How to Scale Your Big Data Project Effectively

Data and big data have become ubiquitous parts of any business these days as organizations look to uncover the next valuable insight which could make or break a business. An estimated 2.5 quintillion bytes of data was created every day in late 2016, and that number will be boosted even further by the imminent rise of the Internet of Things.

But as businesses gather ever more data, the costs of trying to hold that data and glean useful insights become harder as the data becomes too large for our spreadsheets and brains. Managers find themselves with reams of data which they cannot make the least sense of precisely because they have underestimated the challenges of scaling big data.

Big data is supposed to transform how businesses operate, which means that traditional methods of storing and analyzing data will no longer suffice. New practices have to be implemented to handle this increasing data inflow.

Businesses and analysts love to talk about the revolutionary idea of “big data,” but businesses have been using data to draw conclusions since the birth of civilization. So what precisely makes big data so special?

If your answer is the sheer volume of data collected, you’re wrong. Simply gathering huge chunks of data and dumping it in some repository is meaningless. In fact, it is harmful for your business due to the costs of storing that additional, useless data as well as the legal and financial risks of a data breach or holding big data.

The key advantage of big data is not the volume of data, but the analysis gleamed from it. Because of this, I prefer the term “big data” and prefer “smart data.” The idea should be to figure out which data to collect and why it is necessary as opposed to thinking “Well, this data set could somehow be useful at some point in the future.”

Consequently, the first step in handling growing amounts of data is to ask yourself if all of this data is necessary. Data should be gathered so it can answer a question like customer preferences or the best shopping hours, not collected just because.

Even after eliminating wasteful and unnecessary data, your business will still likely have more data than can be processed by a single individual or can fit on an Excel spreadsheet. This means that any data must be broken down even further into more manageable subsets.