How to Tame Big Data Analytics
Put simply, the best use of Big Data is within systems and methods that will greatly reduce a data footprint. Why reduce the data footprint? Years of experience in the area of information management say that more data does not necessarily result in better data - the more data generated, moved, and managed, the higher the overheads in development and administration.
As well as the above, the more data that we generate, store, replicate, and transform, the larger the data and carbon footprints are going to become.
How do we use Big Data to reduce Big Data?
We can use Big Data to identify the data that is going to be useful, by the way of profiling. The way we can do this is by identifying the surplus, redundant and irrelevant data.
When we do this, we are using it to categorize, catalog, and classify high-volume sources of data.
What can we do with the profile data?
We can use the resulting information from the profiling to analyze, audit, and review the generation, storage, and transmission of the profiled data. We can also generate discrimination rules, to determine in the future what generated data is relevant and what data can be ignored - we can do this taking a machine learning approach.
Why do all of this?
Big Data can represent a significant challenge and the best way of dealing with significant challenges is to create a coherent and reasonable strategy. By addressing the issues presented by data upstream, we can attempt to turn the problem into a more manageable issue - or, if more practical, we can opt to remove the problem entirely.
How would this work in practice?
We can tame Big Data by reducing the amount of data being generated and we can do this by removing unnecessary channels of data generation and stopping storing any superfluous data. Signal generators (apps and devices) can be removed from logging protocols, or by changing the logging processes so that only usable data is logged and stored.
We can also filter out data dimensionally - this is done by association and abstraction of discrete events, phases, values, and facets; by time, proximity and affinity.
What are the benefits of this?
Making collected data smaller reduces the footprint and this means reduced costs, greater focus, and less complexity. The sooner data is filtered correctly, the smaller the footprint is going to be.
Having a smaller data footprint speeds up the processing of the data that is useful to the business, and so has potentially greater value.
Taming Big Data
It is imperative that we only generate data that is required for the business, has value, and has an organizational purpose - and that means business-oriented, technical, or management focussed.
Data should be filtered early, often, and ruthlessly. Data should only be generated, stored, and transmitted only when it is valuable to the overall business and its goals.
Taming Big Data is important for your organization, the management of the organization, and its overall technical capabilities. The best way to deal with the tsunami of data is to make sure there is no tsunami - it is as straightforward as that - this is what is referred to as shifting the issue upstream.