Over the next five years, global data creation is projected to grow to more than 180 zettabytes – and there’s no telling how quickly this acceleration will compound in years to come. With so much data at our fingertips, the potential for keen analysis and business insight and utility is vast.
Accordingly, the challenge for enterprises lies not in having enough data for analysis, but in their ability to process it and get significant insights from it . One of the biggest and ultimately preventable losses in industry today is the sheer number of insights that are ultimately missed due to enterprises’ inability to fully process such massive troves of data cost-effectively and efficiently.
Such a surplus of data would be incredibly promising if standard enterprises were equipped with strong enough tech stacks to fully process their piece of the data-pie. The problem is that the pie is too big, and too few organizations have a large enough knife or plate to cut themselves a slice. Organizations are constantly trying to prioritize hundreds of demands at the same time, but the issue of underutilized data is one that cannot be ignored– if they cannot adequately mine their data for meaningful insights, they will quite simply not survive in this competitive market.
Here’s what is holding these companies back from maintaining an optimized relationship with the data they possess, and what they can do about it.
The state of data
Data analysis is unique in its ability to provide answers to critical business questions. By tapping into the subtlest of trends across many months of data at a time, automated data analysis can unveil insights and predictions that would likely be missed by the naked eye.
But often there just isn’t enough time to fully dig into these trends – it calls for loading and preparing large datasets, connecting often siloed data sources, building or choosing the right analytical methodology or algorithms, analyzing and storing insights in the cloud, and creating the requisite reports. With so much data involved, the process can be monthslong, by which time the old data becomes less relevant while new raw datasets remain unaccounted for.
Time is only the first limitation. Some organizations are deterred by how complex and costly it appears to answer big questions, while others don’t even know what questions to ask of their data. Accordingly, huge swaths of data are left untouched, their insights all but squandered. And with more and more raw data being created every year, this analysis gap is only widening.
What’s holding businesses back?
There are three primary obstacles that companies must overcome in order to get the most out of their data: scale, location, and cost. While these challenges have always existed in the realm of data analytics, the extent to which data has proliferated has only made these tasks more arduous.
Lacking the capacity to sufficiently scale their analytic operations alongside the growth of their data, many organizations opt instead to ask smaller questions. However, the insights yielded from this diminished scope of analysis are far from optimal.
Naturally, the larger datasets become, the more difficult they are to store and transfer. Moving data from one location (source) to another in order to join all relevant sets in a place where they can be properly analyzed – a critical process in extracting insights – now requires an amount of time, storage, network and compute that some organizations simply don’t have.
Finally, getting big answers from big questions traditionally requires big budgets – more and more a luxury in today’s strained economic landscape. And even for organizations that have the budget to overcome the above barriers and mine the necessary insights to stay competitive, it is unlikely they are using these funds to their fullest.
Is big data getting too big?
Amid such mounting obstacles, what is the optimal way to cope with the ever-expanding data ecosystem?
These stores of data hold unprecedented potential to serve businesses – and the more data, the better the insights. Accordingly, the key to keeping up with big data growth clearly isn’t cutting back on the amount of data examined or limiting the scope of analysis. Rather, it lies in effective processing.
To that end, organizations must seek to adopt processing tools that will improve the ROI for the business insights they gain. Fortunately, we are seeing great strides in the abilities and availability of such processing tools.
Many enterprises have opted to maximize their cloud capabilities for both storage and analysis as a path to gain another layer of storage and strengthen processing solutions. Indeed, experts predict that worldwide cloud spending will surpass $1.3 trillion by 2025, illustrating just how many organizations see cloud optimization and related tools as critical digital allies. But as cloud adoption proliferates, and gradually becomes the new normal, it is crucial for enterprises to realize that the cloud may not always be the right solution given limitations on budget or scale. In fact, when data is peta-scale large or too complex for traditional tools, storing it on the cloud (as opposed to on-premise storage solutions) can require inconvenient amounts of processing power to store, move, share, and analyze the data, further increasing the friction that these organizations are trying to eliminate in the first place.
Regardless of which processing tools companies turn to, it is crucial that they look holistically at the requirements of their organization and choose programs that are best suited to their specific data needs. And above they must remember that the tools themselves are not always the key to data success. Rather, it’s how companies use them – thus, they would be wise to cultivate increasing expertise in the area of data science and data engineering.
Only by adopting robust data solutions that can mold to the ever-changing data landscape, will data-reliant companies (which, these days, should be most companies) stay ahead of the curve.
With the right approach, 2023 could be the year that reinvigorates our relationship to the vast amount of data we create, leading to the big payoffs that big data promises.
