Your organization could be using multiple business software tools, such as enterprise resource planning (ERP) and customer relationship management (CRM) systems, to store data and automate processes. These disparate systems can lead to unstructured and siloed data, making it hard for your employees to find the information they need to complete their tasks. It’s also difficult to analyze this type of data and derive insights from it.
Insight engines can search, discover, and analyze data from both internal and external sources, including software systems, to help users quickly find the information they need. These are intelligent enterprise search engines that use machine learning (ML) and natural language processing (NLP) to understand unstructured data (text, videos, or images) and deliver complex search queries in an easy-to-understand manner.
A wide range of insight engine platforms is available on the market, and choosing the one that best meets your needs and budget is important. This guide will help you understand the different factors you must consider when shortlisting an insight engine.
Here’s what we'll cover:
What is an insight engine?
An insight engine, also known as cognitive search or enterprise knowledge discovery and management software, is an intelligent enterprise search platform that uses artificial intelligence (AI) to search and extract information from internal and external data sources. It uses NLP and ML to analyze large volumes of complex data, both structured and unstructured, and deliver the most accurate results to user queries.
Businesses use insight engines to find the information (media content, documents, raw data, etc.) present in their data sources—be it the internal digital workspace or public-facing sources such as the company website.
Analytics and insights in Lucidworks Fusion (Source)
Common features of an insight engine
Software features vary by product or vendor, but most insight engines platforms have the following features in common:
||Search and find the most relevant information from the databases used in your organization. The software uses AI technologies, such as NLP, ML, and semantic search, to provide a single point of access to enterprise content sources.
||Use AI and ML capabilities to prepare, analyze, and visualize data to gain insights faster.
||Combine various data sources, including business and cloud storage apps, into one integrated space for data analysis and processing.
||Analyze data and convert it into a format that can be indexed and searched. The process includes cleansing, normalizing, calculating the search relevance, filtering, sorting, and scoring the data by relevance.
||Get suggestions on the best course of action or the most relevant information as you use the system.
||Gather, organize, share, and analyze knowledge resources, including documents and data, to find relevant information in real time.
||Use NLP and text analysis to interpret and classify the sentiment (positive, negative, or neutral) of customer feedback data.
||Assign system access permissions (viewing, suggesting, or editing rights) to users based on their job roles or departments.
||Use data mining, statistics, ML, and AI to analyze data and user behavior to make usage predictions.
What type of buyer are you?
Before evaluating software, you should assess the type of buyer you are. The majority of buyers in the insight engines market belong to one of these categories:
- Small and midsize enterprises (up to 500 employees): Compared to the big players, small and midsize businesses (SMBs) have lower volumes of self-generated data. Most firms in this category are new entrants or still growing. Therefore, implementing an insights engine will help make their business information easily accessible to all employees.
These buyers should opt for a data engine that can scale as their business grows. A scalable tool will allow SMBs to accommodate future data growth, get data-driven actionable insight, and access user-focused recommendations. Also, the insights generated from the tool can help SMBs streamline their processes rapidly.
- Large enterprises (over 500 employees): Large enterprises have multiple data sources and tend to generate large volumes of structured and unstructured data. For users in this segment, it’s a challenge to find relevant information, as the data is either lost or hidden. They need an insights engine that can not only collate data from disparate sources but also provide smart insights to support personalized search and discovery for users.
A fully featured insight engines platform with access management, configuration management, and cross-platform compatibility (websites, customer-facing channels, enterprise search, etc.) would be suitable for these buyers. It’ll help them search and analyze complex data sources as well as prevent unauthorized data access.
Benefits of an insight engine
Here are some benefits you can expect from replacing your traditional enterprise search engine with an insight engine:
- Efficient knowledge management: An insight engine lets you set up centralized knowledge infrastructure in which all the data within your organization can be collected, indexed, and tagged based on user search queries. The software uses ML to extract and bundle enterprise knowledge in a central database that offers a 360-degree view of company-wide data. This ensures efficient knowledge management and provides employees a complete understanding of your business, customers, processes, and much more.
- Improved productivity: An insight engines platform can connect to multiple internal or external sources and make all business data available on the same platform. Its single search functionality eliminates the need to switch between different applications. By consolidating data from various systems, it helps streamline processes and save time, leading to increased employee productivity. It also provides benefits such as better collaboration between teams or individuals working on the same project and prevention of data redundancy or duplication.
Listed below are some considerations to keep in mind when selecting an insight engines platform:
- Functionality and scalability: Look for an insight engines tool that can process and analyze complex data from various sources and return accurate results in real time. Also, take data storage into consideration. The software you choose should have the ability to scale storage to accommodate your evolving business needs.
- Security: When using an insight engine, data security should be your top priority. Look for a software tool that offers access controls and protects data as it moves from different sources to the software. Also, ensure the tool complies with industry-security standards, such as the General Data Protection Regulation (GDPR), to help prevent data breaches.
- Integration with data sources: The insight engine you select should play well with the data sources available at your organization. If the software is unable to integrate with the sources, your users won’t have access to complete information. Therefore, take the integration aspect into account when making your purchase decision.
Market trends to understand
Here’s a recent trend in the global insight engines market that you should be aware of:
- Insight engines are supporting the digital transformation of businesses. Intelligent enterprise search engines provide Google-like search functionality for information resources within an organization. They offer insights tailored to the needs of each user. This helps employees understand business data and derive relevant information to work smarter and deliver more personalized experiences to customers. Relevant and timely insights from enterprise-wide data also help drive product development changes according to customer needs. This search capability has become the core of businesses looking to provide more intuitive, personalized, and relevant digital experiences.
Note: The application selected in this article is an example to show a feature in context and is not intended as an endorsement or a recommendation. It has been obtained from sources believed to be reliable at the time of publication.