Due to the rapid increment in business applications worldwide, data sits in several applications or servers which can result in the creation of data silos. Two highly critical situations cause data silos—several teams either store complementary but separated data or keep storing the same data. Each situation creates different issues.
Indeed data silos restrict your team’s ability to utilize data to make informed decisions and manage business processes. Moreover, they hinder the business processes of operational workers from accessing relevant and required data regarding the supply chain, customers, products etc. Rigid data silos create issues in accessing real-time data and deficit the holistic aspect of customer data. Applying a robust data governance program can quickly minimize the data silos in the company and enhance the common data policies and standards.
Top 5 big data silos that hinder your organization
from having data-driven decision making
1. Data analysis is very tricky
Data silos can create issues in data analysis as the data may be stored in different and inconsistent formats. There are several time-taken manual works to be done to put this data into a standard and compatible form.
2. Access issues can hinder your data research
Silos can hinder issues in data accessibility that can result in slow data research. As a result, it can degrade the ROI of related projects.
3. Effort and work are duplicated
Many times different team members can access the same data parallelly. However, if there is an in-efficient resource in the organization, the chance to share experience and collaboration between the teams can be wasted.
4. Information goes in handy with a siloed culture
Whenever data is siloed, it frequently focuses on a siloed working environment and an organizational culture where vertical divisions work autonomously and no one offers data outside of their area. Organization would work on excessively rigid group structures where information isn’t being shared.
5. Data security is compromised
There’s a more severe gamble of information data with no all-encompassing control over how data is collected and analyzed. Since representatives are running their exploration or putting away information on non-supported applications, there’s a more severe risk of the data being lost or, in any case, splitting the difference.
Getting Started with Us
Without data silos, sharing of data across an organization becomes a lot smoother. The decision-makers can utilize BI (Business Intelligence) tools to improve products, find hidden opportunities, and improve business operations. By gathering and analyzing data from different sources via data integration, they can find patterns and trends that might have been challenging to find earlier.
Agilisium has expertise in the agile techniques behind different data-driven technologies. With our expertise, we assist organizations in getting recovered from the issues of data silos.
Scalable Big Data Analytics Platform
We are skilled in utilizing scalable big data analytics platforms and finding out new opportunities during data discovery. The BI tools we use can easily break down the barriers between data-sharing and collaboration among the teams.
AWS S3 Data Lake for a Single Source of Truth
AWS S3 data lakehouse enables eradicating data silos. Just keep one copy of your data in the S3-based data lake and utilize some other services like Redshift to query the data. You can also combine the operational databases using machine learning. We can help you in leveraging the full potential of the AWS S3 data lake to successfully overcome data silos.
Data Warehouse in AWS Redshift that Supports Downstream BI & Advanced Analytics at Scale
The Data Warehouse in AWS Redshift can eliminate the silos. It helps you store structured transactions for data analytics and reporting applications. Moreover, organizations can easily develop data lakes to keep up a large amount of data which can be unstructured, formulated, structured, and semi-structured.
We offer architecture design and infrastructure guidance to build, optimize, and maintain app containerization methods utilizing modern data analytics and big data tools.
Access to a 360-degree view of customer data
Business answers with fewer clicks through the self-service BI tool
2x faster territory transition
Supports scalable Advanced Analytics at the speed of thought