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.

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

5 Proven Strategies to Break Through the Data Silos