What Do Whiskey and Data Analytics Have in Common? Maturity Matters

We live in a world of data that’s growing at unprecedented rates. Not much of a surprise there—data sprawl has been news for a while. The problem is how to glean value from this data, given the challenges of storing, accessing, and analyzing it. There have been many tools promising the moon for data analysis, but few have met that promise—until today.

At this point, you’re probably wondering what whiskey has to do with data. It’s all about maturity. The great whiskies of the world become smoother and more refined the longer they age. Analytics and artificial intelligence (AI) have become better and better the more they’ve matured in the market (not in casks, though)—and modern data analytics demands similar mature and powerful infrastructure.

The more mature your use of AI and analytics is, the more value you’ll gain from your data—and the better your competitive posture as seen in the chart above. In this case, maturity is about capabilities and sophistication. Organizations need a full and holistic view into data, including an understanding of the past and the potential future. 

The Maturity Path to Data-driven Insights

Here’s how data analytics and AI maturity should progress, from least sophisticated to most sophisticated: 

• Data collection and exploration: Gathering data is better than collecting no data at all, but it doesn’t provide hindsight or foresight.
• Reporting: What happened in the past, and why did it happen?
• Predictive modeling: What could happen in the future, based on what we’ve discovered about the past? 
• Prescriptive analytics: How should we respond to what’s happened in the past? 
• Automated decision making: Data, machines, and algorithms combine their power to provide decision making and informed guidance.

How Mature Is Your Organization? 

Let’s look at some of the outcomes captured around analytics maturity in a recent ESG paper published by Pure. On a high level, an organization that has advanced its analytics maturity is:

• Rich in data and uses more data from more disparate data sources to feed analytics applications
• Heavily investing in analytics by spending more (as a percentage of their IT budgets) on analytics than their peers and are further along in their adoption of AI/ML technologies in strategic projects
• Focused on analytics and placing analytics initiatives among their top five business priorities, if not their number one priority

The Backbone for Mature Data Analytics

What gets you to this level of maturity? Careful consideration has to go into your supporting infrastructure. That’s where Pure comes in.

Data needs to be at the right place, at the right time, and in the right format. The infrastructure to support these goals needs to keep data scientists productive and efficient and keep valuable GPUs productively fed with data. This is in an environment where astronomical amounts of data are generated and gathered by most organizations, so when you want to generate value from that data, all of these pieces matter.

For data pipelines to be effective, the underlying storage infrastructure needs to be able to handle all kinds of data, scale seamlessly independent of compute, and provide cloud-service-like agility and simplicity. FlashBlade® is purpose-built for modern analytics and AI, powered by a massively parallel architecture from end to end.

To benefit from a mature state of analytics and AI, infrastructure is critical, in addition to applications, strategy, and processes. This optimal combination is as satisfying as a smooth, polished whiskey—one that’s perfectly matured.