It only takes a Google search (or a hot minute or two on LinkedIn) to find a long list of modern data trends:
Big data and the mind-bogglingly explosive volume of it all. Competitive pressures across industries as more organizations endeavor to digitally transform. Machine learning and the use of AI becoming the rule, not the exception. The push towards data democratization. Globalization, distributed teams, and increasingly stringent and complex digital privacy governance and regulations.
Acting as a force multiplier, data collaboration can dramatically increase the effectiveness of organizational efforts and systems without the need for additional resources, effort, or investment. That said, there exists an exceptional amount of pressure on data engineers to get data collaboration right within their organizations.
All of this leads to a central truth: It’s imperative that organizations both understand and embrace what data collaboration is, how it functions to benefit an organization, why data engineers should begin their own collaboration initiatives by looking outside their profession, and best practices that can help facilitate success.
What is data collaboration?
Data collaboration is the process or practice of sharing, managing, and working on data across different teams, departments, or even entire organizations to achieve common goals in a coordinated and strategic manner. These goals can vary but often relate to an organization wanting to increase data integration to make better decisions, improve personalization and customer experiences, or increase operational efficiency.
Based on these goals, data collaboration may be applied on a case-by-case basis or implemented as a fundamental aspect of how an organization functions. But to fully understand (and appreciate) data collaboration, we must go beyond its technical definition and touch on the complicated interplay it relies upon and enables in data-driven ecosystems.
Data sharing, one of the main aspects of data collaboration, will often involve combining datasets (i.e., collections of data) from different departments within an organization and external data producers.
Partnerships between departments and external sources will be unique to an organization, as differing business outcomes may require data sharing with a variety of data sources—suppliers, consultants, customers, research institutions, or other businesses. This diversity is important, as this pooling of resources and knowledge enriches data, leading to better, more comprehensive insights.
Application programming interfaces (APIs) play a vital role in facilitating the connection and communication between all the differing software applications data providers bring to the table. Workflows ensure data sets are collected, processed, analyzed, and utilized accurately and efficiently. And visualization tools like dashboards play a vital role, enabling data collaborators to more easily monitor, report, and make decisions based on their enriched data.
But the value data collaboration creates can’t come at the expense of data security. Data access—and stakeholders maintaining control of their data throughout the data collaboration process—is essential. So data teams must stringently manage permissions, controlling access at various levels to ensure these newly created datasets remain compliant and abide by all relevant privacy regulations.
While the above only scratches the surface of the complexities involved, it sheds light on the interplay required to successfully implement data collaboration to drive insights, innovation, and improved business performance.
The benefits of data collaboration
Again, there are as many potential benefits of data collaboration as ways of using data. In general, within an organization, these typically include better organizational knowledge and skill development, more creativity, innovation, and better cross-functional alignment,
However, for an organization's more technical practitioners, beneficial use cases may specifically include the following:
Enhanced data change management processes
Robust data collaboration helps ensure any and all changes to data structures, formats, and usage are systematically recorded, reviewed, and implemented with full visibility.
The resulting transparency and accessibility facilitates clearer communication among data stakeholders. In this way, data collaboration also improves the traceability of changes and better compliance with data governance standards.
Improved dependency alerting
Data collaboration also enables dependency alerting to become more accurate and proactive. In turn, the organization can benefit from earlier identification of potential issues, more agility to respond to changes, and minimized impact of said changes on dependent processes or systems.
Highly collaborative teams may also find it easier to automate alerts that notify relevant data consumers in real time about changes in the data they depend on.
Change request centralization
Business environments are increasingly dynamic, meaning that change requests can come from anywhere in the organization (and at any time). Data collaboration also benefits here as it can centralize the process of requesting changes to data or data practices. Further, the requests themselves become more structured and efficient.
Change requests submitted through a centralized platform enable teams to log, prioritize, and address each in a timely manner. Overall, this can make an organization’s data management strategy more adaptive.
Faster detection and response to broken data pipelines
In addition to consistently producing better solutions, collaborative data analysis can help teams identify issues before they become problems (and fix problems before they create issues). As a result, teams (not just individuals) get better at analyzing and interpreting trends, patterns, correlations, and anomalies related to their data pipelines over time.
This also means that when a pipeline does break down, the collaborative team is better equipped to drill down into an issue faster, decreasing time-to-resolution.
Data models evolve more effectively
Data models enjoy enhanced flexibility and scalability thanks to robust data collaboration. This fosters an environment where stakeholder feedback, insights, and emerging needs are better integrated into the evolution of data models.
Additionally, the improved alignment with business goals and increased ability to leverage new data sources often improves the effectiveness of data-driven initiatives.
Improved potency of data contracts
As a final benefit of note, while drafting data contracts, highly collaborative organizations enjoy more opportunities to reduce misunderstandings or potential conflicts regarding their data use.
Data contracts are drafted, agreed on, and maintained with contributions from all relevant parties. As such, they enter service optimally comprehensive, clear, and aligned with the needs of all stakeholders—facilitating smoother data exchanges and integration efforts.
The valuable (and somewhat counterintuitive) starting point for successful data collaboration initiatives
Melvin Conway is well-known among software developers for Conway’s Law: “Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.” As noted by the author, speaker, and software developer Martin Fowler, this axiom is important enough “to affect every system [he’s] come across, and powerful enough that you're doomed to defeat if you try to fight it.”
There’s something immutable to this idea—that the things we make are invariably shaped by the ways we make them—which is why Conway’s Law can, and should, serve as a potent starting point for data engineers to champion data collaboration best practices in their own organizations.
Here’s why:
Data engineering often requires an extensive focus on technical proficiency and system optimization. Engineers typically work on specific systems or components within larger organizational architectures.
In these cases, user experience or user-centric design may naturally take a back seat, while the predominant roles and goals of the data engineer may not include a deep understanding of the broader dynamics at play within an organization, especially how these dynamics pertain to data flow and usage. For engineers looking to head up or bolster a data collaboration initiative, these factors can lead to several blindspots.
Rephrased for the context at hand, Conway’s Law could instead read as follows: “In organizations where data engineers are tasked with implementing data collaboration initiatives, the structure and effectiveness of these initiatives will mirror the communication patterns and organizational structures within the company."
As such, embracing our take on Conway’s Law grounds the data engineer in a mindset where cross-functional collaboration needs to be championed in addition to the technical aspects of data systems.
Despite the complexity and time invested in existing projects, the engineer must realize that the organizational change inherent in data collaboration necessitates adaptations in data systems, with an emphasis on flexibility and scalability.
And, as part of true collaboration, the pursuit of technical solutions can’t overtake the need for clear data governance structures that both support and mirror the overall communication and decision-making processes of the organization.
With this important keystone of collaboration in place, data engineers can employ best practices knowing that the foundation they’re building on is immutable as the law that informs it.
Best practices for implementing data collaboration
There exists a healthy bit of overlap for how best to implement a data process or practice. Since data quality is a foundational requirement for all data activities, it should be viewed and treated as a product, not a commodity. Clear governance and policies need to be established to ensure data is shared and used correctly.
There are, however, a few best practices specific to successful data collaboration:
1. Encourage cross-functional collaboration
Actively break down organizational silos (data silos, departmental silos, etc.) in order to foster collaboration across different departments and teams. Whatever their justification, siloing stifles the diverse data and sources that productive collaboration hinges on.
2. Promote transparency and open communication
While important in all areas, everyone involved in data collaboration, be it a project or process, needs to be accountable for promoting transparency and open communication. Build on the clear policies and guidelines established through governance. Establish feedback mechanisms. Recognize and reward openness as data collaboration begins to thrive.
3. Invest in people and processes before tools and technologies
In data engineering, prioritizing tools and technologies is common. But that doesn’t make it correct. Due to the complexity and scale of data collaboration in organizations, even the tech provides diminishing returns without skilled personnel operating within a robust framework of efficient, adaptable processes. It will be the people and processes that begin to cultivate a culture of collaboration, one where technology serves an organization’s goals (as opposed to dictating them).
From Conway to contracts: Why collaborative success hinges on better beginnings
For those seeking to harness the full potential of their data through data collaboration, the implementation of data contracts inevitably emerges as a pivotal tool. These contracts serve as a blueprint ensuring clarity, consistency, and compliance in the collaborative process, much like architectural plans in building design.
As we navigate the intricate landscape of data collaboration, the value of well-defined data contracts cannot be overstated. They are the cornerstone of effective data management, enabling organizations to unlock the true power of their data assets.
For those intrigued by the possibilities of enhanced data collaboration and the strategic use of data contracts, we extend an invitation to join our product waitlist at Gable.ai. There, you'll find a community of forward-thinking professionals and a platform poised to revolutionize the way we approach data collaboration.
