Not only is data big and scattered everywhere, but it is also siloed. Shattered into a thousand pieces, part of it is stored in the cloud, some other part rests in a random computer memory. Data that you didn't even know existed on your servers falls off your radar. Untouched, it slowly ages until it rots in the depth of your databases infecting the integrity and consistency of the rest of your data.

What is a data silo?

Also known as an information silo, a data silo is a repository of data that is not part of an organization’s enterprise-wide data management. These silos reveal a situation where one or more information systems or subsystems that are conceptually connected, are incapable of operating with one another.

With data solely accessible to specific groups of users, these data silos describe isolated islands in an insular system that are discovered when discrepancies occur.

When building a 360° view, we might need to cross internal data from one or many ERPs, back-ends, CRMs and some real-time customer behaviour data from the website. Ideally, all systems provide complementary data about customers, their user profile, history of purchases and online behaviour and when all linked, can create consolidated intelligence. However, if the systems cannot communicate with each other, then, the data sets cannot be linked, hence are invisible to each other.

Why do data silos exist?

Silos can be formed intentionally but also unintentionally. In dysfunctional organizations, people regroup in cliques, inter-office politics ensue and data protection becomes the cultural norm.

In other big organizations, silos exist for a reason. For large-sized companies, opening up all information silos equates to information overload. In a sense, compartmentalization creates discretion and prevents these data floods. However, inter-communication is instrumental in facilitating the sharing of information and preventing teams’ isolation.

Data silos develop due to different causes:

It is like seeing departments as separate rooms, each with its own locked door, blocking the passage of information. In these disorganized organizations, it becomes more and more difficult to uncover silos. And whether they happen because of departmental politics, turf warfare or accentuated sense of rivalry, the result is the same: division.

In most cases, these silos happen naturally, driven by one-sided views and a lack of transparency, where people look at things in isolation from one company’s perspective.

Data silos can have insidious effects, impede productivity and call into question data integrity, which can be disastrous for any organization.

Nonetheless, it is still valid to ask whether silos can be useful, rather than assuming that because they do have negative aspects that we need to break them down.

Does the silo align with the organization’s strategy? Is the silo an instrument for data security? Is the silo causing challenges? How can we integrate the silo?

It is important to acknowledge that data silos emerge out of organizational silos. Separating people in forms of departments or workgroups will inevitably lead to some sort of silos. It’s a basic relationship of cause and effect.

Unmanaged data silos

When unmanaged, data silos can cause an insidious effect on a company. They, impede productivity and call into question data integrity, which can be disastrous for any organization due to:

These silos cause wasted resources, inhibit productivity and slow down all organizations’ dreams of becoming data-centric, where access to data is democratized

How to fix the data silos?

Let’s cut to the chase, undoing data silos is not a piece of cake. There are 2 solutions: either you:

  1. Identify what systems or practices are generating those data silos. Audit how each department or each team collects data. Identify which data sources are in use, the data consumers and assess if the generated data is accurate.
  2. The second step is to unify your technology platforms. Most companies rely on siloed software systems to store data, hence, produce inefficiences and duplicated data flows. It’s surprising how many technologies are used within one single department, let alone a whole company. Too many disparate software applications that do not communicate, siloed systems that create blind spots and super-users serving as data bottlenecks.
  3. Breaking the silos down is a time-consuming process with high costs. This method does not give you the insurance that the silos will disappear or reappear.

    • Embrace them and find a way to manage them:
      Rather than falling into the trap of trying to break the data silos, embracing them via a management solution. We cannot stop data silos from emerging, yet what we can do is think of ways to manage them. It is no longer about breaking down the data silos but rather about looking across the silos and uncovering the insights hidden inside them. What you need is a flexible and source-agnostic solution, so that data from anywhere and everywhere can interact with each other. Using an end-to-end data platform that is architected to connect to data sources, collect and process all forms of data (structured, unstructured, historical or real-time) will help you manage, control and explore your data. It will also help you tap into your data in full autonomy and have an end-to-end picture of his journey and experience with your organization thanks to a holistic all-in-one platform.

    The holistic all-in-one solution Digazu

    Digazu is a cutting-edge, feature-rich data science platform that can help you navigate through data silos and bring holistic data management into your organization. Thanks to the platform, you can leverage the highest possible value out of your data and build custom-made customer experiences.

    With Digazu, you can ingest data from all your data sources be it your internal databases, social media or even web data, store it in a ready-to-use data lake and make it available to different data users in a standardized and fully governed way.

    You can also build distribution and transformation pipelines to cross and combine data coming from all sorts of sources, and uncover more meaningful insights. Needless to mention, that you have access to a secured environment, where your analysts can explore and build artificial intelligence in real-time.

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