Is the following scenario very familiar to you?
You urgently need certain data, but you don’t have access to it and need to ask colleagues from other departments or teams for the data.
From the back-and-forth email exchange and long waiting time, you feel exhausted.
The lack of centralized data management in your team might indicate you are having a problem called data silos.
What Is A Data Silo?
Data silo, sometimes used interchangeably with the term information silo, refers to the scattered storage of data or information in various departments or business systems.
The information is not interconnected, shared, or utilized together as a team.
Data silos could be formed due to various reasons and in different forms.
Data are scattered everywhere like isolated islands from each other.
What Leads to A Data Silo?
Data silos could be formed due to various reasons. Most companies would fall into either of these categories:
Siloed Department and Organization Structure
A data silo is extremely common in companies with multiple departments with each function stand-alone and having its own data.
Each business division store and manage its data in its own way, forming different subsystems.
For instance, different departments could be using different types and versions of information management systems. The production department might be using the ERP system while the sales department uses the CRM system.
The data stored in each subsystem is like a unique and isolated island. It is difficult to connect, exchange, and integrate with other data in the enterprise.
At the same time, many companies with independent departments are more resistant to interdepartmental cooperation. Employees do not want to share the data of their own departments with others.
They are used to each owning their own data and information and focusing on their own department’s business goals.
This problem requires changes from the management level of the organization because it is caused by the long-term enterprise culture – a culture that does not promote collaboration and working towards the common business goal.
Lack of Focus on Data Management
Many companies collect data but lack systematic data governance. They usually collect certain data without governing the data quality and use the data directly to obtain insights.
Companies with a lack of focus on data management also often have problems such as data inconsistency and lack of guidance and standardization in data collection and usage, including the phenomenon of data silos.
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Lack of Technical Know-how
Lastly, many business owners might have already realized the problem with data silos and they want to manage their data more effectively in a centralized platform, but they are not sure what to do.
Due to the lack of technical know-how, they do not know what type of solutions are there and what type of tools can they use to eliminate the problem.
Why Data Silos Are A Problem?
A data silo could be a HUGE problem for you if you want to embed data into your decision-making. Let me explain.
1. Data Inconsistency
The first issue with data silo is that it leads to data inconsistency.
Due to a lack of data integration, various departments of the enterprise will produce repetitive data when carrying out data collection and having the same information stored in different databases.
Such data duplication and redundancy reduce the quality of the data, take up storage space, and reduce the efficiency of employees.
It is even worse if the team cannot collect or obtain the data themselves and needs to send data requests to other teams at any time. As different departments have different understandings and definitions of data, the cost of back-and-forth communication within the enterprise rises. Also, once the data is not updated or shared timely, data inconsistency will occur.
Such management of data will cause the company to waste time, and money and lower overall working productivity.
2. No Full Picture in Making Decisions
When there is no data integration, business leaders can’t see the full picture when making decisions. This will lead to wrong decision-making and missing business opportunities.
It is very risky to analyze and make decisions based on only part of the data, because the causal relationship concluded may be wrong as many other important factors may be missing at the moment.
We can only see the complete picture and be able to make more accurate data-driven decisions when all the important data are combined together.
3. Lack of Trust in Data that Limits its Use and its Business Benefits
Healthy data refers to everyone in the organization being able to access the data they need when they need it with confidence. This requires proper data management or governance to maintain the quality of data. Without data governance, members don’t trust their data.
If members can’t use the data with confidence in business usage or get the data they need easily, data doesn’t add value to the business.
4. Data Breaches and Leakage
The next serious consequence of a data silo would be data breaches and leakages.
When data are scattered around in different systems and held by different people, it’s almost impossible for the company to exercise control over who can have which data. The company can’t exercise data governance and impose regulations or restrictions on data usage and sharing.
For most companies, data silos are stored by individual users either as offline documents or online cloud-based services like Google Drive. This can pose serious issues as, firstly, these documents could be easily accessed by third parties outside the company and could cause potential data leakage.
Second, companies might be violating data privacy and protection laws from the act.
Lastly, once the member left the company, this information (which might be confidential) could be disseminated to outsiders.
When the same piece of information is stored in multiple devices, it’s taking up huge storage space.
It’s a cost burden to the company as the number of servers and storage devices has to be added to accommodate the needs. This also indicates waste and low efficiency in IT usage.
6. Poor Coordination Between Members
Data silos greatly reduce the collaboration between departments.
In many cases, it tenses the relationship across departments from difficulty obtaining the data they need.
The tension refrains team members from working towards the common goal and achieving greater productivity and efficiency.
Generally speaking, data silos will pose a barrier to success for companies.
Inaccurate and untimely data often lead to wrong or slow decision-making. Thus affecting the reputation of companies and their competitive advantage in the market. Making decisions from inconsistent data could also potentially damage relationships with customers.
Viewing from a long-term perspective, enterprises should resolve the problem of data silos as a priority, so that the information and data of various departments can be interconnected. This will benefit the company in terms of overall productivity, efficiency, and long-term progress.
How to Break Down Data Silos?
Our top preferred solution to break down data silos is through Azure Data Factory.
Azure Data Factory is a cloud-based data integration service that allows you to construct ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines.
By using Azure Data Factory, you can break down data silos, enabling seamless data movement and transformation across various data stores.
Capabilities Azure Data Factory offers:
1. Unified Data Integration
With Azure Data Factory, you can connect to a plethora of data sources, whether they are on-premises, in the Azure cloud, or even in other cloud services.
This diversity of data sources helps to dissolve the barriers between data stored in separate systems. Consequently, you can centralize the information from diverse databases, data warehouses, and big data stores, creating a single, unified view of your data assets.
2. Seamless Data Transformation
Azure Data Factory isn’t just about moving data; it’s also about transforming it.
With integration with Azure Databricks, and Azure HDInsight, you can carry out a wide range of transformation activities. Whether you need to clean your data, aggregate it, or restructure it for analysis, Azure Data Factory has the tools to do the job.
This way, not only is your data unified, but it’s also ready for insightful analysis and decision-making.
3. Automated Data Pipelines
One of the key strengths of Azure Data Factory is its ability to automate data pipelines. Once you’ve set up a pipeline, you can schedule it to run on a regular basis, or even trigger it based on specific events.
This automation saves significant time and effort, as you no longer need to manually initiate data transfers or transformations. Moreover, it ensures that your data is always up-to-date, providing your teams with the latest insights.
4. Secure and Compliant
Azure Data Factory also excels in maintaining data privacy and security. It offers robust data encryption during transit and at rest, and it complies with a host of global regulatory standards. This ensures that even as you break down data silos, you can keep your data safe and compliant.
DataSI’s Approach to Data Silos
At DataSI, we adopt a simple 4 steps framework to help you to break down data silos effectively.
Talk to us today
Find out how we can help your business to build a successful data strategy.