In the modern enterprise, data is both a product and a process stream that is created by and flows through the tech stack. There are hundreds of tools and technologies making up the tech stack across applications, development tools, APIs, databases, and infrastructure in a hybrid multi-cloud environment.
Research across 15 high-growth economies and 19 industries shows an average of 72% of companies are using more than 500 enterprise applications across their organization, according to an Accenture interoperability study. That only accounts for a portion of the average tech stack in a large enterprise, which continues to grow and change.
This growth has led to most companies ending up with a massive “tech pile” where the lack of integration and data interoperability make productivity, collaboration, and innovation nearly impossible.
To innovate, maximize productivity, and meet customer demands, enterprises must build systems, pipelines, and software to drive projects and products. They also need the applications, databases, and infrastructure that enable collaboration and productivity to support those products and projects. When, how, and where an enterprise’s workforce uses different aspects of the tech stack is always an unending tangle of interconnected and separate needs and outcomes.
This quickly becomes a pile of incompatibilities, obscurities, inconsistencies, redundancies, and gaps where tech stack silos block data exchange and access in ways and places that enterprise data users cannot see or untangle.
The idea of a single source of data truth becomes obscured in a tech pile without the ability to access and redirect the right data for specific groups, tasks, projects, and workflows. Being able to do so starts with understanding the challenges posed by tech piles and data interoperability.
The rise of hybrid, multicloud, and cloud-native architectures—along with IoT integration and data-driven decision-making—has caused tech stacks to swell into what some experts now term “tech piles.” This explosion of disparate tools and systems leads to fragmented data environments, which significantly hinder operational efficiency and agility.
Developers and IT teams waste a lot of time managing and configuring these environments rather than focusing on innovation. This lack of cross-functional collaboration and data interoperability leads to many hurdles, including the following.
These practices must come before a technology is chosen. Too many organizations reverse this order and decide upon costly data technologies without defined practices in place.
Defining practices first sets the foundation for a well-functioning organization where data becomes a truly agile resource for innovation. This foundation enables sound data technology choices that can continuously adapt to changing needs.
Structured and unstructured data is often fragmented across legacy systems, hybrid cloud environments, departments, and a host of geographically disparate locations. Different data formats, data lakes, warehouses, third-party stores, and overarching regulatory requirements all prevent enterprises from unifying data and creating a single source of truth. Breaking down data silos is a critical first step toward achieving this unity.
Many enterprises, particularly in sectors like BFSI or utilities, still rely on outdated systems, creating bottlenecks in real-time data exchange. Integrating these with modern platforms is difficult and costly.
A lack of modularity and reusability within tech stacks makes data integration and interoperability complex.
Without a centralized data governance framework, enterprises cannot standardize data practices, which leads to data duplication, errors, and inconsistency.
The lack of a data interoperability process within enterprise tech piles leads to millions lost in operational productivity, efficiency, innovation, and overall business growth. Fragmented tech stacks result in longer, inefficient, and error-prone development cycles and increased labor costs.
In the BFSI sector, legacy systems hinder the integration of real-time customer data across departments and countries. This poses a hurdle to streamlining regulatory compliance/auditing as well as improving product innovation, customer experience (CX), and revenue growth.
When it comes to utility companies, they are flooded with IoT data from sensors and smart meters; however, interoperability challenges slow down the real-time integration required for achieving operational efficiency.
Lastly, in manufacturing, complex supply chains require real-time data integration from numerous systems for seamless operations.
While this is only a sample of the industries with specific interoperability challenges, they all can take advantage of strategies similar to those presented below to achieve interoperability.
Every enterprise understands that data is both the fuel and the product of its tech stack, which requires interoperability and composability. However, it has become a herculean task to implement interoperability protocols across and between the different aspects of this stack, which leads to it becoming a tech pile.
Removing data silos created by these separate layers in a way that delivers a unified source of data truth is only part of the answer to interoperability. Enterprises must combine disparate data while enabling and maintaining real-time:
This can be driven by composability (the combination of different tools and technologies to create unique solutions), cloud-native architectures, automation/AI, and the management/governance of data and metadata. Companies must also modernize their legacy systems and integrate them with newer platforms, as both measures are critical to interoperability.
Strategies for achieving true interoperability require a mix of both technical and business strategies that work together holistically.
The following are just a few of the most common technical solutions that enable broader interoperability. The strong connection between them is why they are often foundational to enterprise digital transformations to achieve a litany of business outcomes.
Transitioning to cloud-native architectures, such as microservices and containerized solutions, gives enterprises the flexibility and scalability they require for managing large volumes of data.
Modernizing legacy systems through APIs and microservices enables enterprises to use current data without replacing their entire infrastructure. These architectures allow for the agile deployment of interoperable data components, significantly reducing downtime and improving real-time access to data.
Relying on open APIs and vendor-neutral standard/common data formats enables enterprises to remain technology-agnostic, which, in turn, allows them to:
This also applies to achieving semantic interoperability, where different systems interpret data consistently across departments, countries, systems, platforms, and vendors.
When it comes to data management and open source, there are numerous tools and platforms commonly used separately and in tandem to accomplish different goals. Just some of the open-source data tools and platforms include:
All of these have open-source alternatives for proprietary, high-performance, integration, complexity, language, or other requirements. Different combinations can be found within a single enterprise’s technology stack, making the need for a single data management platform (for abstraction and visibility) even more imperative. A big data interoperability framework can help orchestrate these disparate tools and establish common procedures.
Many vendor-neutral standards come with built-in ontologies or metadata schemas that support a contextual understanding of data. Composable architecture also supports interoperability by using an API to communicate between modular components for seamless data exchange.
Automating data management through CI/CD processes helps ensure that development teams build in interoperability from the start, minimizing the chance of misalignment between systems.
AI-enhanced automation bolsters interoperability in the enterprise tech stack by streamlining data flow, ensuring consistency, and reducing the need for manual intervention. These tools must work across—not just within—tools and systems to drive:
Automation minimizes human error in repetitive or complex tasks while helping enforce standardization, early error detection, and remediation.
Enterprises should focus on a data interoperability process that aligns their data and metadata lifecycles so systems can communicate and move data efficiently. This process includes ensuring compatibility via the use of open standards for data formats and APIs.
Companies should also go beyond using CI/CD processes for metadata management to further automate data integration, improve consistency, and minimize errors. The goal is to align data and metadata lifecycles via metadata as code for improved data governance.
The business solutions for achieving interoperability work holistically with the technical solutions in obvious ways. Below, we cover just some of the options available.
Interoperability is a fundamental part of every long-term digital transformation roadmap because it is fundamental to future-proof enterprise tech stacks and cross-platform data flow. This can be seen in the interconnection between:
These all rely on interoperability built on data and data projects supporting collaboration, operational and workflow efficiencies, innovation, business planning, CX, and much more. This foundation of a business-driven data architecture is what makes every transformation capable of delivering defined outcomes across the enterprise.
A digital transformation roadmap is defined by business-driven data architecture, which enables an enterprise to build roads to every business outcome destination. This is the only way true interoperability can be achieved in a massive and complex tech stack.
This perspective enables a data-first culture, resulting in true collaboration between the C-suite and a host of data stakeholders. They will need to use standardized data governance frameworks and automation to:
The goal is to provide data consumers across the enterprise with unlimited options for data projects. These projects will need access to data sets that are highly related to project needs to deliver better insights and decisions for business innovation, operations, and growth with minimal risk.
Enforcing robust data governance and security is critical to ensure the quality, compliance, and risk management of data, which makes interoperability possible across the entire tech stack. To safeguard their sensitive data, enterprises must go beyond traditional techniques and implement:
These are a few of the many ways that interoperable data and metadata play a critical role in governance, security, compliance, and risk management, but there are others such as:
These governance aspects combine in ways that enable an enterprise to assess the impact of data sharing on security, compliance, and risk management.
Most hybrid cloud/multi-cloud systems, platforms, tools, and environments lack the robust integration and level of metadata management required to foster true interoperability. The need for continuous metadata and semantic interoperability can be difficult in a tech pile. This is true even with the use of APIs, DevOps, CI/CD, containerization, microservices, orchestration, and a host of other automation-driven agile approaches.
Interoperability and data governance present more pronounced challenges with emerging technologies like automation/AI, IoT, and edge computing. All these technologies, platforms, and methodologies rely on each other to achieve business outcomes. And while they enable differing levels of interoperability within their respective tech stack systems, what they lack is a way to manage and govern all data and metadata across the enterprise to achieve unified and collective interoperability across the entire tech stack.
The challenges of interoperability are clear when enterprise respondents say they:
This was according to the “EY Reimagining Industry Futures Study 2024” survey of 1,405 enterprises.
Even automation within different parts of the tech stack has difficulty tracking and updating metadata. This is because these systems and tools lack clear and organized approaches to data/metadata sharing, cleaning, and updating that enable a single source of truth across even linked systems.
The reality is that many aspects of the tech stack (even those layers and tools designed to foster interoperability) leave enormous gaps in, and challenges to, interoperability. Companies cannot easily manage and understand data and metadata across the enterprise tech stack with a high degree of trust. This requires maximized automation and governance from a data integration platform capable of overcoming these and a host of other issues related to interoperability within the tech stack.
Data exchange is the foundation of every tool, process, and aspect of the enterprise tech stack. In the recent past, this function seemed far too complex and changeable to expect finding a tool or platform that could make data interoperability simple, agile, and effective in real time. Doing so for every data producer, consumer, and project made the goal even more elusive, until Witboost.
Witboost provides a comprehensive data integration platform that addresses the core challenges of integrating data interoperability protocols in companies with complex tech stacks.
By breaking down data silos, ensuring data governance, and offering an enhanced developer experience, Witboost helps organizations streamline their data practices through its core capabilities of building, governing, and discovering data for projects—functions that address the interoperability challenges of a massive enterprise tech stack.
Witboost enables seamless data integration across disparate systems, departments, and geographies in a hybrid cloud/multicloud world. This ability to achieve end-to-end data interoperability by integrating legacy systems with modern data platforms in a customizable way ensures that data flows freely within the organization.
Witboost’s Marketplace streamlines data discovery processes, saving organizations an average of 50% in data project preparation time, as cited by the Forrester Total Economic Impact (TEI) Case Study.
The same Forrester report on Witboost reveals businesses are losing millions because of the inefficiencies brought about by poorly integrated tech stacks. The report shows how in one case study, a company used Witboost to achieve $4.2 million in efficiency savings (65%) over three years.
The Witboost Governance Shift Left methodology standardizes data quality and automates governance. This approach is designed to meet the constantly changing needs of data interoperability in extensive and changing enterprise tech stacks by ensuring that metadata, code, and data follow the same lifecycle. This is achieved by following a code-based software development lifecycle for collecting, processing, storing, deploying, using, and maintaining metadata.
The Shift Left governance method unifies the management of metadata in the same repository as that of the code. Companies can then easily establish a robust metadata management strategy that both aligns with business goals and objectives, as well as ensures they handle data securely and in compliance with industry regulations.
Such a strategy will enable them to:
Witboost also gives enterprises the ability to turn governance policies into computational policies that are automatically internalized as code rather than being rules to follow. This makes sure data users can understand what they can and cannot do with certain data and the reasons why. It further compels them to act in a certain manner regarding specific data and data set use.
This supports change management and training, regulatory compliance, security, and risk management. Witboost’s cloud deployment model works well for enterprises distributed across regions or even the globe while also enabling metadata policies that follow shifting data use compliance rules.
One of the many important ways the Witboost platform makes data interoperability possible is by integrating with Git repositories and CI/CD to manage metadata as software. This developer-centric approach enhances the developer experience and gives them the ability to apply computational policies to enforce the metadata curation process before projects go live in production.
Policy registration, editing, validation, and backtesting further streamline the continuous deployment process, reduce errors, and improve time to market for developers.
The Witboost platform is modular, customizable, scalable, and technology-agnostic. This empowers data producers and consumers across the enterprise to manage enterprise tech stack data as an interoperable ecosystem.
Its compatibility and integration abilities across multiple enterprise tools, applications, and systems enable data discovery, project build, and governance from a single UI. Furthermore, the recent launch of its partners space provides a full picture of Witboost’s support for vendors and technologies that prioritize open standards and interoperability protocols. This is in line with the platform’s support of your entire tech stack, including:
For large enterprises navigating the complexity of vast tech ecosystems, achieving data interoperability is no longer optional—it’s a business imperative.
Witboost offers the most efficient solution for breaking down data silos, integrating legacy systems, and future-proofing your tech stack for seamless operation. To see a real-world enterprise case study of Witboost supporting interoperability, click here.
Turn your tech piles into interoperable tech stacks where data access becomes part of a business-driven data architecture. Explore how Witboost can transform your data strategy and achieve seamless interoperability in your enterprise tech stack. Visit Witboost’s platform page today.