March 31, 2026
Turning Data Into Insights
With the right strategy, systems and partnerships, organizations can unify their fragmented data stores and accelerate their artificial intelligence initiatives.
Enterprises have more data than ever, and yet many struggle to turn this information into insights that drive meaningful decisions. Fragmented systems, legacy tools and limited in-house analytics expertise often result in dashboards that provide little clarity and reports that fail to spur action. With the emergence of generative artificial intelligence, business leaders are taking a new look at their data programs, searching for solutions that will help their organizations take advantage of emerging technologies and create a competitive advantage. An effective data program requires not only modern tools but also organizational changes including executive sponsorship for data programs, the unification of data silos, proactive governance initiatives and increased automation for IT tasks. For data programs to succeed, they must promote real-time visibility, self-service analytics and AI-ready data, and they must be aligned with critical business outcomes. A trusted partner such as CDW can help supplement in-house talent, provide vendor-agnostic advice, accelerate time to value, and optimize organizations’ data programs as business goals and technologies evolve.
Enterprises have more data than ever, and yet many struggle to turn this information into insights that drive meaningful decisions. Fragmented systems, legacy tools and limited in-house analytics expertise often result in dashboards that provide little clarity and reports that fail to spur action. With the emergence of generative artificial intelligence, business leaders are taking a new look at their data programs, searching for solutions that will help their organizations take advantage of emerging technologies and create a competitive advantage. An effective data program requires not only modern tools but also organizational changes including executive sponsorship for data programs, the unification of data silos, proactive governance initiatives and increased automation for IT tasks. For data programs to succeed, they must promote real-time visibility, self-service analytics and AI-ready data, and they must be aligned with critical business outcomes. A trusted partner such as CDW can help supplement in-house talent, provide vendor-agnostic advice, accelerate time to value, and optimize organizations’ data programs as business goals and technologies evolve.
Data volumes continue to explode. Artificial intelligence (AI) tools are everywhere. And yet, many enterprises are struggling to attain actionable insights from this potentially powerful combination.
In sectors such as financial services, healthcare and retail, organizations are collecting more structured and unstructured data than ever before. Semrush reports that 181 zettabytes of data were created in 2025, nearly 100 times as much as the 2 zettabytes generated worldwide in 2010. And according to one study, the average organization collects data from around 400 different sources, with some businesses drawing from more than 1,000 data sources to feed their business intelligence and analytics systems.
The problem isn’t that today’s tech tools are insufficient, and it’s certainly not that companies lack vast stores of valuable data. Rather, many organizations lack a coherent data strategy that helps them identify and adopt the systems and practices needed to use that information effectively. Common challenges include fragmented data sources, legacy business intelligence tools that are costly and slow to adapt, and an overreliance on technical teams to generate reports.
As a result, analytics programs often become reactive rather than strategic. Business and IT teams may have access to countless dashboards, for example, but these tools typically lack the context, governance and alignment needed to inform critical decisions. Instead of using their data to forecast where markets are going and make forward-looking decisions about hiring, operations and product development, too many companies rely on their analytics programs largely to explain events that have already happened.
These limitations are directly tied to negative business impacts including delayed decisions, inconsistent metrics across departments, compliance risks, and missed opportunities to improve customer experience or operational efficiency. Again, the core issue is not a lack of technology but rather a lack of clarity, integration and maturity. Historically, data platforms have largely been designed by technologists, with little input from business users, and this gap has engendered skepticism within some organizations about whether data programs can make meaningful improvements to the business. While executives and business users want to use analytics to inform their decision-making, many simply do not trust the data delivered by IT shops due to previous poor experiences.
The rise of generative AI has created a new sense of urgency around maximizing the value of data. Business leaders know that AI tools can help improve worker productivity, make supply chains more efficient and help companies deliver better customer service. However, each of these benefits depends on the quality of an organization’s data. With poor inputs, AI tools will struggle to deliver value and may actually guide companies down the wrong path based on faulty information. This is a key reason why many companies have struggled to move AI from experimental pilots to full production deployments that lead to measurable business value.
With companies across sectors racing to disrupt their industries with AI, data is perhaps the ultimate competitive differentiator. Organizations that can move beyond static reporting toward governed, scalable and decision-ready insights will be better positioned to respond to change, maximize ROI and empower leaders and users at every level of the enterprise.
63%
The percentage of business leaders who describe their organizations as “data-driven”; under half (49%) say they can reliably generate timely insights
Source: deloitte.com, “AI trends 2025: Adoption barriers and updated predictions,” Sept. 15, 2025
Data volumes continue to explode. Artificial intelligence (AI) tools are everywhere. And yet, many enterprises are struggling to attain actionable insights from this potentially powerful combination.
In sectors such as financial services, healthcare and retail, organizations are collecting more structured and unstructured data than ever before. Semrush reports that 181 zettabytes of data were created in 2025, nearly 100 times as much as the 2 zettabytes generated worldwide in 2010. And according to one study, the average organization collects data from around 400 different sources, with some businesses drawing from more than 1,000 data sources to feed their business intelligence and analytics systems.
The problem isn’t that today’s tech tools are insufficient, and it’s certainly not that companies lack vast stores of valuable data. Rather, many organizations lack a coherent data strategy that helps them identify and adopt the systems and practices needed to use that information effectively. Common challenges include fragmented data sources, legacy business intelligence tools that are costly and slow to adapt, and an overreliance on technical teams to generate reports.
As a result, analytics programs often become reactive rather than strategic. Business and IT teams may have access to countless dashboards, for example, but these tools typically lack the context, governance and alignment needed to inform critical decisions. Instead of using their data to forecast where markets are going and make forward-looking decisions about hiring, operations and product development, too many companies rely on their analytics programs largely to explain events that have already happened.
These limitations are directly tied to negative business impacts including delayed decisions, inconsistent metrics across departments, compliance risks, and missed opportunities to improve customer experience or operational efficiency. Again, the core issue is not a lack of technology but rather a lack of clarity, integration and maturity. Historically, data platforms have largely been designed by technologists, with little input from business users, and this gap has engendered skepticism within some organizations about whether data programs can make meaningful improvements to the business. While executives and business users want to use analytics to inform their decision-making, many simply do not trust the data delivered by IT shops due to previous poor experiences.
The rise of generative AI has created a new sense of urgency around maximizing the value of data. Business leaders know that AI tools can help improve worker productivity, make supply chains more efficient and help companies deliver better customer service. However, each of these benefits depends on the quality of an organization’s data. With poor inputs, AI tools will struggle to deliver value and may actually guide companies down the wrong path based on faulty information. This is a key reason why many companies have struggled to move AI from experimental pilots to full production deployments that lead to measurable business value.
With companies across sectors racing to disrupt their industries with AI, data is perhaps the ultimate competitive differentiator. Organizations that can move beyond static reporting toward governed, scalable and decision-ready insights will be better positioned to respond to change, maximize ROI and empower leaders and users at every level of the enterprise.
The State of Enterprise Data
29%
The percentage of leaders who say that legacy system integration is the biggest challenge their organization faces in adopting agentic AI, more than any other factor
Source: deloitte.com, “AI trends 2025: Adoption barriers and updated predictions,” Sept. 15, 2025
67%
The percentage of leaders who say they feel pressure to implement AI quickly; 42% say they lack full confidence in the accuracy and relevance of their AI outputs
Source: salesforce.com, “Study: 84% of Technical Leaders Need Data Overhaul for AI Strategies to Succeed,” Nov. 4, 2025
70%
The percentage of data and analytics leaders who say that their most valuable business insights lie in data that is siloed, inaccessible or otherwise unusable
Source: deloitte.com, “AI trends 2025: Adoption barriers and updated predictions,” Sept. 15, 2025
The State of Enterprise Data
29%
The percentage of leaders who say that legacy system integration is the biggest challenge their organization faces in adopting agentic AI, more than any other factor
Source: deloitte.com, “AI trends 2025: Adoption barriers and updated predictions,” Sept. 15, 2025
67%
The percentage of leaders who say they feel pressure to implement AI quickly; 42% say they lack full confidence in the accuracy and relevance of their AI outputs
Source: salesforce.com, “Study: 84% of Technical Leaders Need Data Overhaul for AI Strategies to Succeed,” Nov. 4, 2025
70%
The percentage of data and analytics leaders who say that their most valuable business insights lie in data that is siloed, inaccessible or otherwise unusable
Source: deloitte.com, “AI trends 2025: Adoption barriers and updated predictions,” Sept. 15, 2025
- BUILDING A DATA STRATEGY
- ANALYTICS THAT DRIVE ACTION
- SCALING ACROSS THE ENTERPRISE
Before organizations can execute their data strategies, they must create the conditions for their initiatives to thrive. This includes ensuring data quality, governance and integration. It also means engaging executives in the effort, modernizing application architecture and automating other areas so that IT professionals can keep their focus on helping the organization implement a transformative data roadmap.
MODERNIZE ARCHITECTURE: Organizations will struggle to scale analytics and AI initiatives if their underlying architecture is built around fragmented legacy systems. Modern data architectures bring together structured and unstructured information across cloud and on-premises systems while embedding governance, metadata and security directly into the platform. Already, major data platform providers are evolving their offerings into AI platforms, delivering capabilities such as vector search and embedding generation natively. This means that many organizations can begin modernizing their architectures by extending and optimizing the technologies they already use.
ENSURE EXECUTIVE SPONSORSHIP: Without the support of leadership, data programs will be seen across the organization as “just another IT project.” Often, this means that momentum will stall as soon as challenges arise or budgets become tight. For data initiatives to succeed, they cannot be championed only by the organization’s chief data officer. Instead, they require vocal, visible support from leadership across lines of business, including operations, finance and sales. When these leaders treat data as a priority that affects their most important business goals, the rest of the organization will follow.
BREAK DOWN DATA SILOS: In many organizations, it can feel as though data is everywhere and nowhere at once. Although every application, business unit and even individual employee is constantly generating valuable data, this information is typically trapped inside disconnected silos. As a result, business and IT leaders lack visibility into the data stores that could help them take full advantage of emerging AI tools. Before a data program can achieve its full potential, organizations must create pipelines that connect and standardize data across the enterprise.
ESTABLISH DATA GOVERNANCE: Historically, governance has focused largely on restricting access to data, and it has often been layered on top of solutions after everything else is in place. But advanced AI and analytics solutions require governance practices that ensure data can be accessed immediately to provide instant answers to users. A well-governed, semantically rich data set becomes a reliable product that teams can build upon as they race to stand up AI systems. Essentially, organizations must stop treating data governance as a compliance checkbox and instead recognize that it is a prerequisite for AI success.
IMPROVE DATA QUALITY: Governance establishes rules and ownership, but organizations must also ensure that the underlying data is accurate, complete and up to date. Companies are pouring millions of dollars into new AI systems, but if these tools are built on top of faulty information, much of this investment will be wasted. Worse yet, business units might make critical decisions based on AI outputs generated with data that doesn’t actually reflect reality. Automated platforms can speed up the process of implementing data quality rules, making them one of the most effective ways to increase the return on AI investments.
Click Below To Continue Reading
How siloed is your data? Enterprises often manage data across dozens, or even hundreds, of disconnected platforms, limiting visibility.
Have you implemented modern business intelligence solutions? Many legacy BI tools were designed primarily for static reporting rather than real-time decision-making. As organizations embrace AI, these dated technologies can increase costs while slowing time to insight.
Does your team spend more time preparing data or analyzing it? According to industry estimates, analysts spend up to half or more of their time gathering and preparing data, leaving relatively little time for modeling and analysis.
Is your data clean and consistent? When records are incomplete or outdated, business users quickly lose confidence in analytics and AI outputs.
Collecting and integrating data is only the first step toward building a successful data strategy. To create real business value from analytics investments, leaders must also rethink how they ask questions, deliver insights and empower decision-makers across the enterprise.
BUSINESS-ALIGNED QUESTIONS: Too often, organizations start with technology. Rather than taking time to first identify the decision-making processes that they want to improve with analytics, they begin by gathering large volumes of data, building dashboards and experimenting with AI tools. The greatest successes, however, are almost always the result of focusing on business outcomes — such as supply chain efficiency, customer satisfaction, sales performance and employee productivity — and then reverse-engineering data systems and processes that will move the organization toward those goals. By framing analytics initiatives around strategic priorities, leaders will not only improve the effectiveness of their data programs but also build buy-in across the organization and encourage collaboration between business users and technical teams.
REAL-TIME VISIBILITY: In both business and daily life, the speed of information continues to accelerate, and employees expect to be able to access up-to-the-minute data not only at the office but around the clock on their mobile devices. Traditional analytics models often relied on reports or dashboards that were refreshed only periodically, leaving decision-makers reacting to events after they had already occurred and doing their best to apply historical lessons to future problems. Now, real-time data pipelines and automated analytics platforms make it possible to monitor operations continuously and respond more quickly to changing conditions. For example, companies can track supply chain disruptions, shifts in customer demand or operational anomalies as they happen, rather than waiting for scheduled reporting cycles.
AI-READY DATA: As business and IT leaders have seen over the past several years, simply rolling out AI tools is unlikely to bring benefits if the data that feeds into these tools is outdated, siloed and inconsistent. Organizations must ensure that their data is well-organized, governed and accessible across the enterprise so that AI systems can generate reliable outputs. This often means modernizing data architectures, improving metadata management and establishing clear ownership for critical data sets. Many organizations are also adopting “data product” approaches that package data sets, metadata, governance policies and logic into reusable assets for analytics platforms and AI applications. These structured data assets make it easier to deliver trusted information to both human decision-makers and automated systems.
SELF-SERVICE ANALYTICS: Increasingly, modern analytics platforms are designed to put insights directly into the hands of business users. In the past, many organizations largely relied on centralized data teams to build reports and dashboards. In theory, this reduces the burden on business teams; in practice, the workflow can create bottlenecks that slow down insights and cause important details to get lost in translation. Today, analytics and AI tools allow leaders across the organization to explore data independently and answer their own questions. Advances in natural language interfaces have accelerated this shift, enabling users to essentially have conversations with corporate information, rather than navigating inscrutable data dashboards. By empowering business users to explore data directly, organizations can reduce dependence on technical teams while dramatically increasing the speed of data-driven decision-making.
If data programs are going to lead to truly transformative benefits, analytics initiatives cannot be the domain of a small group of specialists. To capture the full value lying in their data stores, organizations must scale analytics capabilities across the enterprise and embed insight-generating workflows into all areas of the business. This requires organizations to build platforms that allow trusted data, governance policies and analytical tools to be shared consistently across departments. It also requires new ways of working. Data teams must collaborate closely with business leaders to ensure that analytics initiatives address real operational priorities rather than isolated technical objectives. Many organizations turn to a trusted partner such as CDW to help them map out their data strategies, architect modern data platforms and bring outside expertise to complex challenges including governance and AI enablement.
SUPPLEMENT IN-HOUSE EXPERTISE: Leaders often assume that large internal data teams are needed to stand up and scale analytics and AI solutions. However, hiring and retaining data specialists is a considerable challenge. Data engineering, governance, security, architecture and AI development each require distinct skill sets, and few organizations have deep expertise across all of these areas. Some organizations try to upskill existing IT professionals to support their data programs, but this is time-consuming and costly, and it can pull teams away from other priorities. CDW Data Strategy Services help organizations assess the current state of their data programs, identify capability gaps and develop actionable data roadmaps.
LEVERAGE STRATEGIC PARTNERSHIPS: A strategic partner such as CDW brings external expertise to data programs and offers organizations vendor-agnostic advice to help them evaluate and deploy solutions that fit with their specific goals and environment. CDW frequently stores organizations’ surplus hardware inventory at its warehouses to help them more effectively stage rollouts, and also provides a direct conduit to vendors that helps organizations navigate supply chain challenges. Programs including CDW’s Modern Data Platform Workshop help business and IT leaders modernize their data ecosystems, gain critical business insights and support better decision-making across the organization.
CONTINUOUSLY OPTIMIZE: A data program never enters its final form. Business priorities change, technologies evolve, and users generate more and new types of data. To keep up with shifting conditions and maintain a competitive edge, organizations must continually evaluate their analytics environments to make sure they remain aligned with operational needs. CDW offers services throughout the lifecycle of a data program, including regular health checks to reveal issues such as data quality gaps, advisory services to identify new opportunities and periodic reviews to ensure that organizations are taking advantage of new AI capabilities as they become available. By treating analytics environments as living systems that require continuous improvement, organizations can ensure that their data investments remain relevant and effective over time.
ACCELERATE TIME TO VALUE: Modern data strategies deliver measurable ROI faster than traditional approaches. Organizations that focus on data quality and governance early on will accelerate AI adoption and reduce long-term costs. However, organizations can only benefit from this speed if they have the expertise and resources needed to stand up their new data programs quickly, rather than spending years developing new data platforms. A partner such as CDW can help remove roadblocks, introduce automation tools that shrink deployment timelines and ensure smooth rollouts. Instead of building their data programs from scratch, IT and business leaders can adopt tested approaches that allow their internal teams to focus on generating insights and capturing value from analytics investments as quickly as possible.
- BUILDING A DATA STRATEGY
- ANALYTICS THAT DRIVE ACTION
- SCALING ACROSS THE ENTERPRISE
Before organizations can execute their data strategies, they must create the conditions for their initiatives to thrive. This includes ensuring data quality, governance and integration. It also means engaging executives in the effort, modernizing application architecture and automating other areas so that IT professionals can keep their focus on helping the organization implement a transformative data roadmap.
MODERNIZE ARCHITECTURE: Organizations will struggle to scale analytics and AI initiatives if their underlying architecture is built around fragmented legacy systems. Modern data architectures bring together structured and unstructured information across cloud and on-premises systems while embedding governance, metadata and security directly into the platform. Already, major data platform providers are evolving their offerings into AI platforms, delivering capabilities such as vector search and embedding generation natively. This means that many organizations can begin modernizing their architectures by extending and optimizing the technologies they already use.
ENSURE EXECUTIVE SPONSORSHIP: Without the support of leadership, data programs will be seen across the organization as “just another IT project.” Often, this means that momentum will stall as soon as challenges arise or budgets become tight. For data initiatives to succeed, they cannot be championed only by the organization’s chief data officer. Instead, they require vocal, visible support from leadership across lines of business, including operations, finance and sales. When these leaders treat data as a priority that affects their most important business goals, the rest of the organization will follow.
BREAK DOWN DATA SILOS: In many organizations, it can feel as though data is everywhere and nowhere at once. Although every application, business unit and even individual employee is constantly generating valuable data, this information is typically trapped inside disconnected silos. As a result, business and IT leaders lack visibility into the data stores that could help them take full advantage of emerging AI tools. Before a data program can achieve its full potential, organizations must create pipelines that connect and standardize data across the enterprise.
ESTABLISH DATA GOVERNANCE: Historically, governance has focused largely on restricting access to data, and it has often been layered on top of solutions after everything else is in place. But advanced AI and analytics solutions require governance practices that ensure data can be accessed immediately to provide instant answers to users. A well-governed, semantically rich data set becomes a reliable product that teams can build upon as they race to stand up AI systems. Essentially, organizations must stop treating data governance as a compliance checkbox and instead recognize that it is a prerequisite for AI success.
IMPROVE DATA QUALITY: Governance establishes rules and ownership, but organizations must also ensure that the underlying data is accurate, complete and up to date. Companies are pouring millions of dollars into new AI systems, but if these tools are built on top of faulty information, much of this investment will be wasted. Worse yet, business units might make critical decisions based on AI outputs generated with data that doesn’t actually reflect reality. Automated platforms can speed up the process of implementing data quality rules, making them one of the most effective ways to increase the return on AI investments.
Click Below To Continue Reading
How siloed is your data? Enterprises often manage data across dozens, or even hundreds, of disconnected platforms, limiting visibility.
Have you implemented modern business intelligence solutions? Many legacy BI tools were designed primarily for static reporting rather than real-time decision-making. As organizations embrace AI, these dated technologies can increase costs while slowing time to insight.
Does your team spend more time preparing data or analyzing it? According to industry estimates, analysts spend up to half or more of their time gathering and preparing data, leaving relatively little time for modeling and analysis.
Is your data clean and consistent? When records are incomplete or outdated, business users quickly lose confidence in analytics and AI outputs.
Collecting and integrating data is only the first step toward building a successful data strategy. To create real business value from analytics investments, leaders must also rethink how they ask questions, deliver insights and empower decision-makers across the enterprise.
BUSINESS-ALIGNED QUESTIONS: Too often, organizations start with technology. Rather than taking time to first identify the decision-making processes that they want to improve with analytics, they begin by gathering large volumes of data, building dashboards and experimenting with AI tools. The greatest successes, however, are almost always the result of focusing on business outcomes — such as supply chain efficiency, customer satisfaction, sales performance and employee productivity — and then reverse-engineering data systems and processes that will move the organization toward those goals. By framing analytics initiatives around strategic priorities, leaders will not only improve the effectiveness of their data programs but also build buy-in across the organization and encourage collaboration between business users and technical teams.
REAL-TIME VISIBILITY: In both business and daily life, the speed of information continues to accelerate, and employees expect to be able to access up-to-the-minute data not only at the office but around the clock on their mobile devices. Traditional analytics models often relied on reports or dashboards that were refreshed only periodically, leaving decision-makers reacting to events after they had already occurred and doing their best to apply historical lessons to future problems. Now, real-time data pipelines and automated analytics platforms make it possible to monitor operations continuously and respond more quickly to changing conditions. For example, companies can track supply chain disruptions, shifts in customer demand or operational anomalies as they happen, rather than waiting for scheduled reporting cycles.
AI-READY DATA: As business and IT leaders have seen over the past several years, simply rolling out AI tools is unlikely to bring benefits if the data that feeds into these tools is outdated, siloed and inconsistent. Organizations must ensure that their data is well-organized, governed and accessible across the enterprise so that AI systems can generate reliable outputs. This often means modernizing data architectures, improving metadata management and establishing clear ownership for critical data sets. Many organizations are also adopting “data product” approaches that package data sets, metadata, governance policies and logic into reusable assets for analytics platforms and AI applications. These structured data assets make it easier to deliver trusted information to both human decision-makers and automated systems.
SELF-SERVICE ANALYTICS: Increasingly, modern analytics platforms are designed to put insights directly into the hands of business users. In the past, many organizations largely relied on centralized data teams to build reports and dashboards. In theory, this reduces the burden on business teams; in practice, the workflow can create bottlenecks that slow down insights and cause important details to get lost in translation. Today, analytics and AI tools allow leaders across the organization to explore data independently and answer their own questions. Advances in natural language interfaces have accelerated this shift, enabling users to essentially have conversations with corporate information, rather than navigating inscrutable data dashboards. By empowering business users to explore data directly, organizations can reduce dependence on technical teams while dramatically increasing the speed of data-driven decision-making.
If data programs are going to lead to truly transformative benefits, analytics initiatives cannot be the domain of a small group of specialists. To capture the full value lying in their data stores, organizations must scale analytics capabilities across the enterprise and embed insight-generating workflows into all areas of the business. This requires organizations to build platforms that allow trusted data, governance policies and analytical tools to be shared consistently across departments. It also requires new ways of working. Data teams must collaborate closely with business leaders to ensure that analytics initiatives address real operational priorities rather than isolated technical objectives. Many organizations turn to a trusted partner such as CDW to help them map out their data strategies, architect modern data platforms and bring outside expertise to complex challenges including governance and AI enablement.
SUPPLEMENT IN-HOUSE EXPERTISE: Leaders often assume that large internal data teams are needed to stand up and scale analytics and AI solutions. However, hiring and retaining data specialists is a considerable challenge. Data engineering, governance, security, architecture and AI development each require distinct skill sets, and few organizations have deep expertise across all of these areas. Some organizations try to upskill existing IT professionals to support their data programs, but this is time-consuming and costly, and it can pull teams away from other priorities. CDW Data Strategy Services help organizations assess the current state of their data programs, identify capability gaps and develop actionable data roadmaps.
LEVERAGE STRATEGIC PARTNERSHIPS: A strategic partner such as CDW brings external expertise to data programs and offers organizations vendor-agnostic advice to help them evaluate and deploy solutions that fit with their specific goals and environment. CDW frequently stores organizations’ surplus hardware inventory at its warehouses to help them more effectively stage rollouts, and also provides a direct conduit to vendors that helps organizations navigate supply chain challenges. Programs including CDW’s Modern Data Platform Workshop help business and IT leaders modernize their data ecosystems, gain critical business insights and support better decision-making across the organization.
CONTINUOUSLY OPTIMIZE: A data program never enters its final form. Business priorities change, technologies evolve, and users generate more and new types of data. To keep up with shifting conditions and maintain a competitive edge, organizations must continually evaluate their analytics environments to make sure they remain aligned with operational needs. CDW offers services throughout the lifecycle of a data program, including regular health checks to reveal issues such as data quality gaps, advisory services to identify new opportunities and periodic reviews to ensure that organizations are taking advantage of new AI capabilities as they become available. By treating analytics environments as living systems that require continuous improvement, organizations can ensure that their data investments remain relevant and effective over time.
ACCELERATE TIME TO VALUE: Modern data strategies deliver measurable ROI faster than traditional approaches. Organizations that focus on data quality and governance early on will accelerate AI adoption and reduce long-term costs. However, organizations can only benefit from this speed if they have the expertise and resources needed to stand up their new data programs quickly, rather than spending years developing new data platforms. A partner such as CDW can help remove roadblocks, introduce automation tools that shrink deployment timelines and ensure smooth rollouts. Instead of building their data programs from scratch, IT and business leaders can adopt tested approaches that allow their internal teams to focus on generating insights and capturing value from analytics investments as quickly as possible.
Rex Washburn
Chief Architect and Head of Engineering – Data
