V characteristics, there are also two others to deal with: Variability. Data capture and volume may be inconsistent, not just inaccurate, so varying quantities and qualities of data will be acquired at different times. Together, these factors mean that managing the data can be an extremely complex process, since there are many data sources with differing types and formats of data, but these need to be correlated and made sense of if they are to be useful.
- 7. Conclusion Big data isn’t just an emerging phenomenon. It’s already here and being used by major companies to drive their business forwards. Traditional analytics packages simply aren’t capable of dealing with the quantity, variety and changeability of data that can now be harvested from diverse sources – machine sensors, text documents, structured and unstructured data, social media and more. When these are combined and analysed as a whole, new patterns emerge. The right big data package will allow enterprises to track these trends in real time, spotting them as they occur and enabling businesses to leverage the insights provided. However, not all big data platforms and software are alike. As ever, which you decide on will depend on a number of factors. These include not just the nature of the data you are working with, but organisational budgets, infrastructure and the skillset of your team, amongst other things. Some solutions are designed to be used off-the-peg, providing powerful visualisations and connecting easily to your data stores. Others are intended to be more flexible but should only be used by those with coding expertise. You should also think to the future, and the long-term implications of being tied to your platform of choice – particularly in terms of open-source vs proprietary software.
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