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Stéphane Recouvreur 03 Aug 2023
The vast majority of search solutions available on the market cater primarily to the e-commerce industry.
Why? Money.
E-commerce is a gigantic sector, pulling billions of dollars in revenue every year, that was the first to see the importance of offering advanced search capabilities to customers. Search software providers were simply too eager to fill that need, focusing all their attention and efforts to the detriment of others.
The problem of course is that not everyone is running an e-commerce business. Some legitimate industries (governments, education, etc) with large amounts of information have been neglected over the years.
While every enterprise search engine is busy offering solutions catering to structured data sets (remember this term — we'll get back to it later!), there is a growing need to offer search solutions specifically designed for unstructured data in order to improve information discoverability and user experience further.
Structured data refers to information that comes in an organized and predefined format — think rows and columns in a spreadsheet or a relational database.
Structured data comes with a predefined data model, meaning that information must fit within a predefined structure or categories (.i.e. size, color, availability, etc). Such data can commonly be found in data warehouses, data lakes, or simple spreadsheets. These databases help organizations categorize and store product and service information in a systematic manner – optimizing search experiences for customers.
Let's take the e-commerce example.
Products are organized within a structured database so that each listing contains specific fields such as product name, description, price, size, color, availability, and location.
By applying filters, customers can easily narrow down their search results to find products that meet their specific requirements, enhancing the overall shopping experience.
Structured data also lends itself to personalized digital experiences, as it can be presented across different channels and platforms to customers, based on third-party or first-party data insights.
There are four main challenges and limitations that come from building a search platform that prioritizes structured databases.
Unstructured data refers to information that is not organized in a pre-defined format. It includes a wide range of different data types, including text, rich media, document collections, social media posts, analytics, Internet of Things data, and more.
Unstructured data is often complex and variable in nature, while also accounting for the majority of data managed by organizations across different digital ecosystems. Importantly, it can’t be easily searched with a structured query language.
The majority of data created today is unstructured and comes from a wide range of sources.
Unstructured data search like our Squiz Search capability, goes through a complex process to turn disparate data into a searchable index, which is simply not possible with structured data search solutions.
When using an unstructured data search platform, indexing is crucial for centralizing and surfacing relevant content from disparate sources. It integrates search with any database, directory, social media, or website via API or automated crawling.
The steps a search platform takes during indexing include:
After a search platform has indexed data from all sources, it can then search this unstructured data during a query. The process generally includes:
Metadata is a way to search unstructured data based on other criteria like document type or its source/platform.
Not all your data and information live neatly in a structured way.
According to Tom Foremski, in today's digital landscape, "every company is a media company." In essence, this implies that each organization will generate unstructured data, encompassing articles, videos, podcasts, and more. As a result, they face the critical challenge of effectively presenting this content to their target audience.
It doesn’t matter what industry or sector your content lives in – even e-commerce – if you want to surface relevant content to your end users every time, then moving to an unstructured data search tool is a must.
Solely focusing on structured data search exposes you to unanswered queries, simply because they don't fit the mold of your content, with users feeling frustrated and seeking answers elsewhere.
Technologies, such as machine learning and AI, allow automated, precise management of unstructured data, with these advances developing at an incredible rate.
With tools like natural language processing (NLP), search platforms increasingly possess the ability to comprehend text with machine learning algorithms in a manner akin to human understanding and create seamless, personalized digital experiences that start to blur the barriers between search and other experiences.
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