What Does Silo (Data Silo) Mean?
A silo in IT is an isolated point in a system where data is kept segregated (on purpose or accidently) from other parts of an organization's information and communication technology (ICT) architecture.
One classic example of a silo is a relational database that stores customer addresses. If internal security policies prevent this information from being shared with the organization's marketing team, for example, the database can be referred to as an information silo. When this happens, the organization may face the following roadblocks:
- Different business divisions will create multiple copies of the same data.
- Employees will make decisions based on inconsistent or incomplete data.
- C-level staff will struggle to obtain an accurate big picture view of the organization's data.
- Mid-level managers will find it difficult to quickly locate and access data for specific business initiatives.
Data silos often occur in large organizations because departmental units often have their own business priorities. Silos can be created on purpose -- using air gaps to protect sensitive information, for example -- but they can also be created by individuals who want to protect their own turf within an organization. This practice, which is sometimes referred to as knowledge hoarding, can be especially dangerous in organizations that do not value information transparency.
Techopedia Explains Silo (Data Silo)
Many IT experts talk about the limitations and negative impact of information silos.
Importance of minimizing data silos
What if an organization wants to know if a new product will work with their current marketing strategy and customer base and their current marketing stats and customer information is stored in separate data silos? They will need to take a metaphorical hammer to those silos and break them down to bring the data together. Here’s how to do that:
1. Decide what data is required to solve a business problem.
Consolidating and cleansing data to bring out business intelligence is no small task. That’s why the whole process needs to be driven by the questions management wants answered. Then write down those specific questions and decide what data will be required to answer them.
2. Understand the location of the data to be sourced.
Carry out a database audit to identify exactly what data the organization is already collecting. For each database, understand the following:
- Where is this database located?
- What are the key features, inputs and outputs of this database?
- What data is being captured?
- Which of the data points recorded here can answer your business questions?
- What’s the best way to get this information out of the database?
- How can this data be combined with other sources to create better context and analysis?
3. Consolidate data physically or virtually into a central repository.
Create data flows to collect data from disparate databases and data warehouses. Consider using a data lakehouse architecture to accommodate both structured and unstructured data.
The idea is to create a combined dataset that contains all the key information in one place. This can involve mapping individual data fields together, understanding the context of each data field, and developing individual data elements that show that data in a logical and cohesive way.
4. Preprocess the data to consolidate and cleanse it.
Once silos have been broken, administrators will need to clean the data and verify its quality. Initially, the data is likely to be “noisy.” It may have incorrect or missing information and other characteristics that need to be smoothed out. This part of the process is vital for data integrity, because end users need to have confidence in the data if they’re going to use it to make data-drive business decisions. Be careful when cleansing data, however. It's important not to miss important outliers and trends, as they too can often provide valuable insights.
5. Turn data into actionable business intelligence.
Once data silos have been broken down, end users can use the cleansed, integrated data to power reporting and business intelligence tools and be more confident about making business decisions. That translates to more efficiency, less waste, happier stakeholders and an improved bottom line.