Have you ever found yourself stuck between two unappealing options? Imagine spending hours perfecting a report only to realize that, while accurate, it might arrive too late to impact business decisions. Or, on the flip side, rushing out a quick summary to meet a deadline, only to be filled with qualifiers and uncertainties. This dilemma has plagued data architecture for years, known in the industry as the “bimodal IT” trap.
What is Bimodal IT?
“Bimodal IT” was a term coined by Gartner to describe a pervasive challenge in data architecture. Organizations often find themselves with two paths:
Mode 1 is focused on stability, reliability, and accuracy but is notoriously slow, sometimes too slow to impact timely decision-making.
Mode 2 prioritizes speed and agility but comes at the expense of accuracy and reliability.
This creates a lose-lose scenario where companies are either waiting forever for accurate data or making rapid decisions based on potentially incomplete insights. As Dr. Thomas Redman, known as “the Data Doc,” puts it, “Without high-quality data, you’re going to get high-quality wrong answers at high speed.”
So, how do we escape this trap?
Enter Data Mesh: The End of Bimodal Headaches
Enter Data Mesh, a game-changing approach that allows data architecture to break free from the constraints of bimodal IT. The concept is simple but powerful: decentralize data ownership. Rather than treating data as a single entity owned by IT or centralized data teams, data mesh distributes data ownership to individual business domains—putting the right data in the hands of the right people at the right time, what I call the 3 Rs.
This approach means that each team—whether marketing, sales, or operations—manages and treats their data as a product, ensuring it’s clean, accurate, and useful. As Zhamak Dehghani, a pioneer in data mesh, explains, “Data mesh shifts responsibility closer to the people who know the data best. It promotes agility, innovation, and data democratization.”
This decentralized model also supports scalability, making it easier for businesses to handle the demands of rapid growth. As Gartner notes, “Scalability through decentralization is the next frontier for data-driven enterprises, allowing them to adapt to the demands of real-time business intelligence.”
Imagine a scenario where each team handles its data independently but follows agreed-upon standards, making collaboration smoother and more effective. Data no longer sits in a silo, waiting in a queue for updates or access; instead, it’s always available and ready to go.
Speed Meets Accuracy: How Data Mesh Solves the Dilemma
Data mesh enables organizations to move quickly without sacrificing accuracy. Here’s how:
Real-Time Data Access: Teams can access trusted data as they need it, without waiting on a central team to update or provide it. This autonomy allows, for example, the marketing team to analyze customer sentiment in real time and create targeted campaigns with up-to-date insights.
Ownership and Context: By giving ownership of data to the teams that know it best, you reduce delays and errors caused by handoffs and misunderstandings. It’s like allowing a chef to create their signature sauce rather than trying to follow a recipe made by someone who doesn’t know their style.
This approach doesn’t just improve speed; it also boosts cross-functional collaboration by breaking down data silos and ensuring that insights can be shared across departments in real time. McKinsey has found that “organizations with robust cross-functional data practices are 23% more likely to meet or exceed business objectives,” highlighting the strategic value of accessible and shareable data.
As Ramesh Narasimhan, Head of Analytics at IBM, notes, “When you put data ownership back into the hands of business users, it creates a much stronger alignment between data and business outcomes. This shift is transformational.”
Making Data Mesh Real with Latttice
Decentralizing data sounds great in theory, but putting it into practice requires the right tools. That’s where Latttice comes in—a platform designed to make data mesh a reality.
Latttice serves as a comprehensive toolkit, enabling teams to build a high-speed, accurate data products:
Domain-Centric Management: With Latttice, teams can easily manage, share, and productize their data products, connecting those who want to use data to those who want to share data.
Data as a Product: All data can be treated like a product—cleaned, combined, augmented, secured, documented, and ready for use. Latttice streamlines the productization of data products, so your data is accessible, understandable, and usable by anyone who needs it.
Self-Service: Gone are the days of waiting for central teams support. Latttice empowers teams to create and manage their own data products independently, reducing bottlenecks and allowing for quick turnarounds.
Federated Governance: While teams enjoy autonomy, there are still guardrails in place to ensure consistency and compliance. Latttice provides the necessary governance to keep your data products are cohesive, reliable, secure, and compliant.
This approach also reduces technical debt by distributing the data management workload, allowing each domain to evolve its data products independently. Forrester reports that “Decentralized data ownership can mitigate technical debt, providing agility and allowing IT resources to focus on innovation rather than maintenance.” With reduced technical debt, teams can focus more on innovation and less on maintenance, a shift that many organizations are finding essential in today’s fast-paced market.
Real-Time Data Access Without Compromise
The true power of data mesh, amplified by tools like Latttice, is the ability to handle real-time data processing without sacrificing quality or speed. Traditional centralized systems often hit bottlenecks when asked to handle fast, real-time analytics. By pushing processing closer to where data is created, data mesh minimizes these obstacles, delivering high-speed, high-accuracy insights.
Decentralization in data mesh doesn’t just enhance speed; it also enables better data security and compliance. Teams can manage compliance requirements relevant to their data independently, adapting more flexibly to regulations like GDPR or CCPA, whilst applying enterprise guardrails.
Deloitte highlights that “Domain-specific compliance management increases an organization’s ability to respond to new regulations, ensuring they can adapt without compromising agility or speed.”
According to Doug Laney, author of Infonomics, “Speed is meaningless without quality. With data-as-a-product approaches like data mesh, you don’t have to sacrifice one for the other. This creates a new paradigm where data both informs and drives business in real-time.”
Latttice strengthens this capability by offering a unified platform that supports data mesh principles, maintaining necessary governance while allowing for agility and independence. It’s truly the best of both worlds.
Why Settle for Less?
For too long, companies have struggled with the slow-but-accurate vs. fast-but-risky dilemma. Data mesh, with a little help from Latttice, finally provides a way to overcome this limitation. High accuracy and high speed? Yes, you can have both.
As data literacy and skill development increase across teams managing their own data, organizations find themselves more resilient and ready to adapt to business demands. Harvard Business Review emphasizes that “Organizations with strong data literacy are twice as likely to outperform their peers on key business metrics.” Data mesh, facilitated by tools like Latttice, creates this literacy by embedding data management skills across departments, fostering a data-driven culture and making each team invested in the quality and reliability of their data.
If your organization has been stuck in the bimodal IT rut, now might be the time to enhance your architecture. Embrace a solution that offers the best of both worlds and enables your teams to be both quick and precise.
Cameron Price.
References
Dehghani, Z. (2020). Data Mesh: Delivering Data-Driven Value at Scale. O'Reilly Media.
Gartner. (2014). Bimodal IT: How to Be Digitally Agile Without Making a Mess. Gartner Research.
Laney, D. (2018). Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage. Routledge.
McKinsey & Company. (2021). The Data-Driven Enterprise of 2025. Retrieved from McKinsey Insights.
Redman, T. (2020). “Without high-quality data, you’re going to get high-quality wrong answers at high speed.” Harvard Business Review. Retrieved from Harvard Business Review.
Deloitte. (2022). Compliance and Decentralization: Managing Domain-Specific Data in the Age of GDPR. Deloitte Insights.
Narasimhan, R. (2021). “When you put data ownership back into the hands of business users, it creates a much stronger alignment between data and business outcomes. This shift is transformational.” IBM Analytics Insights. Retrieved from IBM.
Forrester. (2021). Decentralized Data Ownership and Reducing Technical Debt in Data Management. Forrester Research.
Harvard Business Review. (2020). “Organizations with strong data literacy are twice as likely to outperform their peers on key business metrics.” Harvard Business Review. Retrieved from Harvard Business Review.
Amazon Web Services (AWS). (2022). Empowering Teams for Innovation with Decentralized Data Architectures. AWS Blog. Retrieved from AWS.
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