5 Proven Strategies to Break Through the Data Silos

4 minutes, 48 seconds read

Published on Mar 6, 2020

Updated on May 10, 2020

In 2016, when Dell announced a major merger with EMC and VMware, their biggest challenge was to break through the organization silos. All three giants had their legacy systems and data management platforms. Integrating the networks and creating a collaborative work environment posed an immediate call to action.

Silos exist both internally and externally. Different departments use different software that generates data in their formats, which are not necessarily compatible with other software or applications.

Today, while organizations seek AI initiatives to improve productivity and operational efficiency, siloed data from legacy systems pose constrictive barriers to achieving the expected outcomes.

Data is fodder for any AI-based system. Even in a connected ecosystem, siloed data is extremely difficult to repurpose. To maintain a competitive edge, organizations need to embrace data-driven transformation. And to achieve this, there’s a dire need to break through the data silos.

5 Strategies to break through the data silos

We produce over 2.5 quintillion bytes of data every day. However, a recent study reveals that individual organizations own nearly 80% of the data and are not searchable by others.

Edd Wilder James of Silicon Valley Data Science says that just like data analysis, which requires 80% of efforts in data preparation, breaking through data silos will require 80% of work in becoming data-driven. The data-driven approach corresponds to integrating all the data sources and making them available across the organization as a whole.

1. Data democratization

The pressure to use data for fact-based decisions is immense on organizations. However, the organizations lack a clear strategy to make the data accessible to every accounted stakeholder. So far, the IT department of any organization owned the data supporting the silo culture.

Data Democratization aligns with the goal of making data available to use for decision making with no barriers to understanding or accessing them. Backing up with smart technologies and solutions, it’s simpler to achieve data democracy. For example-

Data Federation: A technique that uses metadata to compile data from a variety of sources into a unified virtual database.
Data Virtualization: A system that retrieves and manipulates data cleaning up data inconsistencies (e.g. file formats).
Self-service BI Applications: Tedious data preparation is involved in powerful analytical insights. Gathering all useful data and presenting insights in a way that even a non-technical person understands is a way through the data silos.

2. Cloud-based approach

To achieve the initial levels of BI, it’s crucial to organize all the data in a reusable format. The best way is to aggregate data into a cloud-based warehouse or Data Lake. However, it is important to maintain data lakes strategically with useful data because every business is unique and one just can’t pull a unique advantage off the shelf.

Cloud has benefited many global financial organizations in breaking through the data silos. AllianceBernstein, one of the US leading asset management firms, is an early adopter of the cloud-based approach (2009) to empower its sales, marketing and support teams with proactive and real-time updates.

3. Representation Learning

Featured Learning or Representation Learning is a branch of Machine Learning to understand data at different levels. Especially real-world data comes in the form of images, audio, and video, which many current enterprise applications are not capable of using directly.

Representation learning provides process-ready (mathematically and computationally convenient to use) data to the applications, thus bridging the gap between real-world and internal data for deriving intelligent insights.

4. Creating a unified view of data management systems

Large enterprises and Government organizations are essentially the victims of siloed data. Ironically, these are the ones who need a composite knowledge about their customers from different touchpoints.

For example, NASA, for years, struggled to find a relation between its many tests, faults, experiments and designs. The organization partnered with Stardog to create a unified view of its data with real-world context. Unifying data from different sources is also known as data virtualization. It is a process of integrating all enterprise data siloed across the disparate systems, processing it and delivering to business users in real-time.

5. Embracing the omnichannel infrastructure

An omnichannel approach is famed for bringing exceptional customer experiences. But, from the data point of view, it is of great benefit for the organizations as well. Omnichannel infrastructure involves bringing together multiple (in fact, all) systems and applications that have different data formats.

Enterprises have started leveraging the omnichannel approach through point-to-point integration and APIs. For example, FlowMagic is a workflow automation platform used by some of the leading insurance companies in the world for end-to-end claims automation. The platform integrates all the digital touchpoints of any operational processes and creates a unified system for data collection, storage, and processing for decision-ready insights.

Bonus – Translation tools

It might seem insignificant to many, but languages and regional software also contribute to creating data silos. Combing through digital records becomes cumbersome for MNCs when the information is stored in an unfamiliar language to the stakeholders.

A simple solution to overcome this kind of data silo is to opt for a platform with cognitive capabilities. KPMG, using Microsoft Azure’s built-in translation tools, is able to improve its analytics services and derive better outcomes.

The bottom line

Most organizations face challenges in collaboration, execution and measurement of their business goals due to siloed data. While data is the new oil for businesses, becoming a data-driven organization requires overcoming silos, which may be prevailing in several forms like structural, political, or maybe vendor lock-in.

In the world of AI, being data-driven is at the core. However, not everyone has the luxury of implementing data strategies (the way we need data now) from scratch. Thus, applying an incremental approach is feasible to anything and everything that creates silos and thus breaking through it.

Seeking an integrated platform for your organization’s operations? Or have thoughts and suggestions on this outlook? Please feel free to write to us at hello@mantralabsglobal.com.

Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform.

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization: Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

5 Proven Strategies to Break Through the Data Silos