Many companies store large quantities of files and documents on network drives. The challenge: network drive searches are often tedious and time-consuming. Although network drives are excellent for storing data, they are not always ideal for finding it again. In this blog post, you will find out why network drive searches are often inefficient and how you can improve them – and what role AI-supported search solutions play.
Table of Contents
What is the network drive search?
Drive search refers to the targeted search for files and documents within a network drive. Various search parameters can help to find relevant content more quickly. In many companies, the network drive search is carried out either via operating system functions or special third-party software. An optimized drive search should be fast, reliable and efficient in order to increase productivity.
Why is network drive search so challenging?
Network drives provide a central repository for files, but are often not optimally organized. Over the years, most companies have stored more and more information on drives. Over the years, dozens of duplicates and outdated information have accumulated – which are nevertheless often needed by employees. This creates various challenges for employees on a day-to-day basis, such as
- Unstructured data: Over the years, countless documents are created without a clear structure. Typically, each department builds up its own “filing logic”.
- Lack of indexing: Many network drives do not have a powerful search function that indexes and searches files efficiently. This makes searching very time-consuming, especially for data volumes that have grown over the years.
- Slow search speed: Without optimized indexes, a simple search query can take a long time.
- Lack of semantic search: Traditional search functions are based on exact terms and do not take into account the context of the content. This makes the quality of the search unsatisfactory for the end user.
- Different file formats: Documents are available in different formats (PDF, Word, Excel, emails), which further complicates the search.
- No preview function: If hits are found, you first have to open the entire document and then search in the document with “Ctrl + F” instead of quickly and easily opening a preview.
All of this makes it very challenging for employees to obtain the relevant information quickly. As a result, this leads to poor knowledge levels among employees and poor decisions, as they often don’t have the information they need.
How can you improve the drive search?
From the challenges mentioned so far, which many companies can certainly identify with, there are a few tips and tricks on how to optimize the network drive search. These include, for example:
1. Creating a better data structure
Uniform naming conventions and clear folder structures help to improve the drive search. Metadata can be used to make documents easier to find.
2. Enable and optimize indexing
Many operating systems and third-party tools offer the option of efficiently indexing network drives. Check that indexing is updated regularly to ensure faster drive searches.
3. Use filters and advanced search operators
Metadata such as creation date, author or file type help to make the network drive search more targeted.
Example of the use of filters in the drive search
4. Formulating targeted search queries
Get to know the advanced search operators of your system. For example, many search functions support wildcards (*), logical operators (AND, OR) or filters (date, file type).
Optimize drive search with an AI search
Even if you follow all the tips, you will only be able to optimize the drive search to a limited extent – ultimately, the search algorithms are not designed for the often too large amounts of data. There are now better, AI-based solutions such as amberSearch, which not only search documents intelligently, but can also write summaries or enable employees to chat with the documents. Some of the advantages of such a solution are, for example:
- Semantic analysis instead of pure keywords: amberSearch’s AI builds its own index that semantically links all content in SharePoint. This allows documents to be found that match the content, even if the exact search terms were not used.
- Interactive search results: Employees can interact with the search results – similar to a chat. This allows summaries or automatic responses to be generated based on internal knowledge. This function not only makes it easier to find relevant information quickly, but also supports efficient further processing of the data.
- Integration of multiple data sources: amberSearch goes beyond a pure Network drive search. It also includes content from other systems such as Outlook, Teams or SharePoint. This creates a uniform, centralized information landscape that significantly simplifies access to company-relevant data.
- Automated monitoring and maintenance: The AI continuously analyzes the search index and adapts it to changing data volumes and structures. This reduces the manual maintenance effort and ensures that the search results always remain up-to-date and relevant.
If you are wondering what such an AI search looks like in practice, you can watch this video for an explanation. We also offer interested parties the option of trying out amberSearch for themselves via our online demo to get a feel for the technology.
Conclusion
The classic network drive search is often time-consuming and inefficient. A better data structure, optimized indexing and targeted search operators can improve the drive search. However, for truly efficient search processes, an AI-supported solution is the best way to optimize network drive searches. Companies that use modern search technologies save time and significantly increase their productivity. Interested parties can use our ROI calculator to work out how much this actually means. Would you like to find out more? Then send us an inquiry now and we will be happy to help you: