Tag Archives: data engineering

Composable Data Architecture: Build Flexible Systems

Why Monolithic Systems Drive Composable Architecture Adoption

Enterprise data infrastructure has historically been built on a predictable model for decades. They are centralized systems that were developed to store, process, and aggregate all information (data) together with an overall one architecture method. The original large-scale systems were designed for both stability and control.

However, the modern data environment has changed dramatically and now businesses are generating data at volumes much larger than ever before from many diverse sources – cloud platforms, customer applications, marketing tools, sales technologies, Internet of Things (IoT) devices, and other third-party intelligence platforms.

Legacy systems also have limitations when it comes to performance. Long deployment times for analytics (both new and modified) result in lack of timely data availability. Additionally, tightly coupled components make upgrading legacy systems a very risky endeavor. Lastly, scalability may become limited if the usage of each database increases at an unplanned rate.

The Business Impact of Architectural Rigidity

Data quality continues to be a major integrity problem for organizations; over 60% of the organizations surveyed indicated that data quality is their biggest challenge with respect to integrity, and many studies have indicated that companies may be losing on average 25% of all their revenue because of poor data. Gartner’s research on data quality economics confirms that poor data quality costs organizations an average of $12.9 million annually, with revenue impacts reaching 20-30% in data-intensive industries. However, in addition to data quality being an issue, repairing data quality issues within a monolithic system is extremely difficult.

With a monolithic data platform, each question from the business feels like a complete re-platforming project. The time to produce a new data set (from when the question is first asked until that data set can be delivered for use) takes weeks or months. New projects require new code instead of being able to reuse existing components.

Monolithic platforms also cause paralysis in an organization’s ability to act. Data teams are bottlenecks while business stakeholders wait months for even simple enhancements to analytics. Strategic opportunities can be lost while IT attempts to figure out how to work around architectural limitations.

Gartner and McKinsey have conducted research that indicates that organizations that modernize their data infrastructure are generally able to innovate faster, operate more efficiently, and make decisions with more agility than their less modernized peers.

What Is Composable Data Architecture? (Definition & Principles)

Composable data architecture breaks away from the traditional centralized methods of storing and collecting data using one large integrated platform, to a new way of functioning by creating an architecture (data systems) using modular components (individually developed and deployed) that can be replaced as needed.

It treats the data platform as a series of small independent capabilities that come together as a built item using modularized building blocks and provides for developers to be able to develop and should be easier to scale than traditional systems, which must be manually integrated.

The defining concept for Composable Data Architecture is to allow companies to build reusable components that the company’s development teams can assemble and reassemble together, based on the company’s requirements. This is similar to how the software industry transitioned from monolithic applications to microservices.

Core Principles

Composable data architecture is built on four core principles that distinguish it from traditional systems.

Modularity – The parts of the system can work alone but fit into one whole very nicely.

Inter-operability – All systems can communicate with each other using the same method (i.e., API).

Scalability – The parts of the system can expand according to the demand placed on them.

Flexibility – An organization can change an individual service within a system without the whole system being affected.

Each of these traits provides a distinct difference between composable architecture and traditional architecture. The components remain loosely coupled with one another via a defined API and contract. Each of the domain teams is responsible for their data product, including the documentation, quality checks, and access control.

Traditional vs Composable Data Architecture: Key Differences

Traditional enterprise data architecture has a centralized nature to it, like a monolithic data warehouse, with tightly coupled systems and long implementation cycles. Composable models provide modularity of service, with API enabled integration and a fast cycle to deploy. A composable approach allows organizations to create the best solution that meets specific requirements, whereas traditional architecture typically requires everything to be released at once due to tight coupling resulting in the need to coordinate changes.

Composable environments work to eliminate the dependencies between components of an overall architecture within an organization, pushing responsibility for assuring quality back to the teams developing the components of an architecture.

Key Building Blocks of Composable Data Architecture

A composable ecosystem relies on several foundational technologies.

Microservices

Separate services conduct particular functions on a data set in the computation and processing layer, such as ingesting new data, transforming it, validating it, enriching it with external information, or delivering it to an end user (activation). Each developer designs the services to work independently of one another, and each service will have specific, clearly defined inputs and outputs.

The team designs the data in such a way, that it will be delivered through microservices (i.e., small, specific services that own and provide a specific capability or function). Each microservice will maintain its own data store and establish database technology to meet its needs.

A team can update, change, or scale a single service without affecting the overall system. Rather than rebuilding entire pipelines, organizations compose new capabilities from existing services. A marketing team needing real-time customer segmentation assembles ingestion, enrichment, and activation services without custom development.

APIs as the Connective Tissue

APIs are the universal language of composable architecture. Every component exposes its capabilities through APIs, enabling discovery, orchestration, and decoupling.

Well-designed API layers expose data and functionality through standardized interfaces multiple consumers access regardless of underlying implementation details. Research indicates the platform segment held the largest share of 73% in 2024, propelled by growing usage of cloud applications fueling demand for APIs and microservices.

B2B companies rely on APIs to move data around. These platforms share firmographic details, tech usage patterns, and signs of buyer interest. Marketing tools, customer databases, and analytics programs use those API feeds. The data helps teams make smarter decisions. Information moves directly between systems without extra steps. This setup keeps operations running smoothly. Each piece of data has clear value in real time. Platforms update constantly to reflect current behaviors. Teams can act fast when signals change. The flow supports day-to-day marketing workloads. It also improves how sales and marketing align. All information stays accessible across tools. That consistency helps avoid missteps. Data stays up to date through continuous updates. Teams see buyer movements instantly. Tools automatically pull new data as it appears. This keeps the workflow moving efficiently.

Cloud-Native Platforms

Modern composable systems operate on cloud-based platforms offering scalable resources, managed functions, and worldwide access. Instead of setting aside set levels of capacity initially, these platforms adjust resource amounts depending on real-time needs.

Gartner reports that by 2025, about 51% of IT budget will move to public cloud services. Cloud-native setups offer adaptability that traditional on-site systems fail to provide. This benefit stands out for tasks managing changing workloads. For now, this flexibility is at least in theory a strong advantage.

Modular Analytics Layers

Analytics capabilities are themselves modular. Organizations combine query engines, transformation tools, orchestration to coordinate workflows, and visualization through multiple BI tools serving different user communities.

The analytics layer enables domain teams to compose components into data marts, feature stores, and APIs supporting specific decisions. Organizations do not rely on a single analytics system. They create separate analytical tools for specific tasks. Many platforms now have built-in analytics. Data teams share insights within other apps. This lets analytics move past basic dashboards into daily operations. Decisions happen where they are needed most. These tools work best in real-world settings.

Data Contracts and Governance

As components multiply, consistency becomes critical. Data contracts, declarative specifications defining schema, quality rules, and access policies, ensure all components interpret data the same way. Contracts are embedded in pipelines, enabling automated validation.

Enterprise Advantages

Organizations successfully implementing composable data architecture realize transformative benefits traditional systems cannot deliver.

Scalability

Composable architectures are scalable in both technical and organizational aspects. On the one hand, cloud-native infrastructure and modular components achieve technical scalability by allowing independent expansion. For example, when data processing needs increase, teams only need to scale the particular components involved rather than the whole platform.

Organizational scalability proves equally important. More companies are embracing decentralized models where domain teams own their data products. Product, marketing, and operations teams now own and manage their trusted data assets rather than relying on centralized data teams for every request.

Agility

Business moves quicker when pieces fit together like blocks. Instead of starting over, groups adjust what they already have. As demands change, so do the parts – no need to begin again. Speed comes from swapping pieces, not rewriting everything.

Packed Data Services thrives in composable architecture. Firmographic APIs plug into data warehouses for ICP analytics. Intent streams flow to CRM systems. Composability unlocks B2B velocity.

Cost Efficiency

Composable architectures cut costs in multiple ways. Firstly, the system charges businesses solely for the resources that they utilize. Secondly, by choosing a combination of different vendors’ products, teams can select the most excellent solutions for their unique requirements without tying themselves to any one vendor. Thirdly, code reuse benefits time-to-market reduction for the new functionalities.

Building Your Composable Architecture

Transitioning from a monolithic setup to a composable one is a big step that needs good strategy.

First of all, you should have truly clear business goals in mind. Work out exactly what problems your existing setup is causing. Set the criteria for success based on business results rather than technical capabilities. Take it step by step. Pick a few small instances where composability is clearly providing value.

Demonstrate that the approach is effective before making it a wider practice. Besides that, it is important to develop the necessary skills progressively.

Invest in governance. Establish data contracts early. Define ownership models. Create discovery mechanisms so teams find and reuse existing components.

The vendors who win will not be those with the biggest platforms. They will be those who understand flexibility drives competitive advantage and design data ecosystems accordingly.

The Data Talent Gap: Why Tools Alone Can’t Fix Your Data Strategy

The Real Skills Missing in Data Teams

When thinking about creating a data team, many people think of data scientists or data analysts; however, that is only part of the team you will need to create a truly balanced, fully functioning team.

Most organizations will have several key roles missing in their teams; however, there are some skill sets (i.e., data engineering) that are highly undervalued. Data engineers build pipelines, design storage schemas, and optimize queries to ensure high-quality, high-quantity data performance. Without data engineering, data scientists waste most of their time cleaning data instead of building models.

Analytics experts serve as the bridge between engineering output and business user-level implementation. Their responsibilities include designing dashboards, producing reports, and answering ad hoc questions raised by stakeholders. High-quality analytics requires a rare blend of statistical rigor and the communication skills needed to interpret data for stakeholders. Generally, a given applicant tends to be strong in one of these areas but not in both.

Data scientists use statistics and machine learning to predict churn, optimize pricing, and identify potential fraud. The position requires an unusual mix of skills from mathematics, computer programming, domain experience, and an understanding of business. Finding someone with this combination of skills can be quite challenging.

Closing the data talent gap means filling all of these roles, not just the ones with the most glamour.

Bridging the Gap: Translators, Domain Experts, and Governance

Nevertheless, the business translator is the rarest and most valuable member of any data team. This individual links technical teams to stakeholders by framing data-driven problems and connecting insights to day-to-day business operations. They have enough technical fluency to understand what’s possible and enough business context to identify what actually matters. Organizations that have strong translators in place reportedly generate five to ten times more value from their data investments than those that leave it to technical teams to figure out business relevance on their own.

Domain experts are equally important, even if they’re less glamorous. A healthcare data analyst who genuinely understands clinical workflows, reimbursement structures, and regulatory requirements operates in a completely different league than a generalist trying to piece it together from documentation.

And then there’s governance and architecture expertise; increasingly, this is a strategic function, not just a compliance checkbox. As regulations get tighter and data ecosystems get more complex, organizations that don’t invest here are setting themselves up for painful problems down the road.

How the Data Talent Gap Creates Costly Team Failures

Even well-funded data programs can stall out because of structural mistakes that have nothing to do with the tools they’re using.

One of the most frequent failures is leaning on IT too heavily. IT departments have traditionally been organized to provide stability, security, and control costs. However, data projects require trials, quick iterations, and a direct link to business results. Reporting through IT buries analytics prototypes in 99.9% uptime processes, preventing the speed needed to learn from failure.

Isolated data scientists producing work that nobody uses is another recurring pattern. Companies bring in skilled data scientists, give them data access, and expect the insights to come naturally. Without business engagement and clear deployment paths, data scientists risk building complex solutions for problems that don’t exist. Studies show that 87% of data science projects never make it to production and that’s rarely because the science was bad. It’s usually because of organizational disconnect.

Fragmented, siloed efforts compound the problem. Marketing builds its own customer analytics. Sales runs its own forecasting. Finance develops its own reporting. All teams replicate their infrastructure independently and also have independent definitions. Therefore, they create reporting that cannot be matched to anything anyone else is creating. This creates “three versions of the truth,” where different teams produce conflicting answers to the same business question.

Closing the Data Talent Gap: Build, Buy, or Partner?

Deciding how to close the data talent gap, whether to build, buy, or partner, isn’t about finding one right answer. It’s about making strategic choices that align with your organization’s capabilities and long-term goals.

One of the advantages of building a team internally is that you get to have deep institutional knowledge as well as real cultural alignment. The disadvantage is that it’s costly and time-consuming. It takes an average of 51 days to recruit data talent: around 10 days longer than the average for the broad labor market. Moreover, with median annual salaries for data scientists going over $1.12M forming a full, fledged team is a major budgetary commitment.

Technology can speed up some things, but it is unable to cover skill shortages. Companies that fail to invest in talent often end up with advanced technology but no one who can use it efficiently.

Leveraging External Expertise and Hybrid Models

Partnering with a data firm provides expert knowledge, established frameworks, and scalability without the need for internal development. A way an organization can get access to specific knowledge that would take them several years to develop in-house is through account intelligence, intention data and CRM enrichment solutions.

Packed Data Services provides firmographic and technographic data with automated scoring, reducing the technical workload for sales and marketing teams.

Typically, the most effective way of getting business value out of data is to use some mix of these three. A hybrid approach uses a small internal team for proprietary models while leveraging partners for foundational layers like contact enrichment and lead scoring.

Designing a Data Team That Bridges the Talent Gap

Organizations that are doing it best have a single thing in common: they consider data as a product rather than a support function. That implies having a Chief Data Officer responsible for the strategic direction and ensuring that data initiatives remain linked to business outcomes. Data engineers that work on infrastructure and pipelines. Analytics translators who keep the work grounded in real business needs. Governance leads those who maintain quality and compliance.

The hub-and-spoke idea is a go-to structure for a reason. A central data platform team takes care of the shared infrastructure, governance, and technical standards. Embedded analytics practitioners are placed in the business units; thus they are close to business needs and at the same time they can get help from the central team. It’s a structure that prevents fragmentation without losing relevance.

Increasingly, leading teams are going further, treating datasets as actual products, with defined users, clear requirements, and lifecycle management. Rather than organizing around technologies, teams own business domains: customer data, product data, financial data. Each team is accountable for quality, documentation, and the access interfaces that downstream teams rely on.

Beyond Hiring: A Lasting Data Talent Gap Strategy

Sustainable data transformation doesn’t stop with hiring.

Data literacy programs for non-technical staff might actually be one of the highest-leverage investments an organization can make. When business teams actually understand data and when they can read a dashboard critically, ask sharper questions, and push back on weak analysis, the demand for insights gets better. More specific and more actionable. Organizations that run serious upskilling programs have reported 40 percent reductions in the backlog of requests coming into their data teams.

Culture matters too, and culture follows incentives. Acknowledging individuals for data-informed decisions encourages them to naturally strengthen that practice. When sales compensation includes data quality metrics tied to CRM inputs, accuracy levels inevitably rise. Furthermore, evaluating product managers on the rigor of their A/B testing instills a much deeper discipline around experimentation.

You get what you measure.

In the end, the whole thing hinges on data efforts being clearly connected to the business outcomes, revenue growth, customer retention, operational efficiency. When people are able to trace their data work back to things the business really cares about, that’s the point when real transformation happens.

Technology will keep moving fast. Talent and organizational structures move slower but they’re what actually determine whether the technology investment pays off. Organizations that seriously close the data talent gap don’t just get better analytics, they receive quicker decisions, clearer competitive insights, and the type of strategic clarity that cannot be easily valued.

Tools make change possible. However, it is people who ultimately bring about it. Creating companies that integrate top tech skills with true business understanding, and data, driven strategy will no longer be a promise but rather a reality.