What are data silos and shadow IT? Break it down for data-driven success

Sharing data across the enterprise breaks down information barriers and sets the stage for any successful transformation. Fragmented data, like isolated islands, hinders a holistic view and informed decision-making. This article delves into the concept of data silos, their drawbacks, and how to overcome them. It also explores the issue of shadow IT as a response to siloed data and the right approach to breaking down data silos.

What are data silos and why are they bad?

Imagine a company with separate filing cabinets for each department: sales, marketing, finance. Each cabinet holds valuable information, but they're isolated fortresses. You can't see the bigger picture because the data is fragmented. It's like isolated data islands with no direct transport between them. This is the reality of data silos, and it's a major hurdle for companies looking to leverage the power of their data.

What are data silos?

Just like on a real island, data silos prevent a holistic view of the company's operations. You can't see the bigger picture because the data is trapped in its own little world. This makes it difficult to generate comprehensive reports, identify trends, and make data-driven decisions.

Sharing data across the enterprise is a crucial first step in any transformation journey. Data transformation is often a top priority when companies embark on digital transformation. This typically begins with dismantling data silos. The objective is to normalize, aggregate, and ultimately make available to analysts across the organization data from different areas of the enterprise.

A defining moment for companies comes when they realize the power of combining data from different sources. This shift away from siloed systems reveals that unified data leads to greater usability, accuracy, and ultimately, better business outcomes. This newfound agility allows companies to react quickly to pressing needs and avoid wasting resources on projects with minimal organizational impact.

Centralized reporting increases the value of data. It is easy to see why removing barriers to data access would have a positive impact on the company. But what exactly does breaking down data silos mean?

From siloed systems to data warehouse to a single data platform

The most common solution for eliminating data silos is to implement a data warehouse. This central repository consolidates structured data from multiple sources, fostering collaboration and information sharing across the enterprise.

Data warehouses require a lengthy ETL (Extract, Transform, Load) process to prepare data for analysis. Meanwhile, unstructured data often ended up in a data lake, requiring specialized skills and tools (like Python, Apache Spark, TensorFlow) for analysis by data scientists. Building and implementing a data warehouse is therefore initially complex and can be challenging. But in the long term, it can be a great (and probably the most effective) source of insights for the entire company.

By overcoming these data silos with the right solutions like data warehousing, everyone can use all the structured data to make decisions such as which products to produce, how to price them, how much stock to hold, etc. Data-driven decisions always deliver the best results, but to make the most informed decision, you need all the data available.

Recently, decentralized data governance models such as data mesh have gained traction as an alternative solution. This approach distributes data ownership and governance across business domains, promoting agility.

To break down data and political silos and promote democratization, the trend is moving towards a single data platform built on a federated data management model.

How is this put into practice? Companies conduct an inventory of all their data throughout the organization and organize it by type, owner, platform, usage, data formats, and terminology. With this information, they then develop a data dictionary and a standardized taxonomy.

What is Shadow IT?

Frustrated by the difficulty of accessing data from other islands, departments might resort to creating their own "shadow IT" solutions. These are like hidden new islands, filled with unauthorized spreadsheets or personal databases. While these islands might offer quick access for a single department, they further fragment the data landscape, creating a storm of inconsistency.

What is Shadow IT?

Shadow IT refers to the use and management of any IT technologies, solutions, services, projects, and infrastructure without the formal approval and support of internal IT departments.

While shadow IT may sound like a thing of the past, it is still with us today. Shadow IT poses significant risks, including security breaches, wasted budget, and further data fragmentation.

Why do users turn to shadow IT?

Employees adopt shadow IT practices for a simple reason: it gets the job done. They use unauthorized tools and applications to fill gaps in the officially supported technologies and fulfill their job requirements in ways that make their life easier. This can be attributed to two key shortcomings of the organization's IT strategy:

Part of the problem lies with the organizations:

  1. Lack of appropriate options: IT may not provide the specific tools or resources that employees need to perform their jobs efficiently.
  2. Slow approval processes: The process for approving and deploying new technologies can be cumbersome and time-consuming, leading to frustration and workarounds.

Risks from Shadow IT

The High Cost of Integration

Now, imagine building a central data warehouse. The problem? Integrating the siloed data from all the islands becomes a monumental task.  It's like trying to build bridges and tunnels between all these isolated landmasses.  The cost of custom coding and data cleansing becomes a heavy anchor, dragging down the entire project.

Departments clinging to their island data often lead to the warehouse replicating information. It's like having duplicate copies of the same resources scattered across different islands - a waste of storage space and logistical nonsense.

Even after a massive effort and expense, the data warehouse might not be a reliable source of insight. Inconsistent data formats and quality issues are like hidden whirlpools, making it dangerous to navigate and leading to unreliable or misleading information.

The key to avoiding the high end of that spectrum is by taking steps to unify your data and avoid the need for costly reconstructions down the line.

How to reduce Shadow IT and break down data silos?

Open communication and collaboration: Promote a culture of open communication between IT and business users. Actively solicit user feedback on existing technologies and identify their needs for new technologies. This understanding will guide IT in providing the right tools and resources.

Empower users through education: Educate employees about the risks of shadow IT and the benefits of using approved solutions. Emphasize IT's role in supporting their technology needs within established protocols.

Establish efficient governance: Create an IT governance framework that promotes innovation by enabling the rapid adoption of new technologies that have been identified, evaluated, and made available to IT users. Develop policies that prioritize and anticipate user needs. Balance policy enforcement with the flexibility to adapt to the changing IT needs of end users.

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