A number of factors contribute to data silos. Decentralized IT purchasing decisions give business units and departments the flexibility to implement the applications and databases they need. However, these systems are often disconnected from other parts of the IT environment. The problem is particularly acute when business units are operated as distinct entities, when the business is growing, and when there’s no unified policy governing data management.
There are several types of centralized data repositories that can help break down silos and facilitate AI and analytics. Choosing the right one depends on the type of data to be stored, managed and analyzed.
Challenges Associated with Data Silos
It can be difficult to detect data silos given that they’re inherently isolated. Often, organizations first recognize them when they prepare to implement AI and analytics tools. However, there may be other clues. Incomplete or out-of-date datasets and inconsistent reporting may signal a problem with data silos.
AI models must be trained with high-quality, consistent data. When they can only access a portion of organizational data, they are unable to analyze complex relationships. They have limited ability to identify patterns when data is fragmented across various systems, and cannot learn to make accurate predictions. They may even produce inaccurate results, leading to poor business decisions.
Data silos create challenges beyond AI implementation. They may reduce productivity and the quality of customer service. Business leaders aren’t able to manage effectively or capitalize on new opportunities. When users don’t trust the quality of data, they won’t use it or take advantage of its potential business benefits.
Types of Data Repositories
AI, analytics and business intelligence applications need access to diverse datasets across the IT environment. Data must be aggregated and stored in a common repository without disrupting the operation of the source system.
A data warehouse uses a hierarchical system of files and folders for querying and analyzing data, making it most suitable for structured data. However, as much as 90 percent of information is stored in an unstructured format, such as text documents, videos, email and chat sessions. A data lake is more suitable for unstructured data because it has a flat architecture.
A newer approach is the data lakehouse, which combines characteristics of both a data warehouse and a data lake. A date lakehouse uses a so-called medallion architecture to integrate, filter, clean, augment and aggregate data. Structured, unstructured and semi-structured data moves through multiple layers that incrementally refine, improve and enrich it. This enables the data lakehouse to deliver continuously updated data to AI, analytics and other applications.
Developing a Data Governance Strategy
Regardless of the data repository, it typically makes sense to migrate data to cloud object storage. The cloud can store large volumes of diverse data types cost-efficiently and scale on demand as data volumes grow. The major cloud providers also offer an array of features for managing and securing data.
Of course, moving data to a new repository is only part of the process. Organizations should start by documenting data assets and data flows and develop a data governance program that ensures data is collected, stored, managed and used according to best practices.
According to research by IDC, data silos and poor data governance can cost an organization as much as 30 percent of annual revenue. Technologent’s data management practice is here to help you eliminate data silos and implement a data repository to support your AI and analytics objectives.
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