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.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

5 Proven Strategies to Break Through the Data Silos