Intelligent search solutions for organizations are leading to integrate intelligent search
One of the biggest problems facing large, data-driven organizations today is how to make the leap from gathering and storing data to efficiently analysing and making use of it. Gathering data is all well and good, with many services available for data storage. Effective use of that data depends not only on storing it but also on being able to retrieve it and make use of it on demand. Making these vast data sets accessible and easily searchable for technical and non-technical users alike is the goal of Intelligent Search. Read on to learn 5 reasons why your organisation may benefit from integrating an intelligent search solution.
Easily classify and categorise heterogeneous data sets
One of the biggest concerns for many businesses looking to integrate a new technology is the onboarding process. This can be particularly painful for data processing and analysis, as it often means having to format data into a specific format used by the new technology. In the case of intelligent search solutions, this isn’t always necessary.
Intelligent search solutions are capable of data discovery across disparate heterogeneous data sets without the need for any formatting or prep work beforehand. Instead, pre-built connectors and converters are used that collectively allow for data in different file formats to be discovered, indexed, and made easily searchable automatically. This removes what can otherwise be a significant headache for many during the onboarding process.
Analyse and understand connections between data
Data that is gathered but not analysed is wasted data. Market research by IDC suggests that the vast majority of data gathered and stored is unstructured – as much as 90%. Analysing this data and discovering connections within and between data sets leads to richer data and makes searching for data more meaningful.
The intelligent search uses machine learning and natural language processing to develop a deeper understanding of data. Patterns emerge from large data sets that can be identified and given connections, creating valuable relationships within data sets. As an example, a name mentioned across several different data sources can be linked together and have a profile developed, which can also be linked to the types of topics covered in those documents, leading to the creation of a searchable profile for an individual.
Allow users to search for data naturally
Many search solutions rely very heavily on users knowing how to search effectively in order to get the best quality results. Some search engines use a GUI interface to help users construct more specific searches, or some rely on the use of keywords and boolean operators to find particular information. In either case, this puts up a barrier to data accessibility for non-technical users.
Intelligent search solutions use Natural Language Processing (NLP) to both understand and interpret user search terms and analyse data. This means that users can write search terms using much more natural expressions as if they were describing what they wanted to another person. Robust machine learning mechanics continually discover and refine patterns between what users search for and search results that allow it to continually learn from previous searches and improve future search results.
Break down data silos and rescue orphaned data
Many organisations rely on a variety of different tools and services as part of their workflow. These tools and services can vary between different departments and it is often the case that data contained within is not easily accessible outside of a particular tool or the department that uses it.
Uniting this data spread across different tools and departments helps break down these data silos and turns specific employee knowledge and department knowledge into company knowledge made searchable and accessible to anyone who needs it. Organisations do not have to rely on department-wide meetings in order to cross-communicate and it makes sharing relevant data between departments much easier.
Boost employee productivity
All of the above lead to the general conclusion that integrating intelligent search into a business workflow provides easy access to heterogeneous data sets across an organisation enriched by connections and insights discovered based on pattern analysis. All of this has the simple goal of boosting employee productivity. No longer do employees have to know what they are looking for or spend time trying to find the right keywords.
Intelligent search makes large data sets accessible and user-friendly. Employees, both technical and non-technical, can search an entire organisation’s data just as they would search the web. This means less time spent searching and more time focused on their important work tasks. Well-informed employees can make decisions faster, provide more accurate information to customers, and in general enables them to provide more value to the company.