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Companies Need Both Data Modernization and Cloud Migration Strategies to Enable Successful AI Initiatives

Data modernization and cloud migration are reaching a tipping point among large and medium-sized businesses as many companies double their data footprints once or twice a year, according to a new Deloitte survey, Data modernization and the cloud: Which trend is driving the other?

As organizations look at different ways to incorporate artificial intelligence (AI) and other data-based technologies into their business models, the study shows that both cloud computing and data modernization are simultaneously reinforcing one another.

More than 9 in 10 organizations surveyed now primarily keep their data on cloud platforms, with 55% of respondents seeing data modernization as a key reason for cloud migration, second only to security and data protection.

Approximately one-third (34%) of companies claim to have fully implemented data modernization, and another 50% say they have data modernization initiatives in progress.

"For companies to be able to survive and thrive in today's digitally-driven business ecosystem, they must accelerate advancement on both cloud migration and data modernization to help address complex business and information challenges," said David Linthicum, Managing Director and Chief Cloud Strategy Officer, Deloitte Consulting LLP. "Perhaps the biggest risk now is focusing on one area without the other and failing to get ahead of organizational and complexity issues that could derail progress and profits."

Obstacles to Data Modernization and Cloud Migration

Deloitte's survey shows that the biggest impediments to data modernization — the act of moving legacy databases to modern databases to store unstructured data such as customer voice audio, social media comments, etc. — are budget/cost concerns (55%) and a lack of understanding of technology (44%). These reasons are followed closely by lack of consensus among decision makers (41%) and clarity on metrics (40%). And, while most companies report having data modernization efforts underway, less than half (48%) of respondents say they have a specific, formal initiative in place.

When it comes to cloud, while migration is high, complexity threatens future success, with 45% of respondents agreeing that heterogeneity of data is likely the biggest obstacle to leveraging cloud in the next two to five years. However, migration can be more complicated than expected, and 47% consider complexity a primary risk to return on investment (ROI), with the biggest challenges coming in the areas of CloudOps (29%) and DevOps (29%). 28% feel that the primary barrier to solving cloud complexity is having enough skilled talent, and 49% believe that the best strategy for dealing with cloud complexity is training.

Cloud Migration and Data Modernization Should Reinforce Each Other

While the survey confirms that both cloud migration and data modernization have good momentum, many organizations are strongly aligned around only one of these objectives.

"Over the past several years, many companies have started to shift from a data architecture based on relational enterprise data warehouses and data lakes to modernized platforms," said Ashish Verma, Managing Director and Analytics and Information Management Lead, Deloitte Consulting LLP. "Given that data is the linchpin of AI, analytics and other cognitive technologies, companies must consider augmenting their strategies to ensure that they're embracing both cloud and data simultaneously to help better position their businesses, now and in the future."

Survey methodology: The survey was conducted in April 2019 among 500+ respondents in the United States working in IT groups within medium-sized to large companies. Companies have annual revenues in excess of $500 million, and 60% have revenues of more than $1 billion. Respondents include C-suite executives (46%), senior executives/heads of business units (30%) and managers or programmers (24%). All respondents reported being involved in or making decisions about cloud and/or data management issues.

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Companies Need Both Data Modernization and Cloud Migration Strategies to Enable Successful AI Initiatives

Data modernization and cloud migration are reaching a tipping point among large and medium-sized businesses as many companies double their data footprints once or twice a year, according to a new Deloitte survey, Data modernization and the cloud: Which trend is driving the other?

As organizations look at different ways to incorporate artificial intelligence (AI) and other data-based technologies into their business models, the study shows that both cloud computing and data modernization are simultaneously reinforcing one another.

More than 9 in 10 organizations surveyed now primarily keep their data on cloud platforms, with 55% of respondents seeing data modernization as a key reason for cloud migration, second only to security and data protection.

Approximately one-third (34%) of companies claim to have fully implemented data modernization, and another 50% say they have data modernization initiatives in progress.

"For companies to be able to survive and thrive in today's digitally-driven business ecosystem, they must accelerate advancement on both cloud migration and data modernization to help address complex business and information challenges," said David Linthicum, Managing Director and Chief Cloud Strategy Officer, Deloitte Consulting LLP. "Perhaps the biggest risk now is focusing on one area without the other and failing to get ahead of organizational and complexity issues that could derail progress and profits."

Obstacles to Data Modernization and Cloud Migration

Deloitte's survey shows that the biggest impediments to data modernization — the act of moving legacy databases to modern databases to store unstructured data such as customer voice audio, social media comments, etc. — are budget/cost concerns (55%) and a lack of understanding of technology (44%). These reasons are followed closely by lack of consensus among decision makers (41%) and clarity on metrics (40%). And, while most companies report having data modernization efforts underway, less than half (48%) of respondents say they have a specific, formal initiative in place.

When it comes to cloud, while migration is high, complexity threatens future success, with 45% of respondents agreeing that heterogeneity of data is likely the biggest obstacle to leveraging cloud in the next two to five years. However, migration can be more complicated than expected, and 47% consider complexity a primary risk to return on investment (ROI), with the biggest challenges coming in the areas of CloudOps (29%) and DevOps (29%). 28% feel that the primary barrier to solving cloud complexity is having enough skilled talent, and 49% believe that the best strategy for dealing with cloud complexity is training.

Cloud Migration and Data Modernization Should Reinforce Each Other

While the survey confirms that both cloud migration and data modernization have good momentum, many organizations are strongly aligned around only one of these objectives.

"Over the past several years, many companies have started to shift from a data architecture based on relational enterprise data warehouses and data lakes to modernized platforms," said Ashish Verma, Managing Director and Analytics and Information Management Lead, Deloitte Consulting LLP. "Given that data is the linchpin of AI, analytics and other cognitive technologies, companies must consider augmenting their strategies to ensure that they're embracing both cloud and data simultaneously to help better position their businesses, now and in the future."

Survey methodology: The survey was conducted in April 2019 among 500+ respondents in the United States working in IT groups within medium-sized to large companies. Companies have annual revenues in excess of $500 million, and 60% have revenues of more than $1 billion. Respondents include C-suite executives (46%), senior executives/heads of business units (30%) and managers or programmers (24%). All respondents reported being involved in or making decisions about cloud and/or data management issues.

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The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...