As there are many cloud providers and solutions on the market today, in the era of Data Science, there is more than just hosting and basic solutions or costs to consider to serve the enterprise with analytics. In order to maximize data-driven insights, one has to consider the day-to-day skillset of the analytical and business intelligence (BI) or analysts within the organization and the industry it serves.
Flexibility with Hybrid Cloud Offerings: When considering a migration to the cloud, some things are legacy apps that need to remain running as is while other apps and IT rollouts will begin with a cloud strategy. Organizations may also consider a phased approach when migrating to the cloud over a period of time. Any solution should allow for a phased approach or a step approach of combing legacy data centers with cloud offerings. This allows for innovation while also minding the store to ensure things are moving towards the future while maintaining the current integrity of the business.
Compliance. As many businesses consider moving to cloud-based solutions, there are many and various regulatory issues that are both global and industry-specific. The solution chosen by end-users needs to be at the forefront by committing to these GDPR regulations.
Compatibility and Integration: While most organizations want to harness the power of data science, machine learning and artificial intelligence, the process is an evolution rather than a revolution. Integration, compatibility and in-house familiarity will all be key components in order to serve next-generation tools and insights. Solutions to the problem should makes it easy for the many using Windows Server, SQL Server, Exchange and other technologies to move to the cloud. That process will be ongoing and continuous, and you want to provide yourself with the highest chance of success.
While most organizations focus on the end goal of cutting-edge technologies, cost savings, etc., you must consider the process in order to get there. To achieve success, consider your skillset, flexible hybrid approaches, integration as well as industry-specific compliance issues when considering cloud options. IT process bottlenecks, that are typically driven by adoption and familiarity internally, or an external regulatory issue are important considerations to consider on the way to data-driven insights and data science production.