Cloud migration is the first step to maximizing your data and AI

For businesses today, it’s not really a question of ‘if’ you should be investing in AI and Data Science, but rather ‘how much’ and ‘where’ you should be investing. Cloud migration is the key to getting started.

The tech advances of the last two decades have opened the door to major disruption and new, ‘digital native’ market entrants. This is true of almost every industry or sector. As technology has engrained itself in business processes and in our daily lives, the volume of data being produced and handled by organizations has grown exponentially and this growth shows no signs of slowing down. This creates challenges around how to store and manage data in a way that is cost effective and compliant with applicable regional and industry-specific legislation. But it also creates opportunities to generate game-changing insights and innovations.

Artificial Intelligence (AI), Machine Learning, Deep Learning and Data Science have been rapidly adopted by organizations as ways to make sense of the growing volumes of data that surround them, automate tasks, improve the accuracy of forecasts, identify and predict behaviors and trends, and create entirely new business models and services (think autonomous driving in automotive).

For most businesses today, it’s not really a question of ‘if’ you should be investing in AI and data science, but rather ‘how much’ and ‘where’ you should be investing.

When looking at how to start or scale up your AI, Machine Learning or Data Science activities, you should first look at how your existing IT landscape and data infrastructure are set up to support this ambition, and whether they will allow you to derive an attractive return on your investment in AI and Data Science. It’s not a coincidence that AI and Data Science have become more mainstream as cloud computing has gained widespread acceptance across industries – cloud is a key enabler.

There are several reasons why migration to the cloud is a crucial first step toward harnessing the power of AI and maximizing the value of your data. Here are some of them:

Eliminate data silos
AI and Data Science deliver most value when they have access to the right data, in sufficiently large volumes. With cloud, you can bring your data – structured and unstructured – into a single environment, thereby enabling you to analyze, model, and work with it more easily than if it were distributed across different technology platforms, programs or organizational silos.

Access class-leading Data and AI/ML tools offered by hyperscalers
Since the advent of cloud computing, the so-called hyperscalers Google (Google Cloud Platform), Microsoft (Azure) and Amazon (AWS) have built rich portfolios of tools and services (often bundled with underlying infrastructure) aimed at helping organizations derive new and differentiating insights from their data and build AI-based tools and business models. These ready-to-deploy tools enable organizations to get started and improve their use of AI and Machine Learning quickly and cost effectively.

Reduce compute and data storage costs
Running AI and Machine Learning algorithms can be very compute intensive. For the vast majority of businesses, the energy efficiency of their IT infrastructure lags well behind that of cloud computing providers – most of which treat the provision of compute power and data storage as their core business and so are naturally motivated to relentlessly pursue energy efficiency and maximize the utilization of their IT hardware (see the case of Google Data Centers as an example). As such, and considering the continuing growth of data, it’s not cost efficient to deploy large-scale AI or data-driven applications or projects on legacy platforms when cloud options are available.

Scale up or down to reflect your business needs
Data-driven applications and AI generate most value when they’re given large data sets to handle and process. The IP behind the tool is important, but the speed with which the tool or service is able to process data and return insightful results can be the key to competitive advantage. An important requirement these days is for insights or data visualizations to be as ‘real time’ as is possible, regardless of the size of data sets or the demand for service. Cloud affords organizations the scalability to deal with peaks and troughs in demand, and to accommodate large new volumes of data in a way that legacy systems can’t.

Address skill shortages and attract top talent
As cloud-native becomes the preferred choice for new business apps and services, the global tech talent pool naturally gravitates towards where the action is. With certification – be it Azure, AWS or GCP – an increasingly important factor in remuneration, and with skilled engineers, developers, and data scientists not lacking for opportunity, the tech stack used by an organization can be key to attracting the right talent. Working on the latest tools on industry-leading platforms is naturally more appealing to professionals that are keen to stay at the forefront of innovation than working on outdated, on-premise platforms. Working on modern, universally used platforms also makes it easier to augment your existing capabilities with support from external vendors and partners, and to integrate data from third parties. In short, being on the cloud means having more options and more opportunity.

Re-deploy budget from support and maintenance to innovation
Legacy systems and on-premise infrastructure require significant ongoing investment in maintenance and development. Despite the allure of AI and data-driven applications and services, most organizations operate with finite IT budgets so ambitions need to be kept in check. Migrating to the cloud enables organizations to free themselves of a large part of the maintenance and development costs, to stop worrying about the underlying infrastructure, and to start focusing budget and effort on initiatives capable of driving growth and revenue (like AI and data).

Manage data security easily and cost effectively
Organizations today treat data as one of their most important assets. Up until relatively recently, the idea of hosting valuable data on the public cloud seemed at odds with this notion. These days, however, with centralized access control, role management, infrastructure management, and data encryption features, it’s arguably now easier to ensure data security in the cloud than it is with on-premise, legacy platforms. This potentially removes a key concern and frees businesses to focus on achieving their data-driven ambitions.

Innovate faster and reduce time to market
Cloud and deployment methods like containerization enable organizations to provision IT resources quickly and cost effectively, and then deploy them to production environments with minimum hassle. This enables ideas to be turned into fully fledged products and services much faster than would be possible if limited to the use of legacy, on-premise platforms. This, combined with freed-up budget for value-adding activities, can result in accelerated innovation and a reduced time to market.

We at Cledar recently helped a traditional financial services organization migrate from its legacy .NET infrastructure to a cloud environment, enabling it to reduce support and maintenance costs, and laying a solid foundation for a future of innovation.

The team at Cledar have been helping international organizations of all shapes and sizes maximize the value of their data since well before the widespread adoption of cloud computing and deployment technologies like containerization. While leading functions and domains at CERN, one of the world’s largest and most respected centers for scientific research and home to the Large Hadron Collider (LHC), Cledar’s founders – Hubert Niewiadomski and Piotr Nyczyk – helped scientists from a variety of institutions capture, manage, and transform huge volumes of data into meaningful insight.