Subbu is CEO at Aerospike. He has over 25 years of experience leading teams at innovative enterprise technology companies.
McKinsey recently published a study highlighting seven characteristics that will define the data-driven enterprise in 2025, one of which is that data will be routinely processed and delivered in real time.
The study states: “Only a fraction of data from connected devices is ingested, processed, queried and analyzed in real time due to the limits of legacy technology structures, the challenges of adopting more modern architectural elements, and the high computational demands of intensive, real-time processing jobs. Companies often must choose between speed and computational intensity, which can delay more sophisticated analyses and inhibit the implementation of real-time use cases.”
Naturally, I agree with McKinsey’s take on the subject. But while they use 2025 as a target date for their projection, we already live in a “right now” world. New opportunities and new business models are opening up every day. Whether you’re a scrappy startup or an established company reimagining your offerings, it’s critical to satisfy customers — whether consumers or B2B clients — in the moments that matter.
The speed and ease with which a company — and its supporting technologies — can deliver accurate information, orders and intelligent actions directly affect satisfaction and results. Data is behind the scenes of all these technologies — from everyday actions to critical business events like fraud detection, money transfers and smart manufacturing. Every action, every decision, is data-driven. As McKinsey notes, thriving in the “right now” economy requires real-time processing of massive volumes of constantly-changing data.
Moving At Machine Speed
Successfully dealing with all this data goes far beyond new hardware and software. It requires a new mindset. The days of moving at “people speed” are over. Companies today need to move at “machine speed” and “machine scale.” For many companies, the challenge is moving at machine speed in a cost-efficient and profitable manner.
Let’s look at the financial services sector, for example. Digital disruptors are offering new services that make things a lot easier and seamless for the customer. They’re often doing it without the fees that the traditional institutions have charged. Many legacy organizations understand this threat and are moving with haste to modernize their architectures.
In a recent earnings call, Jamie Dimon was candid in explaining why JPMorgan is spending up to $12 billion on technology this year. The CEO told analysts, “If we don’t… we’ll be clunky and inefficient and hamstrung in the future when we’re trying to compete.” He acknowledged that the competitive landscape is far different in 2022: “There’s global competition, there’s nonbank competitions, direct fiber lending competition … there’s fintech competition. There’s a lot of competition, and we intend to win.”
Winning involves more than just beating competitors. It also means operating in a financially prudent manner. In the “right now” economy, data grows as the company grows.
Best Practices For The “Right Now” Economy
Unfortunately, in their zeal to get ahead, too many companies absorb enormous additional costs related to storage, network resources, memory and CPU as they attempt to keep pace with the exponential growth of data. This includes companies operating in or moving to the cloud. Companies that want to avoid having to “choose between speed and computational intensity” must have the right architecture in place, such as a real-time platform that can grow without busting budgets.
Beyond technology, companies should work on three strategies as they move toward a data-driven enterprise.
• Develop a different mindset. The days of a winner-take-all marketplace are over. Many families have different vehicles — sedans, SUVs, pick-up trucks, etc. Why? They serve different purposes. In the real-time enterprise, you’ll find a broad variety of technologies that maximize the value of data. Different workloads and different data strategies call for different solutions. Managers must be open to using the technologies that best serve their customers and future-proof their real-time data stack.
• Plan for the unexpected. A Wall Street Journal story about the “almost astronomical amount of data” being generated by businesses, individuals and governments referenced an IDC statistic that nearly 64 zettabytes of data were used last year, “that’s 64 followed by 21 zeros.” Those numbers were unimaginable just a few years ago. Guess what? It’s going to get worse. Corporate leaders need to factor into their planning the economics of scaling. You must be able to grow without being hurt by costs.
• Embrace risk thoughtfully. Institutional resistance to change is common. When a new problem arises, it’s easy to lean on the existing infrastructure and tools the organization has invested heavily in and that people are comfortable using. There’s risk in change — for both the company and leader’s individual careers. But those that don’t change their data management strategies are doomed. Having said that, it is possible to start with projects that won’t shut the business down but will deliver a measurable return. Build from there.
In addition to architecture, there are a number of other critical technology areas that are fundamental for success in the “right now” economy. Security is at the top of the list. In an upcoming article, we’ll examine how to implement strategies that are scalable, verticalized and sustainable.