Five key methods for big data optimisation
Big data is defined by the three big Vs: Volume, velocity and variety. The volume stands for the sheer size of the data, velocity refers to how fast data is generated and, finally, variety includes structured and unstructured data. The volume of big data is tremendous, the amount of data generated by US companies alone can occupy ten thousand libraries the size of the Library of Congress. The expansion of the Internet of things (IoT) and self-driving cars will see the amount of data collected grow significantly. With the enormous potential of big data, it is important for organisations to know the key tenants of big data optimisation.
Here are five key methods for big data optimisation
Big data is large, complex and prone to errors, if not standardised correctly. There are many ways big data can turn out to be inaccurate, if not formatted properly. Take, for example, a naming format – Micheal Dixon can also be M. Dixon or Mike Dixon. An inconsistent format leads to several problems, like data duplication and skewered analytics results. Therefore, a vital part of big data optimisation is setting a standard format so that petabytes of data have a consistent format and the ability to generate more accurate results.
It is not just enough to implement algorithms that analyse and finetune your big data. There are several algorithms used to optimise big data, such as diagonal bundle method, convergent parallel algorithms and limited memory bundle algorithm. You need to make sure that the algorithms are fine-tuned to fit your organisation’s goals and objectives. Data analytics algorithms are responsible for sifting through big data to achieve objectives and provide value.
Remove latency in processing
Latency in processing refers to the delay (measured in milliseconds) when retrieving data from the databases. Latency hurts data processing because it hurts the rate you get results. In an age where data analytics offers real-time insights having delays in processing is simply unacceptable. To significantly reduce the delay in processing, organisations should move away from conventional databases and towards the latest technology, like in-memory processing.
Identify and fix errors
A key part of big data optimisation is fixing broken data. You can have fine-tuned algorithms and install the best analytics platforms, but it does not mean anything if the data is not accurate. If the data is incorrect, it leads to inaccurate findings, which hurts your ROI. In such cases, a data analyst will have to go in and fix data to make sure everything is accurate. Big data can have plenty of errors, like duplicated entries, inconsistent formats, incomplete information and even inaccurate data. In such cases, data analysts have to use various tools, like data deduplication tools to identify and fix errors.
Eliminate unnecessary data
Not all data collected is relevant to your organisation’s objectives as bloated data bogs down algorithms and slows down the rate of processing. Hence, a vital part of big data optimisation is to eliminate unnecessary data. Once unnecessary information is eliminated it increases the rate of data processing and optimises big data.
Leverage the latest technology
Data analytics is constantly evolving and it is important to keep up with the latest technology. Recent technological developments, like AI and machine learning, make big data optimisation easier and improves the quality of work. For example, AI paves the way for a host of new technologies, like natural language processing that helps machines process human language and sentiment. Investing in the latest technology improves big data optimisation because it accelerates the process while reducing the chances of errors.
Bringing it all together
Big data optimisation is the key to accurate data analytics. If data is not properly optimised, it leads to several problems, like inaccurate findings and delays in processing. However, there are at least six different ways to optimise big data. These methods including standardising format, tuning algorithms, leveraging the latest technology, fixing data errors and removing any latency in processing. If data is optimised, it improves the rate of data processing and the accuracy of results.
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