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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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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