Azure Cognitive Search is a cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. It has a rich set of features, including instant scaling, AI integrations, and complete code flexibility, making it worthy of consideration beyond its initial developer focus. As a platform, Azure Search is a tool in the toolbox for creating complete AI-driven search solutions, and an excellent one at that. Based on the needs of the implementation, Azure Search may be a better platform than Microsoft 365 or Microsoft Search.
Some examples of these capabilities are:
- Azure Cognitive Search indexes could be in the billions of items versus O365, which would be in 100s of millions
- Direct control of relevance ranking, query processing, and indexing
- No limits on size of documents or number of items indexed
- Overcome limits of SharePoint Online search (such as files over 10 GB or large amounts of data outside of Office 365)
- Azure Cognitive Search is much faster than SharePoint search, both in indexing and query performance.
- Sophisticated search capabilities: including real-time indexing, geosearch, pattern search, and search on machine data.
- Built-in integration with Cognitive Services e.g. language detection, image tagging, named entity extraction
- Azure Cognitive Search is a fully managed service so scaling it up or down is far easier than SharePoint. It’s also easier to set up than Elasticsearch or SharePoint.
Requirements like control and openness of the solution, or infinite scalability to potentially billions of records, or specific latency and query performance needs, all point to Azure Search as a key platform to leverage. We see use cases like those emerging more and more, and with them a rapid acceleration of Azure Search as the key enabling engine of enterprise search solutions. To build on this, here are three recent examples that we’ve worked through with our customers that are worth sharing.
Use Case 1:
Azure Government Cloud
The primary driver for this customer came from two areas- the first being the retirement of the legacy Google Search Appliance and the second being the adoption of Azure’s government cloud. The customer found themselves without a supported search solution, and a requirement to drive cloud resources into the Azure Government Cloud, allowing them to meet the strict security requirements that GovCloud delivers. The CIO of this organization spent weeks working with consulting organizations to try and identify potential solutions. With the assistance of Microsoft, who knew that Azure Search could be a key component in the solution with its GovCloud support, we were brought in to provide details on our software-based solution to solve their enterprise search problem. Within the span of 15 minutes, the CIO had heard enough of how the combination of Azure Search and BA Insight’s connectors and AI-driven SmartHub platform could rapidly solve this problem, and he was ready for a quick POC to see it work. Fast forward two weeks later and the solution was up and operational in GovCloud, showing a complete set of enterprise search capabilities. This customer is looking towards a full enterprise deployment in early 2020.
Use Case 2:
Scale at a National Bank
The most interesting part of the use case brought to us by this national bank was the merging of technology assets from two behemoth tech companies- Microsoft and IBM. This customer had legacy enterprise data stored firmly within products delivered by IBM, coupled with an equal amount of core enterprise data stored within the Microsoft technology stack. Oh, and did I mention that in total, they have 4 billion items? They knew that the ability to search equally across these disparate data sets was a key missing capability plaguing their users. They also knew that managing that level of scale for the search solution would be a key factor in their technology selection. As strong Azure users, the idea of Azure Search as a solution was of great interest to them. We were able to show them how our software solution would allow them to create a single cloud-based Azure Search index, incorporating both their IBM technology and Microsoft technology. We then demonstrated how the power of normalizing the metadata within this index and leveraging Azure Cognitive Services to extract hidden information via AI and machine learning would allow them to deliver a level of personalization and relevancy not previously believed possible. The flexible, mobile-ready delivery interface we showed was icing on the cake, firming up the complete vision of search. This customer is rapidly moving through this process and should see real payback on this investment in 2020 and beyond.
Use Case 3:
Enterprise Search in Pharma
Solving search problems in the pharmaceutical space has become a real sweet spot for BA Insight, as the problems faced in this industry are ripe for usage of advanced capabilities available with new AI and ML technologies. This customer is really driving for a competitive edge based on the troves of data that they manage, but they have not been able to leverage it into a real actionable asset to their researchers and knowledge workers. They are convinced (and rightly so!) that if they can find a way to automate intelligence, correlation, and access across all their data, then their ability to respond and innovate will increase dramatically. With this focus on emerging technologies and AI-driven intelligence, they headed down the path of finding the right technology to meet their needs. They knew that a cloud-based solution was the likely answer and settled on Azure Search relatively quickly. Not long after deciding that path, they realized that their vision required more than just an index. They also needed scalable ways to manage and ingest content, extensibility to leverage multiple AI processing engines and medical specific solutions, and a fully configurable user interface to drive multiple disparate departmental solutions under one manageable technology platform. This is what was brought into the conversation by BA Insight. In rapid fashion, we demonstrated the ability to ingest all their disparate data, covering systems like Veeva Vault, SQL, and internal web. We then demonstrated how AI and medical domain processing of this data could uncover hidden intelligence, and how this all could be delivered in an easy to deploy and configure UI framework. All that is left now is a rapid pilot, followed by a full enterprise deployment.
It is great to see Azure Search garner the recognition it deserves as a central piece in the complex enterprise search space. These use cases just scrape the surface on how a cloud-based, easy to deploy and infinitely scalable index can solve real problems and deliver significant ROI to organizations, which is why we developed a product specifically for this platform.