Modern enterprises run on data, and those who leverage it effectively to become data-driven organizations gain significant competitive advantages over their competitors. For example, well over half (57%) of executives surveyed by NewVantage partners attributed their ability to accelerate new product releases to insights from data. The advantages gained from data don’t end at faster product delivery; they cascade throughout the organization, making it stronger and more valuable as a whole. According to a McKinsey study, data-driven companies acquire other organizations 23 times more often than do companies that are not data driven. Similarly, a landmark Harvard Business Review study found that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”
But becoming a data-driven organization is a complex journey that must begin with a modern data infrastructure that can provide flexibility, performance, and scale when working with the truly gargantuan amounts of data that modern enterprises are collecting. In 2020 alone, despite the disruption caused by the global COVID-19 pandemic, the world created more than 64 zettabytes of data, according to IDC. How much data is that? If every terabyte were a kilometer, the distance would cover nearly 14 round-trip journeys to Pluto.
A Seagate study estimates that by the end of 2022, the average enterprise will store approximately 2 PB of data, and only one-quarter of it is stored in a cloud repository. The rest is stored in silos across internally managed data centers, third-party data centers, and edge or remote locations — and 2 PB is just the average. A significant number of organizations could easily have five to ten times as much data. What’s more, according to that same Seagate study, the amount of data enterprises are storing is growing fast at a 26% annual compound growth rate, meaning the total amount of data is doubling every two to three years. Storing, ingesting, integrating, managing, and analyzing this crushing and ever-growing mountain of data on premises is far too expensive and cumbersome. In fact, in many cases, it’s impossible to properly query, analyze, and derive insights from this much data on premises — so, even if one puts cost and complexity aside, organizations simply are not getting full value from their data.
A cloud-first data strategy provides the scalability, resilience, agility, and flexibility required to deliver maximum value from enterprise data. With a cloud-first strategy, organizations can scale storage, databases, search, and analysis rapidly in response to sudden large inflows of data or big seasonal spikes in activity. They can also easily connect data sources and apply powerful analytics and other capabilities to them. The benefits are many. Decision makers can leverage insights to make data-driven decisions that accelerate the achievement of business goals. Security teams can find and access the data they need to ensure digital assets remain safe. Innovation grows. However, developing and adopting a cloud-first data strategy is not a simple task. It’s a complex journey, and it must take place on a sound technological foundation — a foundation that can help organizations overcome the many challenges they currently face when it comes to getting value from their data.
For starters, the data scientists and analytics specialists that organizations need to transform data into insights are in high demand and therefore are difficult to hire and retain. Organizations need a technology platform that enables them to get maximum value out of their data without needing to have an army of data scientists supporting them.
Organizations also need to be able to scale rapidly. When massive amounts of data begin to pour in — such as during the holiday season for the retail sector — the data and analytics teams need to be able to accommodate the sudden increase in volume. Their systems should have the resources to ingest the data from multiple sources, clean it, and analyze it, despite the massive spikes in traffic that may occur.
Data infrastructure must not be an impediment to innovation, especially in today’s modern dynamic marketplace. Developers and data teams must be free to experiment, iterate, and innovate to support the advancement of the business without having to worry about whether the underlying architecture will have the resources to support them.
And the data infrastructure must be secure and compliant. Customer and other sensitive data must be safe from malicious actors. It must also comply with the many regulations under which data falls, from the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) to data sovereignty requirements that govern where data may be stored.
Overcoming these challenges is impossible for an organization to do on its own. In fact, it’s inherent in the idea of a cloud-first data strategy that organizations cannot — and should not — make this a solo journey. While the details of every organization’s cloud-based data strategy will vary, there are several fundamental components, principles, and best practices that will set the course for success. And pulling all these elements together requires partners.
