White Paper
12 min

How the Modern Data Platform Fuels Success

A secure, cloud-based solution can provide nearly unlimited flexibility and help users throughout an organization make better decisions.


Data Is Fueling Digital Transformation

Organizations across industries have embraced digital transformation in recent years, leveraging technology to improve processes and systems, reduce costs, develop new products, drive growth, improve the customer experience, and ultimately gain an edge on their competitors.

In a way, data is both the cause and the effect of digital transformation. Any effective digital transformation effort will rely on internal data to help business stakeholders achieve new insights. In fact, this is the “transformation” part of digital transformation — a term that should be reserved for technology use cases that fundamentally change how an organization operates and delivers value to its customers. At the same time, new digital workflows inevitably produce additional data, creating even more opportunities for organizations to optimize their operations.

These opportunities come with pressure for organizations to keep pace with ndustry competitors by continuously using data in new and creative ways that lead to tangible improvements. According to a 2022 report from Gartner, business leaders believe future decisions will become more and more complex, increasing the demand for data and analytics. “Connections between diverse and distributed data and people create truly impactful insight and innovation,” the report notes. “These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy and contextualized decisions while taking an increasing number of factors, stakeholders and data sources into account.”

According to a McKinsey report, only 8 percent of organizations truly “break away from the pack” when it comes to analytics, while the rest struggle to capture real value from their efforts. The problem is that most companies are content to eke out small gains from a few limited use cases rather than adopting analytics across the organization. By contrast, breakaway performers align their analytics efforts to strategy; embed analytics into processes and decision-making; and build a solid foundation of data, technologies and people to help them achieve their goals.

Key Objectives for Data Analytics

Getting data analytics right can give organizations an advantage over competitors by improving the customer experience, enhancing efficiency and enabling predictive capabilities.

Customer Experience

By collecting and analyzing data on customer behavior, companies can create more personalized experiences, including targeted marketing efforts, useful product recommendations and even customized pricing strategies.

Operational Efficiency

Data analytics can help organization leaders identify inefficiencies and opportunities for optimization. This can yield benefits including streamlined operations, cost reductions and improved time to market.

Predictive Modeling

When organizations run historical data through predictive analytics tools, they can make forecasts about the future that help to improve practices in areas such as sales and inventory management.

The Power and Importance of Data Analytics


The percentage of companies that expected to increase their investment in data and analytics capabilities in 2022


The percentage of companies that report they are delivering business value from data investments


The percentage of companies that have appointed a chief data officer or chief data analytics officer


The percentage of companies that report they are driving business innovation with data

Data Analytics Challenges

Many organizations now consider themselves data organizations. But really, only those that are effective at leveraging their data can truly lay claim to this distinction. While this sounds simple, numerous challenges keep companies from using their internal data in ways that achieve new insights, unlock efficiencies and ultimately create value for the business.


Storing large quantities of data may require organizations to expand their data center infrastructure or scale up resources in another storage environment, such as a public or private cloud. Depending on the business, it may make sense to invest in separate tiers for different use cases. For instance, archival data stored for compliance reasons may be housed on less expensive infrastructure.


Integrating data from multiple sources is a notoriously vexing challenge. Silos prevent organizations from achieving a unified picture of their data, and they can also lead to data duplication and inconsistencies. Breaking down these silos requires investments in integration and management tools, as well as a cultural emphasis on data collaboration.


Data is being generated at an unprecedented pace, and many organizations struggle simply to keep up. This data overload can overwhelm existing systems, making it difficult for business leaders to effectively analyze information to improve decision-making. In fact, organizations with poor data management capabilities may not even know what data they have.


Integrating technology resources and data environments can be one of the biggest wins from a merger or acquisition, but it can also be an enormous challenge. Merging organizations bring different IT systems and processes with them. To successfully integrate disparate environments, stakeholders must conduct thorough inventories and create strategic roadmaps for unifying data.


In part due to security concerns and siloed systems, nontechnical business users often find it challenging to access the data they need to improve operations. In some instances, these users may not even know what data exists within their organizations. In others, they must rely on IT professionals to provide them with reports, which delays decision-making.


Companies seeking to scale up their data analytics efforts often struggle to overcome a shortage of talent with the necessary skills and expertise. The field of data analytics is rapidly evolving, and demand for skilled professionals is high. Talent gaps can lead to delays in new analytics initiatives and underutilization of existing capabilities.


It’s one thing for an organization to store vast quantities of data; it’s another for business and IT leaders to have confidence in the quality and integrity of this information. Too often, data is incomplete, inconsistent or outdated, potentially leading to errors in analysis and missed opportunities to identify important insights.


The more data an organization stores, the more it must protect. Cyberattacks have grown increasingly sophisticated, and ransomware continues to pose an enormous risk. Additionally, organizations must comply with an ever-evolving regulatory environment, with enterprises in sensitive sectors such as finance and healthcare facing additional scrutiny.

Data and Analytics Challenges: By the Numbers


The percentage of chief data officers who say establishing clear data governance responsibilities across the organization is among their top responsibilities1


The percentage of CDOs who cite difficulty in changing organizational behaviors or attitudes among their top data-oriented challenges1


The percentage of CDOs who list a lack of data literacy as one of their top data-oriented challenges1


The percentage of IT decision-makers and influencers who say they face difficulties loading data into analytics platforms2

Sources: 1mitsloan.mit.edu, “Survey Details Data Officers’ Priorities, Challenges For 2023,” Feb. 21, 2023; 2IDC, “Scaling AI/ML Initiatives: The Critical Role of Data,” February 2022

Learn how a modern data platform can unleash business insights.

Meet the Modern Data Platform

Data-driven decisions require the ability to analyze data quickly and reliably. Organizations need data platforms that don’t slow them down. Many business and IT leaders have found that traditional data systems are rigid, costly, complex and ineffective, with disparate technologies stitched together in a way that creates multiple potential points of failure. By contrast, a modern data platform enables organizations to handle the volume, velocity and variety of data necessary for organizations to get ahead of their competitors.

A modern data platform should have several essential characteristics.


Elasticity, scalability and modularity are core features of modern data platforms. These solutions can not only leverage major public cloud hyperscalers but will also likely utilize Software as a Service platforms. Unlike traditional platforms built with scarce and costly hardware resources, cloud data solutions offer virtually unlimited capacity for organizations to store and process their data. This, in turn, provides the ability to run numerous workloads and analytics queries in a central platform without the need to purchase additional physical hardware to accommodate periods of peak demand. As organizations scale out their data environments in the cloud, technology teams must simultaneously assess the technology they have in-house, address any outstanding technical debt and evaluate the skill sets of IT staffers. Many organizations require external consultants and technical experts in order to have these conversations effectively.


If a solution doesn’t feature data governance, then it isn’t a modern data platform. Governance is a cornerstone of the modern data platform, and part of what sets it apart from traditional data architectures. In legacy data systems, governance, security, data duplication and silo issues are common. Ultimately, these legacy systems sometimes require IT leaders to spend more time managing their infrastructure than actually working with data to achieve transformational insights. By contrast, the modern data platform simplifies and unifies data environments, leading to more streamlined governance and security. With a modern data platform, custom access controls can be set with the flip of a switch. Improved governance and security practices not only help organizations to comply with data safety and privacy regulations and protect their environments from compromise, they also increase trust in data among users, making them more likely to leverage internal data in their own work.


This is sometimes called “democratization” of data. Less important than the specific term, though, is the concept: Organizations must make data available for analysis across various lines of business in ways that allow users to take action. Too often, business leaders think of innovation as something that is limited to product development or operations teams. But when they’re given the right data — and a modern data platform that allows them to access, trust and analyze information — users across an organization are empowered to innovate. Within organizations that promote a culture of data for everyone, stakeholders in areas such as legal, human resources and accounts payable may uncover insights that unlock new efficiencies or lead to new revenue streams. A modern data platform delivers the foundation for capabilities such as artificial intelligence and machine learning to everyone who needs them, helping to fuel transformation.

Addressing Data Compliance

As organizations seek to improve their data analytics efforts, they should make compliance one of their top considerations.

Depending on sector and geography, organizations may be subject to data safety regulations including HIPAA, the General Data Protection Regulation and payment card industry regulations.

A unified presentation of data is essential for regulatory compliance. A modern data platform can help ensure that compliance officers know where data is located throughout an organization by making data governance a cornerstone of its deployment.

Organizations need simplified reporting mechanisms. Reliable reporting ensures that data compliance is a matter of following predictable routines, rather than a mad scramble to meet audit deadlines.

Business leaders should remember that compliance and security are separate. Even if an organization is in full compliance with regulations, it could still be vulnerable to cyberattacks.

A Data Practice Set for Success

Even a tool as powerful as a modern data platform can be only as effective as the people who are using it. To get the most out of their data and analytics efforts, organizations must adopt a strategic set of practices for implementation, utilization and improvement over time.

An effective approach to the modern data platform incorporates four important steps.

The most effective analytics efforts begin not with technology but with specific goals for business outcomes. Rather than issuing broad mandates about the use of data analytics, organizations should seek out high-value use cases and quick wins that illustrate the effectiveness of a modern data platform and help to build momentum. Project leaders should also identify business sponsors and champions throughout the organization. A modern data platform is unlikely to have a substantial impact if only IT leaders are advocating for its use. But when executives and managers from business units such as sales join the effort, it quickly can become transformational.

Virtually all organizations already have sources of data that they could leverage more effectively. As they consider their options with a modern data platform, IT and business leaders should undertake an honest assessment of existing systems and challenges. Ideally, this effort will result in legacy data being integrated seamlessly with new data sources. Governance should be a cornerstone consideration during this assessment process. Organizations should seek to create robust data catalogs with comprehensive metadata that enables even nontechnical users to find the information they need with speed and ease. The goal at this stage should be modernization, not merely migration.

Implementing a modern data platform isn’t as simple as purchasing an off-the-shelf, ready-made product and flipping a switch. Rather, organizations must follow the familiar “people, process, technology” framework to achieve true success. During both this stage and the assessment stage, many organizations rely on a trusted third-party partner with cross-industry expertise to make vendor-neutral recommendations that meet their unique needs. At the system’s architecture level, a modern data platform must be designed with a cloud-friendly mindset that embraces modularity. Rather than locking themselves into one vendor’s ecosystem, organizations should seek to build a modern data platform with a mix of best-of-breed solutions.

Business and IT leaders must understand that a modern data platform is not a destination but rather a journey that relies on continuous improvement. This means that organizations must engineer their solutions to support evolution over time and work to embed a culture that promotes experimentation and rewards creativity. The fields of data science, artificial intelligence and machine learning are evolving rapidly, and the analytics use cases that will create the greatest value for an organization in the future may not even exist yet. This is why it is so important for organizations to ensure that they have the data, systems and processes in place to meet the moment when new opportunities arise.

Key CDW Services

Two key services from CDW can help organizations get started on their modern data platform journeys.

This is an interactive workshop aimed at helping organizations take the first steps in modernizing their data ecosystems. CDW’s data and analytics experts guide your technical teams through defining the goals and scope of their anticipated modern data platform, conduct a high-level architecture review, design systems, and ultimately deploy the data platform. The workshop enables organizations to leverage best-of-breed technologies and practices and to align with business stakeholders on a common vision for innovation.

During a Data Modernization Roadmap Workshop, CDW’s experts will identify an organization’s specific business and compliance needs and make customized recommendations. This is especially important for organizations operating in highly regulated industries, such as healthcare, finance and education. Working alongside line-of-business sponsors who are eager to get data into the hands of users to transform the business, CDW’s experts make customized recommendations for how an organization can best leverage its modern data platform. Before the engagement ends, participating organizations will have a multiyear roadmap in hand with achievable milestones and specific details.

Story by

Jesus Diaz, who has 20 years of experience in technology as an entrepreneur, thought leader and solutions architect. He is an advocate for automation, cloud technologies, analytics and machine learning. 

Christopher Marcolis, a data and analytics expert with 25+ years in analytics, data governance, data science and strategic decision-making. He is skilled in nurturing data-driven cultures, optimizing analytics and empowering teams for growth.

Rex Washburn, who has worked with organizations across industries leading, designing and implementing data platforms. He is head of modern data platforms for the CDW data practice.