Keys to success for data projects in multi-cloud environments

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The Cloud has already become mainstream and there is a strong trend towards the adoption of multi-cloud environments. Find out what it is all about.
At the IT architecture level , the cloud has already become mainstream . And there is a strong trend towards the adoption of multi -cloud environments . However, to gain a real competitive advantage, organizations need to make multi-cloud environments truly empower their ability to explore and analyze data and transform it into knowledge, in order to turn information into intelligence and action . And to do this, they need a data strategy that enables IT control and facilitates use by people, regardless of where the analysis is performed or the data resides.

What aspects must be taken into account when developing a comprehensive data management strategy that works in these environments ?





A study found that 73% of executives believe their data office 365 database are the key to creating business value and more than half (51%) plan to leverage multiple cloud providers to achieve this strategy. However, only 34% have an effective cloud data management strategy.

Source: Harvard Business Review



Great challenges
To compete and grow in today’s challenging business landscape, companies need an insight-centric strategy that embeds analytics capabilities across the organization. To do so, they need to apply a variety of digital science tools—BI, machine learning, and AI analytics, among others . And they need to be able to process and analyze data from multiple locations—on-premises, across clouds , or from a variety of endpoints. They need to have consistent security and governance policies in place, and be able to leverage the best infrastructure options to achieve agility and elasticity across multiple cloud environments . They also need to be able to maintain an open platform so that data is available where it’s needed in a consistent, secure, and traceable manner.

Achieving the above is not so simple. There are several challenges to realizing the full potential value of enterprise data ( which today can be distributed across private, hybrid and multiple public cloud environments ) . Data silos, data lock-in and lack of interoperability are among the biggest strategic impediments facing organizations .

In addition, it must be considered that to convert data into intelligence, it is necessary to involve different data science tools, different analytical frameworks and machine learning and artificial intelligence techniques , which adds even more complexity .

However, don't be overwhelmed : with the right strategy, you can meet these challenges and properly manage data spread across different environments, ensure security and governance , and track data lineage.