Data creation is at an all-time high. There’s so much data, from increasingly diverse and dispersed locations and data sources. So many opportunities to use it. And so often, where the data needs to be used is far away from where it currently sits.
At this point, the conversation then moves to the challenges of moving the data: it’s complex to do over multiple networks or platforms, it’s expensive, it’s unpredictable, it’s slow… especially when moving data at scale. These factors become especially problematic when considering the element of “time”—when data isn’t moved quickly enough for an application to leverage it in a timely manner, like a cruise line providing personalized cruise photos to passengers as they disembark a ship. Not having data—or the right data—hinders innovation, limits strategic decision-making, and reduces operational efficiencies. So what do you do? Do the best with the data you can get to the right place, at the right time—and hope the actual outcome isn’t too far off from what you’re intending. And that your data was statistically significant enough to get you to the right answer—which means those outcomes aren’t misleading or incorrect results—e.g. AI hallucinations.
In a rudimentary example, imagine you’re ordering lunch for a team meeting. You could place an order based on what you think your colleagues want, but without asking them, you might miss out on someone being a vegetarian or having a specific allergy. Yet, while you might end up with a few hungry (and unhappy humans), you can usually rely on their own quality control and ability to decline an inappropriate meal. Such isn’t the same when you translate this same concept of statistical significance and “data tradeoffs” to a grander scale and apply it to organizational or agency-wide projects and workflows. Not only are the risk and revenue disruptions higher, but often the automated “actions” and processes driven by an analytics and artificial intelligence don’t necessarily have the same quality control function to filter for bad outcomes—and thus an entire project or program can fall victim to AI hallucinations.
Take for instance, genome sequencing—within which extensive data pools are analyzed to create personalized medicines and care, guide new drug development, and more. Because of the high costs, distance, and often genome data owner’s unwillingness to allow data to leave the premises (e.g. hospital system A sharing with hospital system B) associated with curating that data, researchers often must make compromises that limit the size, scope… and thereby potential efficacy… of their projects. That could mean working with partial data, or unknowingly not including the right variations of data—which could lead to non-optimal therapy development and poorer patient outcomes. But what if you didn’t have to compromise on the outcomes of your workloads?
What if the barriers to efficient and seamless data access were dismantled—so you didn’t have to choose between the data you could and should use? Today, it doesn’t matter what your data looks like, if it’s encrypted, where it’s located, or what your bandwidth limitations are. With new capabilities, like ultra-fast data movement (move data hyper fast) or remote data access (use it without moving it first), you can focus on how you can use your data rather than how you’re going to get it. That also means that you can better trust in the outcomes of your analysis, because you’re not making tradeoffs beforehand.
With Vcinity, you don’t have to choose. Put the power back in your hands and make holistic, efficient data access your strategic advantage—not a compromise.
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