Data silos: many symptoms, one root cause

Change is coming. Better use of data means better decision-making and new forms of automation. However, on average, companies only use 50% of their available data. Why? Data silos are often the cause.

• Inability to create accurate and timely financial reports.
• Time-consuming and error-prone manual general ledger or P&L generation.
• Inability to measure marketing ROI or to optimise the conversion rate.
• Lack of visibility into customer lifetime value, buying patterns and cohort groupings.
• Opacity in sales: which sales behaviours yield the best results?

The list goes on. The symptoms of data silos are felt across departments. And often, each department sets out to solve the symptoms it feels most keenly. After all, this involves fewer discussions and much less headache than a whole-company approach.

In fact, it can make the problem worse. Different systems are owned by individual departments, creating data silos. Data inconsistencies create doubt and make effective analysis impossible. To succeed, businesses should treat the root cause of data silos, not just the symptoms.

Here’s an example:

The customer view

76% of customers expect companies to understand their needs and expectations. 70% say connected processes are very important to winning their business. This means seamless handoffs or tailored engagement based on past interactions.

In contrast, less than 40% of marketers are using even the most basic personalisation data. This might include purchase history, browsing history, referral source or click-stream data. The most likely diagnosis? The data is available, but stored in difficult-to-use silos.

It’s reflected in industry reports too. 44.2% of companies rank data silos as their top customer experience challenge. In large companies, it rises to 61%.

Beating the data silos

Data strategy concerns the whole business. And a data centralisation project will help you to solve all those symptoms in one go. It also lays the groundwork for adopting new technologies, including AI.

Here are three steps to consider:

Step 1. Centralise all data in one location. ELT can help you to speed up this process, by minimising human capital and cost.

Step 2. Apply your business logic to your centralised data. This creates a single source of truth that can be relied on by all departments.

Step 3. Address any data quality issues. It’s a common instinct to fix source system data before centralisation. But creating a central data store first will allow you to identify data issues more easily. This is more efficient, as you won’t waste time on duplicated or unneeded information.

Final thoughts

Gartner predicts that by 2022, a third of chief data officers will seek to formally value their organization’s information assets. This is likely to drive improvements in data literacy and management. If information is a strategic asset, it’s time to manage it like one.

Data silos impact the whole company. Inconsistent and inaccessible data is inefficient. Teams spend hours attempting to reconcile inconsistencies – or looking for the data they need. A single implementation can solve all of these issues at the root.

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