Is Microsoft Syntex a breath of new life for your outdated SharePoint document center or intranet?
At the recent Microsoft 365 conference, the Skypoint team got a first-hand look at several new product feature releases, most notably the launch of Microsoft Syntex.
Syntex effectively integrates AI into both your SharePoint content and workflows by:
- Enhancing Content: understand and assemble content with AI powered summarization, translation, auto-assembly, and annotations integrated into Microsoft 365 and Teams.
- Connecting Content: Discover and reuse content with AI powered search, eSignature and integration into business workflows like contracts and invoice management.
- Managing Content: Analyze and protect content through its lifecycle with AI powered security and compliance, backup/restore and advanced content management.
With all the hype around AI, what makes Syntex the game changer for your M365 ecosystem?
Syntex fills a previous gap in SharePoint’s tagging and metadata capabilities. It also supercharges SharePoint to handle both structured and unstructured data, opening a new world of possibilities.
Syntex, combined with SharePoint and Power Platform, now presents a viable, low-code back-end alternative to common database solutions like Azure SQL. Previously, the Skypoint team was judgmental of Microsoft for making their “free” back-end for Power Platform SharePoint… but now the tides have turned. Did Microsoft have this coming all along, or was it just dumb luck?
While Azure SQL excels in managing structured data, providing a reliable platform for storing and querying data, it’s not designed to handle unstructured data, like your organization’s documents and content.
The prevalence of this unstructured data plays an increasingly crucial role in many business processes, requiring a more comprehensive solution.
The introduction of Syntex opens the door to new low-code “integrated back-end solutions”, or commonly known as your legacy SharePoint intranet or file repository everyone loathed, offering unparalleled capabilities for document management, content understanding, and process automation. It lowers the barriers, enables businesses to embrace low-code options, and empowers its employees to harness the full potential of their data. It almost seems like the golden age of SharePoint is back!
Structured Data Vs. Unstructured Data
To build the case for this new low-code back-end alternative, it’s important to first understand how structured and unstructured data play deciding factors in whether this low-code back-end scenario is best for your organization.
| Structured Data | Unstructured Data | |
| Definition | Well-organized data with a predefined schema and format | Data that lacks a predefined structure or format |
| Storage Format | Tabular (e.g., rows and columns) | Text, images, audio, or video files |
| Data Examples | Customer information, financial records, inventory data | Emails, social media posts, documents, images, videos |
| Organization | tables, rows, and columns | No organizational structure |
| Schema | Predefined schemas and data models | No strict schema or predefined structure |
| Querying and Analysis | Supports complex queries, aggregations, and reporting | Challenging to perform complex queries and analysis |
| Automation and Integration | Easier to automate and integrate into workflows and systems | Requires advanced techniques for automation and integration |
| Extraction of Insights | Well-structured data allows for easier data analysis | Requires advanced techniques for extracting insights |
| Search and Discovery | Limited search capabilities based on specific fields | Relies on text search and natural language processing |
| Processing | Easier to process, manipulate, and transform | Requires techniques like text mining and machine learning |
| Example Technologies | Relational databases (Azure SQL) | Microsoft Syntex, Azure Cognitive Services, Elasticsearch |
“Structured data” is organized and stored in a predefined format within a database or a file. It follows a specific data model and has a well-defined schema, which defines the structure, data types, and relationships between the data elements. Structured data can be easily organized, processed, and queried using a database management system (DBMS) with structured query language (SQL).
A spreadsheet containing columns for customer names, addresses, phone numbers, and purchase amounts is an example of structured data. Each piece of information is stored in a specific field within the table, making it easy to search, sort, and analyze. This is where Azure SQL takes the cake.
“Unstructured data” does not have a predefined structure or organization. These are your text documents, images, videos, audio files, social media posts, emails, and other content. Unstructured data does not fit neatly into traditional rows and columns like structured data, making it more challenging to organize and process.
These types of data contain information but lack a predefined structure, making it difficult to extract meaning or perform traditional queries without additional processing. This is where back-end solutions like SharePoint really shine, but still need help to tag and process at scale.
Different approaches, tools, and even skillsets (people) are required to handle each type of data effectively. While structured data fits well into relational databases and can be easily queried, unstructured data requires additional processing techniques, such as natural language processing (NLP) or machine learning, to extract meaningful insights.
Organizations often deal with a combination of structured and unstructured data, requiring appropriate strategies and technologies to handle and derive value from both types of data. This is where Syntex plays a huge factor in the back-end infrastructure.
How Microsoft Syntex Solves SharePoint’s Structured Data Shortcomings
The introduction of Microsoft Syntex helps solve SharePoint’s structured data shortcomings to some extent. While SharePoint is primarily designed for managing unstructured data, Syntex introduces capabilities that enable the extraction and management of structured data within your SharePoint files.
Here’s how Microsoft Syntex helps address SharePoint’s structured data limitations:
- Content Understanding and Extraction: Microsoft Syntex uses advanced artificial intelligence (AI) and machine learning (ML) capabilities to analyze unstructured content within SharePoint, such as documents, images, and forms. It can extract structured data from these unstructured sources, automatically identifying key entities, relationships, and attributes within the content.
- Metadata Enrichment: Syntex can now automatically assign metadata and tags to documents based on the extracted structured data. This means your organization’s content can now be categorized and organized, making it easier to search, filter, and sort documents based on their properties.
- Custom Entity Extraction: Syntex allows organizations to define custom entities specific to their business domain. This capability enables the extraction of structured data elements that are unique to an organization, helping to capture important information from unstructured content and convert it into structured data.
- Integration with Power Platform: Microsoft Syntex integrates seamlessly with the Power Platform, including Power Automate, Power Apps, and Power BI. This integration enables organizations to create workflows, build custom applications, and generate visualizations based on the structured data extracted by Syntex.
By combining the power of Microsoft Syntex with SharePoint, supplemented by Power Platform, organizations can easily bridge the gap between structured and unstructured data.
Syntex helps unlock structured data hidden within unstructured content, enhancing SharePoint’s capabilities to handle and manage structured information effectively.
While Syntex provides valuable tools for extracting and managing structured data, it does not transform SharePoint into a fully-fledged relational database management system like Azure SQL. For complex structured data scenarios, Azure SQL might still be a more suitable option.
So how do you determine if this low-code back-end combination is right for your organization?
Azure SQL versus SharePoint and Syntex: Which is right for you?
Organizations need to carefully evaluate variables like data storage needs, workflow processes, collaboration requirements, compliance obligations, and overall business objectives to determine the most suitable solution for their unique circumstances.
Sometimes, a combination of Azure SQL and SharePoint plus Syntex may be the optimal choice, leveraging the strengths of each platform for different aspects of the organization’s data and content management needs.
Here are some general guidelines to help inform your decision:
| Organization Type | Best for Azure SQL | Best for SharePoint + Syntex |
| Data-Intensive Businesses | Organizations dealing with large volumes of structured data, such as e-commerce platforms, financial institutions, or data analytics firms | Organizations with a focus on document management, collaboration, and content processing, such as legal firms, research organizations, or content-heavy enterprises |
| Application Developers | Businesses building custom applications that heavily rely on structured data storage, complex queries, and transactional processing | Businesses developing solutions that involve unstructured content understanding, document-centric workflows, and integration with AI-powered capabilities |
| Analytics and Reporting | Companies requiring advanced analytics, data mining, and complex reporting on structured data | Companies that need document-centric analytics, content extraction insights, and reporting on unstructured data |
| Compliance and Records Management | Organizations with regulatory compliance requirements for structured data storage and management | Organizations with compliance needs for document management, data classification, and content retention |
| Collaboration and Document Management | Companies focused on collaboration, version control, and document-centric workflows, such as legal firms, marketing teams, or content-driven organizations | Companies with a strong emphasis on document management, content extraction, automated metadata tagging, and intelligent search capabilities |
| Enterprise Content Management | Organizations with a need for comprehensive enterprise content management solutions, including document storage, collaboration, workflows, and records management | Organizations that require AI-powered content understanding, automated document processing, and integration with existing SharePoint environments |
By combining SharePoint’s low-code development environment, automated document processing with Syntex, and integration with Power Platform, organizations can more effectively empower citizen developers to create business solutions rapidly and efficiently.
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