Simply said, the optimal use of Big Data is inside systems and processes that decrease data footprints significantly. What are the benefits of reducing your data footprint?
Simply said, the optimal use of Big Data is inside systems and processes that decrease data footprints significantly. What are the benefits of reducing your data footprint? More data does not always equal better data, according to years of experience in the field of information management: the more data created, transported, and handled, the greater the development and administration overheads.
In addition to the foregoing, the more data we collect, store, copy, and alter, the greater our data and carbon footprints will grow.
How do we use Big Data to reduce Big Data?
By profiling data, we can use Big Data to discover the data that will be beneficial. We may do this by detecting excess, redundant, and unnecessary data. When we do this, we're categorizing, cataloging, and classifying large amounts of data.
What can we do with the profile data?
We may examine, audit, and review the creation, storage, and transfer of the profiled data using the information obtained via profiling. Using a machine learning method, we can also develop discriminating rules to identify in the future what generated data is useful and what data may be discarded.
Why do all of this?
Big Data may be a serious obstacle, and the best approach to cope with such challenges is to have a well-thought-out plan. We may strive to change the problem into a more manageable issue - or, if more possible, we can eradicate the problem totally - by addressing the challenges presented by data upstream.
How would this work in practice?
We can control Big Data by lowering the quantity of data created, which we can accomplish by eliminating unneeded data generating channels and preventing the storage of any unwanted data. Signal producers (apps and devices) can be excluded from recording protocols, or the logging procedures can be changed to ensure that only useable data is captured and saved.
We may also filter data dimensionally, using time, proximity, and affinity to associate and abstract discrete events, phases, values, and aspects.
What are the benefits of this?
Making the footprint of gathered data smaller minimizes expenses, increases focus, and decreases complexity. The smaller the footprint will be the sooner data is properly filtered.
A reduced data footprint speeds up the processing of data that is relevant to the company, and so has the potential to increase productivity.
Taming Big Data
Taming Big Data
We must only create data that is essential for the business, has value, and has an organizational function - that is, data that is business-oriented, technical, or management-oriented.
Early, regularly, and brutally filtering of data is required. Only when data is useful to the broader business and its objectives should it be created, retained, and communicated.
Taming Big Data is critical for the success of your company, its management, and its entire technological skills. The easiest method to deal with a data tsunami is to make sure there isn't one in the first place - it's as simple as that - this is known as transferring the problem upstream.