Cracking ‘Big Data’ and the New Game Changing Techniques

In the age of digital everything, the volume of data collected is growing exponentially every year. From customer data to business transactions to stock purchases and sales and legal citations, oversized databases are a problem in many industries. What makes it more complicated is that ‘big data’ can often provide incredibly valuable information when it is analyzed as a whole.

Breaking larger data sets into smaller subsets is certainly an option, but it does not always provide the same benefit, especially when trying to make the best possible decisions for your business.

Limitations of Data Sets

Today, there are limitations on data ranging between petabytes and zettabytes depending on the type of data and the technology being used to analyze that data. In many situations, these limits are surpassed quickly due to the raw volume of data collected by scientists, analysts and economists in different fields which make it hard to complete complex analysis without finding creative ways to display and analyze that data.

Fortunately, technology is advancing nearly as fast as our capacity to fill it up and there are new software methods to analyze big data sets – parallel software installed on dozens or even hundreds (sometimes thousands) of servers, processing immense numbers of data at once. Data management is a simple enough process when implemented properly as part of your business’s expansion plan – it is really a matter of knowing when you need it without necessarily making comparisons to other industries.

Processing and Using Big Data

So, if the standard methods of data analysis do not always work for your business, there are other technologies growing in popularity to do so. Simple analysis can be done with:

A/B Testing: Running data through similar conditions and testing for variation can be an immensely effective way to measure differences between those data points.

Association Rule Learning: This is incredibly popular and works well in large data sets. It takes correlating pieces of data and shows likely results. A common example is {onions, potatoes} => {steak}. So, those who often bought both onions and potatoes were more likely to buy steak. Such correlations can be found in large data sets but also must be coaxed out by an experienced analyst.

Cluster Analysis: Cluster analysis involves grouping data points into “like” groups and then analyzing those groups. In many cases, breaking down large data sets into smaller groups can be an effective way to analyze them.

Natural Language Processing: Modern machine learning algorithms are incredibly powerful and it allows computers to extract more information than ever before, such as is the case with IBM’s most recent super computer – Watson.

There is a lot more including regression, predictive modeling, signal processing, visualization, simulation and much more. The idea behind each of these is simple – generate a new, creative way to display and process data in a massive set that does not require a billion dollar super computer. It takes a bit of creativity and some advanced technical capacity, but it is feasible and increasingly effective for those super-sized data sets.

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