By Rachel Lee - September 9, 2021
The current reality for most companies is that they lack sufficient processing power to leverage all the data available to them. However, this bottleneck in processing power isn’t technological—it’s human.
Historically, accessibility to data was a major barrier to effective data utilization. Data lived in silos, controlled via disparate access points, each with its own governance. In contrast, today’s modern data stack broadened data accessibility by consolidating data in cloud warehouses. Yet in many cases, this increase in data availability hasn’t led to a corresponding increase in data utilization.
This gap between available data and utilized data can be a frustrating setback for companies that have made large investments in their data stack. There’s more than enough material to work with, but the work isn’t getting done. What’s the problem?
Now that data is ubiquitous, and cloud services have made storing data affordable, that bottleneck has reversed. Automation has made data collection, integration, and storage more affordable. But the process of translating that data into business outcomes is still a manual one, and good analysts aren’t cheap.
Even if they were, hiring more of them wouldn’t be the answer, at least not in the long term. There’s simply no way to hire enough people to get ahead of the problem. Data collection and automation (which is only picking up speed) means that there will continue to be exponentially more questions to answer and hypotheses for analysts to test.
While scaling with human capital may not be sustainable, there is a way businesses can leverage the data they’re collecting today to make better decisions. It lies in taking the natural ingenuity that people have and bolstering those with technology. We’re not talking about cyborgs, but decision intelligence engines that augment analytics workflows by processing massive amounts of granular data in real time.
These tools offer the ability to automate a broad swath of the most routine, tedious tasks that analysts handle. In turn, those employees are free to focus on more complex and challenging problems. This relationship represents the best-case scenario for many AI and machine learning applications – automation gets leveraged for the boring, time-consuming stuff, while people do what they do best (think, innovate, create). In a nutshell, the role of the analyst isn’t going to disappear, but it will soon be much more productive and empowered. In a handful of market-leading companies, it already is.
Some executives are well aware of the human bottleneck issue delaying data-driven decisions for their companies, and a few are already embracing augmented analytics tools. However, not everyone understands this problem equally. For some, the problem is merely that more questions are asked, but an increasingly smaller percentage of them are answered.
If you’re facing a bottleneck in your data teams analytics workflow, but not sure exactly where the issue lies, ask yourself a few questions:
If you answer yes to those, you’re undoubtedly experiencing the effects of the analytics bottleneck. The longer it goes on, the more obvious it will become, and the more opportunities to make critical decisions for the business will have passed.
The silver lining is that you already have valuable data at your disposal (probably loads of it), and you’ve got smart people on board who want to help leverage that data. They just don’t have the means. It’s a bit like having a grand architectural vision, an overabundance of materials, and a crew of highly skilled builders – but no power equipment or machinery. They’ll do what they can, but without ambitious changes and better equipment, that vision won’t be turning into reality anytime soon. It’s time to invest in some new tools.