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Big data fuels the modern commercial and public sectors. Today, virtually all businesses and government institutions across the globe leverage at least one aspect of big data in their day-to-day operations. For instance, businesses use insights from data to craft market expansion strategies, and governments use intelligence such as demographics to distribute resources within their populations.
Although big data provides a plethora of advantages to businesses and public institutions, many entities are finding it difficult to extensively leverage the technology due to their inability to exploit the power of artificial intelligence (AI) in big data. This article discusses barriers to the use of AI in big data and explores how the use of SQL-accessible ML models can mitigate these barriers.
While small-scale data can be analyzed using typical tools such as spreadsheets, big data is quite complex and requires the use of sophisticated methods such as AI. Unfortunately, many data analysts are finding it challenging to use AI due to limitations such as:
Need to learn new programming languages: Using AI in big data requires data analysts to learn complex programming languages such as Python and R. While these languages offer powerful AI tools that can retrieve complex patterns in vast data sets quickly, they take time to master, and some experts must undertake costly training.
Premium tools: Very few open-source tools offer advanced data analytics capabilities. Popular tools such as Microsoft BI, Google Analytics, Tableau, and QuestionPro are commercial. Some charge as high as $99 monthly. Subscription costs are a major hindrance for small businesses that wish to leverage big data benefits.
Integration issues: Although most AI-enabled data analysis tools are designed to integrate with conventional systems used by businesses and institutions, they tend not to be readily integrable with some applications. Integration issues prevent analysts from using AI-powered data analysis tools.
These limitations can be mitigated by data analysts employing SQL-accessible ML models in data analysis. These tools address these issues due to their availability to everyone, familiarity in the data analyst communities, and their integration with virtually all systems used by businesses and institutions.
SQL-accessible ML models are unique approaches that allow users to employ artificial intelligence in data analysis via SQL interfaces directly. These models allow access to AI capabilities in data analysis through SQL queries. This methodology mitigates enterprise AI adoption barriers in data analysis since it leverages SQL, which is familiar among data analysts and compatible with most systems used by businesses and government institutions. Some of the definitive benefits of employing SQL-accessible ML models in data analysis include;
SQL-accessible ML models significantly lower the entry barrier to machine learning, enabling data analysts and engineers to utilize and deploy models through SQL, a standard language in their competency sphere. These models also allow a broader range of professionals within an organization to engage in AI initiatives, fostering a culture of innovation and collaborative problem-solving. According to Brunner et al. (2016), SQL is one of the best data science languages that leverage a simple structure that is easily learnable and usable. Its simple structure makes it ideal for experienced and average data analysts to combine it with AI in data analysis.
SQL is the universal language for databases. For this reason, SQL-accessible ML models allow users to work within their existing data warehouses or databases without the need for data migration to specialized ML platforms. This facilitates a smoother workflow from data analysis to model deployment. Also, since the models use SQL, users do not have to buy intermediary infrastructure to implement ML capabilities.
This is vital for avoiding additional costs. It is also worth noting that SQL integrates perfectly with other data science languages such as R, Python, Julia, and Rust. This allows professionals with a nudge to exploit different tools to comfortably switch between SQL and other AI-enabled tools.
SQL integration with ML models permits the analysis and utilization of large datasets without the need to downsize, thanks to the robust processing power of modern data warehouses and databases. Automatic resource allocation of these systems also plays an essential role in ensuring performance and scalability as the data grows.
SQL-accessible ML models support rapid experimentation and iteration, giving data teams the flexibility to refine their approaches efficiently. The ability to quickly develop, test, and deploy models directly within the SQL environment accelerates the machine learning lifecycle, allowing businesses to meet their needs timely. It is also widely known that the use of SQL-accessible ML models simplifies and streamlines data cleaning and organization as well as the analysis and visualization of datasets.
Enhanced security and compliance
SQL-accessible ML models benefit from the existing security and compliance frameworks of SQL environments. Kamara & Moataz (2018) note that SQL is one of the database programming languages with robust security. The language not only supports advanced encryption models but also features other approaches, such as access control and ultramodern authentication and verification models. Besides, since data remains within the secure environment of the existing database or data warehouse, data privacy, and sovereignty are hardly adversely impacted by the models.
SQL-accessible ML models function with SQL-based infrastructure, which implies that the approach does not attract costs similar to those of standalone ML platforms. Besides, since the models integrate into SQL environments directly, they avoid costs associated with data transfer across platforms.
The advent of SQL in data analysis using ML models is already leaving a remarkable impact in the data science field. However, this is only the beginning. It is projected that SQL-accessible ML models will have more commanding applications in the field. Some of the areas SQL-accessible ML models are forecasted to dominate in the near future include:
Cloud technologies: The cloud is an integral pillar in data management and analytics. Although SQL-accessible ML models are already usable in clouds, it is anticipated that in the future, these models will have deeper integration with clouds, providing more flexibility, scalability, and efficiency in data handling. This integration will also facilitate access to vast data resources and allow more complex data operations.
More sophisticated SQL and ML integration: Current SQL-ML integration is still in the infant stages. AI-enabled SQL algorithms will become more sophisticated in the future, enhancing the models’ efficacy to streamline and optimize workflows. This will make it easy for analysts to train, test, and deploy advanced SQL-accessible ML models.
Diverse data types: SQL is not quite effective in handling non-relational data. The advancement of SQL-accessible ML models will equip it with enhanced features for managing and analyzing unstructured data, integrating with NoSQL databases, and providing more flexible data modeling options.
Real-time data processing: In the modern, fast-paced world, the demand for real-time data processing is surging. In the near future, SQL-accessible ML models will be able to process data in real time, providing instant insights for quick decision-making processes. To attain this fit, SQL-accessible ML models will be fitted with stream processing technologies and in-memory databases.
Autonomy: Industrial processes are being automated. Soon, SQL-accessible ML models will be automated, obviating the need for active human intervention. This will reduce the burden of database maintenance and optimization. This pivot will allow human data analysts to focus on more strategic data analysis and interpretation.
Big data is an interesting field that allows institutions to make data-driven decisions. Although many data analysts have been barred from leveraging the technology due to their inability to exploit ML capabilities, SQL-accessible ML models promise to mitigate this barrier by allowing all data analysts knowledgeable in SQL to exploit the technology. Besides bridging the skill gap, these models seamlessly integrate with nearly all existing data ecosystems. SQL-accessible ML models signify a substantial shift towards making AI capabilities universally accessible and efficiently utilizable across institutions. In addition to current capabilities, projections indicate that in the future, SQL-accessible ML models will have capabilities such as real-time data processing, autonomous data processing, deeper cloud integration, and the ability to support diverse data types.
About the Author:
Abhijeet Rajwade is GenAI Ambassador at Google. With 18 years of professional experience, he has had the privilege of working in diverse domains encompassing product management, start-up ventures, sales, and business operations. He possesses a strong passion for building innovative solutions, especially in the realms of GenAI, analytics, and business strategy.
As GenAI Ambassador for Google Cloud in the New York region, Rajwade is entrusted with the leadership of pivotal growth initiatives. He has been at the forefront of significant technology disruptions, including cloud adoption, emerging development methodologies such as design thinking, advancements in artificial intelligence (AI), machine learning (ML), and generative AI-driven automation.