How to Scale Your Big Data Project Effectively
Big data carries plenty of downsides, whether it is in the form of paralysis as your company cannot process that much data or the increased risk of a security breach.
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.
The Problem with “Big Data”
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.
Break Down Data
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.
With the power of statistics and analytics, your business will not lose much insight by using a data signal booster and breaking things up into smaller subsets. For example, if you have a million or 10 million names or pieces of data, a random sample size of a few thousand or ten thousands can approximate trends over that larger populace. This is the principle behind polling, which seeks to analyze the political viewpoints of 300 million American by randomly sampling only a few thousand. And by creating sample after sample from the larger data pool, your company can review the results to come up with a final result and also detect errors which exist in that pool.
Looking at multiple, smaller random subsets of information instead of trying to look over all the data at once is a much more efficient approach to discover trends, glean useful analysis, and improve the overall data.
Infrastructure and Communication
A major challenge with using smart data is making sure that the business leaders and tech experts are on the same page regarding the company’s big data strategy. The importance of communication matters most when it comes to building the best infrastructure to manage more data.
The creation of so much data has practically eliminated the traditional approach where a company could keep all relevant data on its own centralized servers. Cloud-based servers or virtualization software like Hadoop are a strong answer for how to store data, but data experts have to be able to show business leaders who may be ignorant about all the implications behind big data of the necessity of updating the infrastructure.
There is more to this than infrastructure as well. Every business leader knows the importance of big data, but they may not understand that it means much more than just collecting a lot of data. By showing them how the infrastructure needs to be updated and the resulting benefits, leadership can be more appreciative of the risks and benefits of big data.
Rewards and Risks
Big data carries plenty of downsides, whether it is in the form of paralysis as your company cannot process that much data or the increased risk of a security breach. That makes it all the more important for your company to understand how to scale up and accommodate this increased data, whether it is by improving infrastructure or removing unnecessary data.
But by slicing up larger datasets into something more digestible, data experts can glean useful information and quickly meet the demands of their customers and leaders. This requires strong communication and new technology.
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