12 Jan

How small data became bigger than big data

    Anyone in the tech world knows that there’s currently a lot of fuss surrounding big data, I hate this word. Not only because I have written about it so much but because it isn’t the right terminology. “Big” Nah, more like “infinite” or “raw.”

    Despite the fact that it should be called something else, the importance of collecting it, analyzing it, and the application of your newly acquired big data knowledge is all conceived as tools. We do this to make smarter strategic decisions, reduce costs, target the right audiences, recalculate risk portfolios, optimize offerings, and overall run your business as efficiently as possible. With projected sales of data analytics tools hitting $187 billion in 2019, it’s apparent that this method of optimizing your business possibilities isn’t going away.

    The breakdown of big data

    To get to the bottom of all this big data hype, we should probably uncover the root of what it actually is. Big data is essentially the massive amount of structured and unstructured information that overwhelms a business daily, whether it’s from business transactions, machine-to-machine data, or social network interactions. The idea is that the available data is so intricate and vast that standard data-analyzing technologies aren’t going to be adequate enough to handle them. Because it’s such a vague term replete with possibilities, you can boil it down to a simple concept: Big data is data that is drawn from various sources and imperative to making decisions that have a positive impact on a business.

    Does big = useful?

    Not really.

    While it’s great that in the last couple years big data has become the central focal point of businesses small and large, just being aware of it and storing it in the massive quantities it comes in doesn’t cut it for most businesses. The whole point is to be able to properly analyze the data and draw both practical and guiding conclusions from it in the hopes of bettering your business practices and capitalizing on trends, improving your outcomes for the future based on what you’ve learned. But for many businesses, there is a disconnect over which data is being analyzed, which data they think should be analyzed, and how they are conducting these analyses; many businesses lack the skills, tools or knowledge base to make use of big data properly, and suffer as a result. Recently, however, the tides have turned….

    Big shift: Small data

    In contrast to big data, small data is a data set of very specific attributes that can be created by analyzing larger sets of data. It is often informative enough to find solutions to problems and achieve actionable results. In other words, small data brings people timely, meaningful insights that are organized in an accessible and understandable way, without requiring the use of expensive technological systems necessary to tackle big data. This means that instead of jumping through the tangled hoops big data consists of, businesses can utilize smaller, more tangible data sets to glean the insights that they need, by isolating chunks of big data to make analyses in their everyday jobs. And they are.

    How is small data taking over?

    The answer is through log analysis. Simply put, a log is the documentation of events related to a particular system (for example, a transaction log of all changes that transpired within a database) that’s automatically produced.  From log files, businesses can extrapolate information on the sales, marketing, finance, security and many other aspects of their day-to-day processes. These logs support business operations through identifying trends, troubleshooting, detecting system errors and the like. In other words, by utilizing small data, businesses can arrive at imperative conclusions without having to navigate through the overwhelming amount of information big data bombards them with on a daily basis. This isn’t to knock big data; small data can only be obtained by converting big data into manageable sets of understandable, applicable information. The two have to work in tandem in order for small data to be useful.

    Making data work for you

    Like with big data, businesses need a means of sorting through the information the logs provide them, but preferably without the complex data-analyzing technologies that a lot of businesses aren’t able to afford. Luckily, there are now emerging innovative systems seeking to convert large sets of data into approachable and manageable patterns that can be analyzed and organized. An example of such a company is Coralogix, a service that turns millions of log records into meaningful and useful patterns that can help you detect and solve your problems quickly, rather than just simply indexing and collecting the information. They identify new errors like production problems and save you hours of perusing the information you’ve compiled, by grouping your data for you into its original patterns so that it’s scannable within seconds.

    It’s innovations like this that have caused a giant shift in where we put our focus as a society of data scanning. Big data had its time, but small data is now our number one tool in competent strategizing and boosting our businesses.

    Small data has taken over in helping us make sense of the digital world we exist in. It has broadened our horizons in terms of how quickly we can break down huge flows of information and isolate the parts that are useful to us and our business. While big corporations may still be able to make sense of their big data with lots of time, energy and expensive machinery, the majority of companies who haven’t had the proper resources finally have found their solution. For most, if not all, small data is the future.

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