The first step to constructing and implementing a multi-cloud approach is determining your needs. But once needs are determined, then what? Learn here in the second blog of our five-part blog series.
Understanding business, technical and end-user requirements arms you to make better-informed decisions about a host of architecture components, including security and compliance challenges, networking features and service providers. But before addressing any of these components requires you to first consider technology capabilities.
At this point in planning a multi-cloud strategy, many IT decision-makers ask, “What technologies and applications do we need?” Answering that question requires a big-picture perspective that takes into account the existing and emerging technologies (and their applications) impacting your multi-cloud architecture.
Today, there are two technologies influencing multi-cloud decision-making more than any others: artificial intelligence (AI) and Internet of things (IoT) devices.
Running diverse workloads and deployment models leads to managing higher volumes of applications and data. This can add complexity that halts efficiency and degrades performance, especially when it comes to IT troubleshooting.
AI is emerging as a solution for streamlining these complexities. Not only can AI technology and machine learning enable networks to process AND interpret rising data volumes, but they also help IT staff identify and resolve infrastructure issues faster.
Like with most next-gen technologies, the benefits of AI are much hyped, but they aren’t a realistic or catch-all solution for every company. Proactively considering how an AI solution fits into your multi-cloud strategy as well as your existing technology stack is essential for successful deployment.
Business Insider has reported that over 20 billion IoT devices will be connected to the Internet by 2020. TechTarget defines an IoT device as “any nonstandard computing device that connects wirelessly to a network and has the ability to transmit data,” with examples ranging from household tools like thermostats and door locks to sophisticated instruments like radio-frequency identification (RFID) implants and pacemakers.
Similar to AI and machine learning technologies, IoT devices help accelerate the transmission and interpretation of data. To accomplish this, infrastructure needs to be optimized to combat latency and boost performance, which can be challenging inside a multi-cloud architecture with geographically dispersed user bases. For this reason, it’s important to understand your IoT device needs (today and into the future) as well where and how infrastructure needs to be deployed for optimal performance.
Understanding your technology options is one thing, but effectively including them within a multi-cloud architecture is another. Bridging this gap urges you to understand the specific challenges facing your multi-cloud deployment.
Next in our five-part series, we’ll explore the key decision-points involved with understanding and overcoming your business’ multi-cloud architecture challenges. If you’d like to read the first installment of the series, check out “How to Determine Your Needs for a Multi-Cloud Architecture.”