Modern AI operations that use AI-driven data environments give organizations a competitive advantage because they provide greater collaboration between data scientists and data operators—data stewards, administrators, and custodians. The data they use spans on-premises, cloud, and hybrid systems. The data must be normalized, trustworthy, and accessible to the right people at the right time.
As organizations become ever-more data-centric and the breadth of data collection grows, processing, governing and analyzing data has become impossible for human input alone. Integrated data environments are essential for data operations, allowing them to store and locate data more efficiently. However, with the seemingly exponential growth of data inputs, there is a danger of this data turning into data swamps without the extra help provided by automation.
Let’s first quickly outline the different environments and systems that data scientists and data operators use, and then look at how AI operations can bring them together.
The data science community typically work in integrated development environments (IDEs) where they write and test code within a set tool package. They use workflows that efficiently breaks the data science projects into phases to better organize and implement projects in a systematic way. Similarly, they organize data and code elements and standardize how those elements relate to each within repeatable models.
Data science projects are usually stored in git repositories and they interface with them using database connectors. They also use third-party plugins when testing and connecting with external systems like machine learning and analytics applications.
Alternately, data operations teams are concerned with operationalizing data management. They often use data warehouses to analyze, report, and make data business-relevant and actionable. Data admins within operations use identity and access management (IAM) systems to govern both internal and external access to data.
Meeting compliance is a key component of data access and governance, but another component to data access is how quickly data can be retrieved and shared against queries. These performance concerns of data access and collaboration are then balanced against the costs of data availability and storage, or between hot and cold storage.
The advantages of bridging data science with data ops is obvious. You gain the innovation and problem solving capabilities of data science with the scale, automation and business value of data operations.
So how do you bring these systems together? How do you properly use metadata, optimize the ecosystem to handle large workloads and leverage the cloud to provide specific tools and services?
AI-driven data environments harness the potential of repetitive data usage and data source inputs to identify relationships and patterns between data. The AI-driven data environment then either inputs metadata information automatically upon data ingestion or presents data admins with intelligent suggestions to significantly reduce the time needed for manual inputs.
The features of AI-driven application environments
There are a number of AI-driven application environment features that help solve the issues enterprises have in applying data governance protocols to extremely large datasets. Below, we’ll explore some of the most beneficial features.
Relationship identification
This is the primary feature of AI-driven application environments and demonstrates the true potential of machine learning in all aspects of data. As we use data, patterns emerge that are obvious to humans but need to be “told” to a machine. With AI-driven app environments, these relationships are identified and sent to data admins, leading to faster classification of datasets and better metadata identification.
Interoperability in data usage for application development
With disparate data being ingested from diverse sources, it is essential for application environments to understand, parse, and catalog all incoming data no matter its format. Interoperability throughout the data lifecycle is critical for all aspects of data operations, so the greater steps towards interoperability, the better.
“For instance, the app Picsart utilizes an AI-driven environment to manage its separate data sources such as its image editing tools and features like the image background remover. This particular feature uses AI to identify and separate the background from the foreground in any image, providing the users with easier and more flexible editing options. It’s another example of how diverse data sets are effectively managed through an AI-driven environment for better user experience.”
Sensitive data detection
Data protection has become a significant risk factor for enterprises due to data regulations such as Europe’s GDPR and California’s CCPA. AI-driven app environments can be deployed to automatically detect sensitive data such as personally identifiable information (PII). This detection can be performed by following user-defined dictionaries or by identifying patterns in how admins deal with certain data.
Natural language processing (NLP)
Getting a machine to “understand” how human language works requires constant iterations of language usage data. The more an AI-driven environment is exposed to natural language, and manual meaning inputs, the more it can read natural language data. It will also be able to automatically detect context and nuance, allowing it to either create beneficial metadata itself or provide smart suggestions to the data scientists using it. This saves considerable time, especially for those handling vast amounts of data objects with single or few entries.
The benefits of AI-driven application environments
When deployed properly, AI-driven application development environments greatly accelerate the data usage and application integration, reduce the resources needed for data operations, and improve DataOps ROI. These benefits include:
- Data governance: Ensuring data safety, lowered risk, and regulatory compliance is a major task. AI-driven data environments enable better governance by automatically flagging certain data types and allowing the application of rules upon ingestion, such as including permissions in metadata.
- Trend identification: As AI-driven data environments grow to understand how data operations teams work, they can identify trends in behavior and security practices. This can then allow them to enforce better security and identify suspicious behavior around data automatically.
- Data preparation: Automated processes that input metadata at ingestion ensure greater data quality right from the start. AI-driven data environments improve on this by recognizing trends in this initial data preparation, meaning incoming data has constantly improving descriptors.
- Duplicate identification: Data duplication is common, especially across disparate or siloed data storage. With AI-driven data environments, these duplicates and copies can be identified and flagged, ensuring they are not further duplicated and can be easily removed. This saves time and money and prevents them from affecting data analysis.
- Data auditing: Data lineage and identifying sources of breaches are essential DataOps security features. The enhanced reporting capabilities and lineage tracking of AI-driven data environments empower data admins to conduct faster and more thorough reviews of data usage.
- Data previews: With NLP and intelligent suggestions, data scientists can get AI-driven summary previews of data without having to open it and find out for themselves.
- Data discovery: Data cataloging delivers considerable benefits in making relevant datasets more easily identifiable for querying. AI-driven data environments make data discovery even easier by quickly identifying patterns in what is being queried, as well as relationships between data in different datasets that may be valuable for a certain operation.
Conclusion
With ever-expanding data operations, AI-driven application environments must keep pace with data needs. The use of AI and ML in data gathering, managing, and analyzing is enhanced by the use of data virtualization platforms, which create an interoperable virtual layer of data between storage and processing functions. This allows AI and ML programs to run through one centralized data location, giving admins the power to apply all non-generalized governance rules they want to all data storage locations.
Intertrust Platform has been helping enterprises improve the capacity, time-to-market, and ROI of their data operations by improving data access and discovery, and AI-driven environments are just another step on the journey to reducing data friction to the minimum possible. Additionally, using secure execution environments for all data processing and sharing greatly reduces the risk of data breaches. With Intertrust Platform, admins even have complete access control down to row and column level.
The Intertrust Secure Execution Environment offers a unique platform for enabling data science at the speed, efficiency and automation of data operations. It lets you aggregate a variety of Docker containers and supports multi-tenant dev environments and also:
- Maximizes data lake asset discovery and interactions
- Provides governed data access between application, users, and data
- Enables agile and secure data operations workflows
To find out more about how Intertrust Platform is improving data operations across the world, including through the use of AI-driven data environments, you can read more here or talk to our team.
