I earned much of my software development skills in architecting, building, and supporting customer-facing search applications. I used many different search technologies over the years, and they all had similar development patterns. You had to set up the infrastructure, load data, configure search indexes, and develop search experiences.
The work to load the data, configure search algorithms, and develop apps was just the beginning. Tuning relevancy was a tug of war between stakeholders with different views and requirements on the heuristics. Each new rule often required revisiting how content was tagged, enriched, or indexed. We had additional work to scale the infrastructure, add new data sources, and reconfigure search interfaces to support growth and new user personas.
Much has changed and improved since those first-generation search technologies, and today’s modernized search platforms Make it easier to build the infrastructure, integrate with content sources, and improve relevancy. There’s also a strong business case to modernize search platforms to improve customer and employee support.
Yet, I find many development and data science teams focus most of their data efforts on the dataops, machine learning, and data visualizations on structured data sources. Searching unstructured data, such as business documents, websites, XML repositories, or other textual data fields often takes a back seat because of the added tech and skills needed to search them well.
For this post, I consulted with three experts on why IT, digital experience, and data teams should consider modernizing their search technologies.
Simplify experiences, dev tools, and system administration
Mark Floisand, senior vice president of product and marketing at Coveo, shares one of the problems with legacy search implementations that can be more easily solved today. “Enterprise search technology has typically been bought or built within departments, siloed and only with individual departmental goals in mind. Instead, you can deliver enterprise search, website search, and in-app search using a single, unified platform,” he says.
Centralizing on a single platform to provide a common user experience, developer tools, and administrative capabilities can impact several departments. Floisand continues, “Unifying search simplifies IT’s management and internal support burden. IT can support all internal departments’ requests with the right platform, whether teams are focused on customer acquisition, conversion, and retention or on helping other employees be more proficient.”
One way development teams can support multiple search experiences is with headless search, especially when the workflow and user experience require personalization. Developers can then use lighter-weight low-code and no-code interfaces to embed search into customer support and employee workflow platforms.
Improve employee experiences to support hybrid work models
The search capabilities bundled with enterprise portals can be sufficient for smaller companies, especially if they have less frequent communications and fewer tools to integrate. But for larger companies with multiple departments and many information sources, centralizing information from multiple content management systems, customer relationship management systems, and other software-as-a-service tools leads to an information-rich experience.
A comprehensive search experience should be a primary tool for employees to find documentation, subject matter experts, and information generated in workflow tools. This capability is critical for teams in a hybrid work model, and it’s one step in creating a virtual water cooler. It can help employee productivity and reduce the stress of finding the key information for their objectives.
Arvind Jain, CEO of Glean, agrees. “Finding what you need at work is complicated, especially as companies grow, as knowledge becomes fragmented across an array of apps and people.”
Of course, building a personalized, relevant, and up-to-date search experience wasn’t trivial before we had cloud, SaaS with APIs, integration platforms, and machine learning. Poor data quality creates a poor search experience that employees must work around.
Jain says, “Building a great enterprise search experience requires solving previously insurmountable challenges, like deeply understanding how employees work and what information matters to them. Advances in technology have helped unlock radically better solutions that allow advanced relevance models to be built without the need for constant manual tweaks.”
Expand search across more content sources
Eudald Camprubí, CEO of Nuclia, highlights search engine capabilities that can expand a company’s scope and scale. He says, “Between 80% to 90% of any company’s data is unstructured. Data lies in different data sources and is in different formats and languages. Ingesting, processing, and indexing this data is among the biggest challenges in search today. Only AI-powered search engines for unstructured data will help enterprises overcome this chaos.”
Search engines with built-in and configurable machine learning algorithms provide significant advantages for companies with multiple apps and user personas searching large information repositories. Search platforms complete on the quality and scale of their machine learning capabilities, including algorithms for entity enrichment, automatic relevance tuning, and recommendation engines.
Why prioritize search platforms?
Here are five more considerations of why organizations should modernize search platforms and experiences:
- Modern platforms go beyond keyword interfaces and simplify the user experience with natural language querying.
- Businesses supporting multiple search technologies should be able to find cost savings by consolidating to a single enterprise search platform.
- Devops teams can reduce technical debt by consolidating to one platform, developing a service layer, and converting proprietary integrations to the search platform’s out-of-the-box ones.
- Upgrading apps by modernizing search experiences has the potential to improve performance, support mobile interfaces, address accessibility, and personalize the experience.
- Search engines with APIs can be a back-end repository to data science, analytics, and data visualization tools, effectively presenting unstructured data as a structured data source.
If your devops teams support legacy search indexes, it may be time to dust off the cobwebs and consider upgrading. Modernized platforms offer significant benefits to businesses, users, data science teams, and technology organizations.