Data Silos are Killing Your AI Performance

Data Silos are Killing Your AI Performance and Scalytics Connect is the Best Solution to Fix It

‍

Organizations investing in analytics, artificial intelligence (AI), and other data-driven efforts face a rising challenge: a lack of integration across data sources, which limits their ability to extract actual value from these investments. To enable greater business insights, IT and business leaders must eliminate these data silos, some of which are operational and others of which are cultural. A large percentage of organizations and their leadership teams understand the value of data and are working to develop a modern data strategy.

Some businesses are still in the early stages of the process, defining their data strategy and deciding which data or workloads to move to the cloud or data lakes and which to keep on-premises in data warehouse. Others have advanced further, with the goal of extracting additional value from their data initiatives or expanding efforts across the entire organization. To gain access to data housed in a siloed system, the most data-mature organizations use several data platforms, some in the cloud, others in private settings, or, in certain situations, utilizing a managed services model for scalable data infrastructure.

And that's the unique spot where Scalytics comes in to solve the most pressing data problems today. Scalytics is currently the most powerful data analytics platform on the market. With advanced AI capabilities, Scalytics is the perfect solution for organizations that want to break free from their data silos without centralizing data. With Scalytics, modern organizations can quickly connect to data silos and use them directly instead of wasting time and money implementing the next, bigger silo. This federated approach allows them to maximize their AI performance, reduce costs, and eliminate technical debt.

‍

Data Silos and How Do They Impact Data Processing Performance

Data silos are typically databases, files, data lakes, or other independent data sources. The data is stored in multiple data systems, also known as shadow IT,  and cannot be easily accessed or shared in a unified way. This creates an unnecessary problem for organizations that are trying to use advanced data analytics and AI, as algorithms need access to large amounts of data in order to learn and make accurate predictions.

But why are data silos such a problem now and in the future for organizations that are looking to build new applications and leverage AI technology?

Standalone data sources have multiple, serious negative implications; the most problematic one is the lack of an integrated connection with other data pools, leaving room for inaccurate insights and poor decision-making that could be harmful in the long run. To solve this issue today, organizations tend to use a data lake architecture combined with ETL processes. As this might solve the problem for some on the first view, this complex architecture pattern poses additional, much more complicated risks. Knowing the potential risks associated with these silos can help companies ensure their AI projects are successful and produce accurate results. 

‍

We identified three major problems in large enterprises with more than 1000 employees, which typically analyze 3 TB of data per day:  

• Complex and time-consuming ETL processes, more costs due licenses and staff
• Attached costs due data transportation, and incompatible data sources, like image archives, text files, compressed files or databases with a limited connectivity, like IoT edges or medical devices.
• Most data might not be allowed or able to centralize due legal constraints like HIPAA, GDPR, data privacy or other regulatory requirements

‍

Not accessible data always leads to inconsistent results that lead to inaccurate decision-making, which always leads to potential financial and operational losses or more drastic outcomes. 

‍

Federated Data Processing Makes Siloed Data Available

There are multiple ways to handle shadow IT and data silos. Federated data processing is the most promising technology to work with increased data velocity without killing budgets or introducing new platforms. Federated data processing enables organizations to connect to almost any data processing engine and analyze data stored in multiple systems almost immediately, removing the time-consuming ETL part. As soon as the data processing layer has access to the underlying system, this data can be accessed.

The idea behind federated data processing is to create a virtual layer on top of data sources, independent of their technology (RDBMS, files, data lakes, or data warehouses). This layer gives a uniform representation of the data, making it easier to evaluate. FL based technology has multiple advantages and benefits:

‍

• Reduced data management costs: ETL costs are reduced because federated data processing minimizes the need to move data from or to different systems. This saves organizations a lot of money on data transfer and data management costs.
• Improved data governance: Federated data processing enables companies to preserve sensitive data in its original place.
• Increased data access: Federated data processing allows users to access data from siloed systems more easily. 
• Improved data analysis: Federated data processing provides a uniform representation of the data, making it easier to analyze it. 

‍

Make Your Data Pipelines Smarter

Scalytics is a powerful AI-driven data access and processing platform that enables companies of any size to manage their data much more effectively. By leveraging the power of distributed data processing, Scalytics provides an efficient and intuitive way to manage and analyze large volumes of data to train machine learning and AI directly at the source of the data. Instead of centralizing data into a much larger data silo, like a data warehouse or data lake, Scalytics enables data teams to use current systems and databases, be they local data stores or cloud-based applications like Snowflake or Redshift. 

‍

‍

With its easy-to-use interface, Scalytics allows users to quickly access critical data and generate insights from all their data without moving the data out of their current system. Scalytics enables organizations to optimize their resources by providing automated solutions for various tasks such as data preprocessing, feature engineering, model selection, hyperparameter tuning, k-mean, neuronal networks, and more. Businesses can gain deeper insights into customer behavior and trends using its extensive data management and processing capabilities to push for improved decision-making. This enables them to make better decisions and maximize opportunities much more quickly than ever before, while also ensuring that their data is secure and available across all departments and users, maximizing the value of their data while minimizing the costs associated with managing it.

‍

Exploring the Power of Automated ML Workflows

Automated machine learning (ML) workflows are becoming increasingly popular in the wake of LLM and other AI models. Scalytics provides an innovative way that enables users to quickly and easily create data science workflows or AI pipelines, that automate the entire process of data preparation, model training, and deployment. Scalytics allows customers to quickly create their ML workflow, share it with other platform users, or even collaborate with other companies to solve a generalistic problem without jeopardizing their data security or intellectual property. This opens them up to focus on the tasks that truly matter: gaining insights from their data and making decisions based on those insights.

‍

‍

Scalytics Connect also provides a number of features that make it easy to manage AI models. These features include:

• Model monitoring: Monitor the performance of your AI models.
• Model management: Manage your AI models, including updating them, retraining them, and deploying them.
• Model governance: Manage the governance of your AI models, including setting permissions, auditing access, and managing compliance.