Now’s The Time To Get Excited About Cognitive Search
Digital Marketing Manager|Kerv digital
Published 06/07/22 under:
Cognitive search employs artificial intelligence (AI) to extract relevant information quickly and efficiently from disparate data sets.
Before we explain why you should be so excited about Cognitive Search, we should probably take a minute to explain what it is.
What Is Cognitive Search?
Cognitive Search (what was Azure Search) is the next generation of search, utilising cutting edge artificial intelligence (AI) to both exponentially improve users search queries and aid in the extraction of relevant information from disparate data sets.
It’s [Cognitive Search] search capabilities go far beyond what a traditional search engine is capable of though, by bringing together various data sources whilst also providing automatic tagging and personalisation of information for your marketing team to use, vastly improving how an organisation can discover, collate access their data.
How Is Cognitive Search Different From Previous Search Iterations?
At it’s core, Cognitive Search is based on the same Lucene engine as many other platforms, but Microsoft has added some serious enhancements on top to help you build intelligence into search apps quickly. The default query engine provides many advantages over plain Lucene queries, with built in NLP based scoring across text fields.
The real power comes in the form of ‘Cognitive Skills’ which allow you to build pipelines for cracking and extracting structured data from your search records before they are indexed. There are some configurable skills which allow you to plug directly into Azure Cognitive Services for common tasks around image analysis and text recognition, but if you want to go further, you can include any kind of web service in the pipeline. This is a powerful option, as it enables you to run custom Machine Learning workflows over your search data without having to worry about writing lots of custom data pipelines.
What Are The Benefits Of Cognitive Search?
Whilst there are many benefits to Cognitive Search, the main one in our opinion will be the knowledge discovery you can leverage out of your data.
It both improves the relevance of information extracted from data sets whilst also improving the performance of query responses. You can also enable the Knowledge Store, which will store a view of all the metadata extracted in your pipelines, so you can gain additional insight into the data you’ve indexed.
Cognitive search makes it really simple to write engaging, semantically rich search applications with minimal effort. I see it as a set of modular tools which can be combined with other ML services to allow users to query data in ways which were previously very complex to engineer – Ed Yau – Principal Architect – Data Solutions, Kerv Digital
Why Is Cognitive Search Important?
Due to the constant advancements in Machine learning and Artificial Intelligence technologies, systems using either a keyword-based search or a traditional enterprise search can no longer keep up with the amount of data that organisations hold and process.
In fact they’ve actually started to hinder organisations that need to quickly process large amounts of data by constantly returning too many results for search queries, returning results that are often irrelevant, incomplete or too vague, thus wasting employee time as they then have to manually sift through the returned results for what they were actually looking for.
What’s the point of making a search if you then have to search through the search results?
That’s where Cognitive Search steps in.
With Cognitive Search technology, the AI behind it is able to delve deeper and understand the users intent, pull advanced meaning from content and learn from past searches to consistently provide concise and relevant search results.
Other benefits to Cognitive Search are:
- Increased Productivity: Cognitive Search enables a single search functionality which means users no longer need to switch between apps whilst looking for results.
This save time not having to re-enter log-in credentials, saves time switching between apps and saves time by receiving better search results the first time round.
- Employee Satisfaction: If your employees are spending their entire day searching through your databases, then sifting manually through returned search results for what they actually needed they’re going to be feeling pretty frustrated.
Doing away with that will raise employee satisfaction levels, make them more productive as a result and as a bonus, increase your staff retention rates.
- Business Operation Costs Are Lower: Not to belabour an obvious point but increasing productivity will decrease your organisations operational costs as time and resources are saved whilst gathering data.
How Does Cognitive Search Work?
The great thing about Cognitive Search is that it can be built on top of existing enterprise search infrastructures and not having to re-build your entire IT architecture is always a nice bonus!
Cognitive Searches’ AI tech will just layer on top of your current search functionality, allowing your staff to find relevant information across all of your organisations data.
Where it really comes in to its own though is it’s use of NLP (Natural Language Processing) to analyse and decipher your organisations unstructured data, heterogenous document data, and rich media like video or images to return meaningful search results.
It’s Machine Learning then learns from your employee’s frequent searches, returning even more relevant results as it becomes more embedded in your business processes.
Depending on the size of your organisation or the amount of data you hold, Cognitive Search is either a ‘would be nice’ luxury or a ‘must have’ necessity.
If you’re bringing in hundreds of new customers daily it won’t be long before your CRM becomes bloated and deriving actionable business intelligence from all that data will be nigh on impossible without the help of Cognitive Search, especially as your parse your data up into smaller and smaller cohorts or segments.
The same can be said for organisations that attract relatively fewer clients, but those clients hold a lot of data… the health or legal sectors for instance.