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Data silos

Data Silos and How to Increase Sales Through Artificial Intelligence Integration

In the real world, silos are giant metal warehouses on farms that store grains to keep them safe from animals, bugs and inclement weather. In the data science world, there is also the concept of silos, but unlike the ones on farms, you don’t want data silos in your company.

A data silo is a deposit of fixed data that is controlled by a specific department and isolated from the rest of the organization. Within data management, a silo is a problem because it means that some part of the organization is disconnected from the company’s central data processing system. After all, data is fuel for artificial intelligence.

Important studies like “Emerging business opportunities in AI from the consultant Comptia revealed that three out of four companies have data silos.

Data sources with inefficient data mining models

According to the study, 44% of mid-sized companies recognized data silos in their organizations, making them more likely than companies of other sizes to have data silos. This is because when small companies grow quickly, they usually make the mistake of creating data analytics systems that are isolated from their other data acquisition systems. And this problem isn’t limited to SMEs. 35% of larger companies, despite having the capacity to implement a general database management system, still do it inefficiently.

Experts in the sector often talk about this paradox: most data acquisition systems are inefficient because they didn’t collect company data correctly in the first place. Designing a data acquisition system requires foresight, and companies need to put in the work before they implement a data management model that can feed the algorithms.

The study shows that apart from structural and organizational issues, there is a problem with how companies approach creating a data model and artificial intelligence system. Many companies treat it like software architecture and use a determinist approach while they solve structural problems. However, artificial intelligence is based on a probabilistic point of view, meaning that with more data sources come more correlations. This gets you closer to the right answers to your queries.

Data feeds artificial intelligence, and artificial intelligence tools help interpret data.

Towards a data management model that makes optimized data-driven decisions

Modernizing the supply chain goes beyond simply compiling data sources in a specific department or correctly managing databases and data warehouses. It involves a paradigm shift and new ways of thinking. This process should be led by executives who aren’t afraid to break traditional molds.

In the first phase, the data acquisition system has to be dynamic, intelligent and collaborative so it can capture data in real time, recognize patterns and synchronize with the other data collection sensors. It also has to show information through a control center that displays data in a graphic and easy-to-understand way.

In the second phase, the system must be able to make predictions. This provides executives with useful information that has a real, immediate and measurable impact on business decisions. With time and more data, the system will automate and produce continuous optimizations in all processes.

Getting rid of data silos is a process that starts with rethinking each department’s operations from scratch, and implementing data centers that are optimized to fit their needs. By connecting all data acquisition systems through a central data processing system, you can digitize your company and start making data-driven decisions.

Guiomar Fernandez