Skip to main content

IBM and Deloitte Announce DAPPER, AI-Enabled Managed Analytics Solution

IBM and Deloitte announced a new offering—DAPPER, an AI-enabled managed analytics solution.

The solution reinforces the two organizations' 21-year global alliance—which helps organizations accelerate the adoption of hybrid cloud and AI across the enterprise—and 10 years of experience implementing the Deloitte Analytics Platform.

DAPPER's end-to-end capabilities will allow organizations to gain confidence in the insights that their data provides via a secured, simple to consume managed service offering that aims to resolve the challenges of adopting AI.

DAPPER is a result of the combined technology leadership, hands-on business experience, and industry experience of the Deloitte and IBM alliance. DAPPER combines the Deloitte Analytics Platform with IBM Cloud Pak for Data on Red Hat OpenShift to offer business users a fully-managed AI solution­–designed to avoid needing to commit the resources and lengthy time associated with developing, implementing, and managing a bespoke solution.

Built upon IBM's modern hybrid cloud architecture and AI technology, DAPPER brings organizations the ability to scale up operations, promote an organization's trust in its data, and enable smart reporting. DAPPER is a managed service run by Deloitte in the IBM Cloud, and available on premises or in multi-cloud environments. Its fully-managed analytics service provides a choice of service offerings and subscription model, and contains three core elements, built so that businesses can obtain maximum value out of their data:

- A cloud-enabled analytics platform: Security and analytics platform backed by IBM with streamlined administration, operations and maintenance provided by Deloitte, engineered to give business users a seamless, automated data analytics solution.

- Analytics development factory: Access to specialized analytics building blocks and methods engineered to streamline analytics development with operations, giving a DevOps experience to help accelerate the delivery of analytics assets.

- Catalog of subscription service offerings: Combines simplicity with data integrations to offer a selection of analytics services including dashboards, enterprise reporting, data management tools, and orchestration and consumption of AI.

With DAPPER, Deloitte and IBM can help data scientists and business users alike build, organize, and manage insight-providing assets to help promote reporting that can be trusted within an organization and AI solutions so that enterprises can focus on business outcomes.

For example, with DAPPER, Deloitte and IBM can help enable a government organization to address fraud, such as tax fraud or those related to employment benefits, pension, or money laundering. With DAPPER, an analytics cloud can collect and help business users report upon siloed data across disparate data centers, making data available for advanced AI algorithms and reports, operating associated use cases, and supporting clients' regulatory compliance requirements. Delivered as a managed service offering, all of this is designed to be deployed within weeks rather than months, and draws from real-time data across various business units or in this example, government agencies.

"Today's announcement is another proof point of the companies' longtime collaboration to help clients modernize with advanced technology," said Evaristus Mainsah, GM, Hybrid Cloud and Edge Ecosystem, IBM. "Together, Deloitte and IBM have created a flexible hybrid cloud engine that can extract real value for clients by using AI to make the most of their data. DAPPER will drive transformation, spur innovation, and reshape business to pave a pathway to digital dominance."

Richard Houston, Senior Partner and CEO Deloitte North & South Europe said, "DAPPER is built on 10 years of Deloitte's business and technology innovation. It represents another market-leading example of how Deloitte and IBM have combined technology, experience and industry knowledge to help clients turn data into insights—quickly, reliably, and repeatedly."

Deloitte, an IBM Platinum Business Partner, is part of IBM's partner ecosystem, an initiative to support partners of all types—whether they build on, service or resell IBM technologies and platforms—to help clients manage and modernize workloads from the mainframe to the edge and everything in between using IBM solutions and Red Hat OpenShift, the industry's leading enterprise Kubernetes platform.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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 ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

IBM and Deloitte Announce DAPPER, AI-Enabled Managed Analytics Solution

IBM and Deloitte announced a new offering—DAPPER, an AI-enabled managed analytics solution.

The solution reinforces the two organizations' 21-year global alliance—which helps organizations accelerate the adoption of hybrid cloud and AI across the enterprise—and 10 years of experience implementing the Deloitte Analytics Platform.

DAPPER's end-to-end capabilities will allow organizations to gain confidence in the insights that their data provides via a secured, simple to consume managed service offering that aims to resolve the challenges of adopting AI.

DAPPER is a result of the combined technology leadership, hands-on business experience, and industry experience of the Deloitte and IBM alliance. DAPPER combines the Deloitte Analytics Platform with IBM Cloud Pak for Data on Red Hat OpenShift to offer business users a fully-managed AI solution­–designed to avoid needing to commit the resources and lengthy time associated with developing, implementing, and managing a bespoke solution.

Built upon IBM's modern hybrid cloud architecture and AI technology, DAPPER brings organizations the ability to scale up operations, promote an organization's trust in its data, and enable smart reporting. DAPPER is a managed service run by Deloitte in the IBM Cloud, and available on premises or in multi-cloud environments. Its fully-managed analytics service provides a choice of service offerings and subscription model, and contains three core elements, built so that businesses can obtain maximum value out of their data:

- A cloud-enabled analytics platform: Security and analytics platform backed by IBM with streamlined administration, operations and maintenance provided by Deloitte, engineered to give business users a seamless, automated data analytics solution.

- Analytics development factory: Access to specialized analytics building blocks and methods engineered to streamline analytics development with operations, giving a DevOps experience to help accelerate the delivery of analytics assets.

- Catalog of subscription service offerings: Combines simplicity with data integrations to offer a selection of analytics services including dashboards, enterprise reporting, data management tools, and orchestration and consumption of AI.

With DAPPER, Deloitte and IBM can help data scientists and business users alike build, organize, and manage insight-providing assets to help promote reporting that can be trusted within an organization and AI solutions so that enterprises can focus on business outcomes.

For example, with DAPPER, Deloitte and IBM can help enable a government organization to address fraud, such as tax fraud or those related to employment benefits, pension, or money laundering. With DAPPER, an analytics cloud can collect and help business users report upon siloed data across disparate data centers, making data available for advanced AI algorithms and reports, operating associated use cases, and supporting clients' regulatory compliance requirements. Delivered as a managed service offering, all of this is designed to be deployed within weeks rather than months, and draws from real-time data across various business units or in this example, government agencies.

"Today's announcement is another proof point of the companies' longtime collaboration to help clients modernize with advanced technology," said Evaristus Mainsah, GM, Hybrid Cloud and Edge Ecosystem, IBM. "Together, Deloitte and IBM have created a flexible hybrid cloud engine that can extract real value for clients by using AI to make the most of their data. DAPPER will drive transformation, spur innovation, and reshape business to pave a pathway to digital dominance."

Richard Houston, Senior Partner and CEO Deloitte North & South Europe said, "DAPPER is built on 10 years of Deloitte's business and technology innovation. It represents another market-leading example of how Deloitte and IBM have combined technology, experience and industry knowledge to help clients turn data into insights—quickly, reliably, and repeatedly."

Deloitte, an IBM Platinum Business Partner, is part of IBM's partner ecosystem, an initiative to support partners of all types—whether they build on, service or resell IBM technologies and platforms—to help clients manage and modernize workloads from the mainframe to the edge and everything in between using IBM solutions and Red Hat OpenShift, the industry's leading enterprise Kubernetes platform.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

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 ...

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 ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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 ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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 ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...