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5 Proven Strategies to Break Through the Data Silos

4 minutes, 48 seconds read

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-

  1. Data Federation: A technique that uses metadata to compile data from a variety of sources into a unified virtual database.
  2. Data Virtualization: A system that retrieves and manipulates data cleaning up data inconsistencies (e.g. file formats).
  3. 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


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