Microsoft might have just changed the business intelligence and analytics strategy for every enterprise organization. The company just announced the next-generation features of SQL 2017, introducing some of the latest data services and analytics capabilities, including robust AI features and support for native R programming and Python.
When technology executives define their analytics strategies for organizations, most agree that AI, machine learning, natural language processing and data mining are critical components of those plans. In the last few years, many of these analytics capabilities have seen an increase in popularity, but they have remained complex and costly and require unique skill sets that prove to be expensive and hard to find.
Microsoft's recent change to its SQL platform highlights its unique solution and perspective in addressing some of the challenges its customers face when it comes to accessing AI and other analytics services. By bringing AI directly to where the data lives, BI specialists can now perform advanced queries, including the application of simple or advanced algorithms, and gain insights in one command. This approach eliminates the need for a client to move data around outside of its container to apply AI to it.
Here are the highlights of the new features in the SQL 2017 platform that will positively impact the analytics strategies of companies.
Organizations can store and manage smarter data
SQL Server 2017 is changing the way we view data, literally. In fact, the new capabilities of the platform will enable data scientists and businesses to interact with data in a way that enables them to retrieve different algorithms that can be applied to the data and view data that has already been processed and analyzed.
Microsoft integrating its AI capabilities with its next-generation SQL Server engine enables the delivery of smarter data.
Cross-platform support delivers more flexibility
Whether an organization is a big Linux shop or simply has a need to do some development that requires the use of a database engine like SQL on a Mac, companies are in luck, as the new generation of SQL can now run fully on Linux as a full install or be containerized in Docker to run on macOS.
The cross-platform support of SQL will provide an opportunity for many firms who are using non-Windows operating systems to finally deploy the database engine.
Advanced machine learning features at the edge
Organizations looking to leverage the advanced capabilities of machine learning can use Python and R programming, which have been successful in the AI field natively within the SQL 2017 server. This offers data scientists the opportunity to leverage all the existing algorithm libraries or to create new ones within the new system.
The integration can prove to be extremely valuable, as there is no need for companies to support multiple tool sets in order to accomplish their advanced analytics goals with data.
Enhanced security at the data layer
With the new SQL release, companies will now have newly enhanced data protection capabilities directly on the data layer. Row Level Security, Always Encrypted and Dynamic Data Masking were previously introduced under SQL 2016, but many tools have received new improvements, including the ability for organizations to secure not only rows, but also columns.
Improved BI features around analytics
There have also been improvements to analysis services, which are commonly used by organizations to process large sets of data in order to provide business users with aggregated data. Some of the new features include new sets of data connectivity capabilities, data transformation features, mashups with Power Query Formula Language, enhanced support for ragged hierarchies in data and improved time intelligence for the date/time dimensions used.
Enterprise clients all recognize that a strategy around business intelligence and gaining insight through data requires a significant investment in advanced analytics data platforms. The process of acquiring data, managing it, applying advanced predictive algorithms to it and passing it back to data visualization tools is simply too long and complex at times.
As a result, a consolidated solution similar to what Microsoft is highlighting in SQL 2017 may be a great example of what could ultimately improve and streamline the process of gaining insights from data without too much complexity, enabling enterprises to quickly gain access to AI and its capabilities with little effort.
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