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What’s the Big Deal With Big Data?Should You Be Using It?
BIG DATA. Make your data ‘fast’ and you can achieve greater business intelligence without encountering certain challenges outlined here.
Despite seemingly bursting onto the scene and into the columns of benchmark industry publications, Big Data actually emerged gradually into the business world, having spent decades incubating underground in academic science circles.
Today Big Data is the tech-bro poster boy of data science gone mainstream—a business fix-all concept promising to unlock every potential profit vector for every possible industry.
But Big Data certainly isn’t the exponential fairytale and business panacea it’s made out to be in trendy business blogs. For most of the business world, size won’t matter.
Make your data ‘fast’ and you can achieve greater business intelligence without encountering certain challenges outlined here.
What Is Big Data?
Simply speaking, Big Data is a field within data analytics that deals with vast datasets arriving in increasing volumes with high velocity from a number of sources.
Consciously trying to implement big data analytics is the act of trying to interpret practical insight from the vast complexity and diversity of the datasets that big data produces—kind of like reading the matrix to find powerful patterns on which to base powerful decisions.
Three Problems With Big Data
Amid all the fanfare it’s easy to overlook one simple fact.
Big Data—as an alternative to traditional methods of business analytics—isn’t accessible or even desirable to every enterprise hoping to benefit from it. Yet many organisations without the sheer volume of data from which to extract Big Data’s utility get swept up in the hype, investing heavily in trying to develop Big Data capability—simply so they can say that they’re doing it.
Here are three overarching limitations of big data for probably most global enterprises (other problems are available).
Problem 1: Not every enterprise is Google or Facebook
Big Data requires phenomenal inputs to achieve even modest outputs.
If you’re a big-tech company churning out terabyte-per-second datasets, Big Data approaches to data analytics may bring impressive upticks in efficiency and profit.
If you’re a retail chain producing comparatively modest sums of daily transactions (even if they’re big and frequent transactions), then the Big Data approach won’t just be overreached by a significant margin—it will actually cost more than it produces by way of actionable insight.
Problem 2: Big Data means big cultural change
Organisations trying to leverage Big Data can find themselves realising after the fact that it’s not as simple as starting the engine and hitting the gas.
Big Data implementation isn’t just strategic implementation—it needs to be rolled out as part of a broader set of cultural changes to departments and business architecture for successful adoption.
The cultural change aspect alone is a significant investment that will render anything less than a significant reporting benefit simply won’t justify the move to Big Data.
Problem 3: Compliance
Compliance becomes a thorny issue when pivoting to Big Data analytics.
The vast nets your cast when trawling Big Data can sweep up untold amounts of uncategorised information, much of which may be confidential or sensitive.
This instantly brings new processes and costs into play, simply to ensure your Big Data efforts can be performed with a clean bill of compliance health.
What’s More Important than Big Data?
Big Data has impressive potential—but for most enterprises, even large global ones in retail, supply chain and finance, there are better alternatives.
Big data vs fast data
Like anything that happens at scale, the bigger you go, the clunkier things get, the more time, energy and cost is involved in moving things around and quickly changing direction.
Where big data might only tell you what’s going to happen tomorrow (if you can put enough data in the hopper), fast data can tell you what’s happening right now—and it can do it in real-time.
Forget ‘go big or go home’—go ‘fast’ instead
Fast data is achieved through smart data management practice.
The output (when implemented consistently) is a ‘data-to-decision’ mindset and a business intelligence strategy that reliably provides significant real-time insight from smaller, more cost-efficient volumes of data.
Instead of attempting to squeeze a few elusive gems of dream insight from giant data sets, making your access to operational and financial data faster brings significant benefit, with comparatively insignificant risk and cost compared with big data.
No need for compliance risk, no need for cultural change—fast data accessed through the right business intelligence tool and data management best practice can make a much bigger impact on your bottom than the big promises of Big Data.
If you’d like to make a big impact with your own fast data, ask us for a demo of FinWorx or RetailWorx (built on top of Power BI) and we’ll show you how they can become the BI pillars of your data-to-decisions journey.