Data management is a critical process if you want to utilize your data reliably for improved business decision-making and profitability. However, in conventional data management techniques, you may face challenges such as scalability issues and data silos. To overcome these drawbacks, you can opt for AI data management. It involves automating various data management steps, including data integration, governance, and security, reducing errors, and streamlining organizational workflow.
Let’s learn about AI data management and how it works. This will help you collect, store, manage, and use data effectively for diverse real-world applications.
What is AI Data Management?
AI data management is the use of AI and machine learning algorithms to efficiently manage your enterprise data. The process of data management involves collecting, organizing, and storing data to perform data-based operations effectively. By unifying AI with data management, you can enhance data accessibility and integrity for well-informed decision-making.
Role of AI in Data Management Lifecycle
AI data management involves the usage of AI in the various stages of the data management lifecycle. Here are some ways in which AI supports while handling data:
AI in Data Collection
You can use AI-powered data collection tools to extract and consolidate data from multiple sources, such as databases, flat files, and websites. These tools are software applications that utilize machine learning algorithms and natural language processing (NLP) techniques to retrieve data from diverse sources. Due to this, you can save the time required to gather data manually and utilize it for other important business aspects.
AI in Data Storage
To store data, you can use any AI-based solution that provides you with tiered data storage. It is a process that involves categorizing data according to its importance and usage.
AI models can easily assess changes in usage patterns and monitor increases in data volume in real-time, facilitating better data storage. You can also use AI solutions to monitor the performance of storage systems to detect and resolve errors, preventing operational downtime.
AI in Data Governance
Data governance is a set of policies that helps you securely and effectively utilize your data. Using AI solutions, you can leverage machine learning models and NLP capabilities to identify and categorize sensitive data records. To protect such critical data, you can use AI-based data governance tools like Domo and Azure Machine Learning that can detect unauthorized access and anomalous user behavior.
You may also opt for AI data lineage platforms that help you track the movement of data across different systems within your organization. By deploying such solutions, you can prepare a robust and secure data governance framework for your enterprise.
AI in Data Archiving
Archiving data is a data management practice of identifying data that is no longer actively used and storing it in a separate system for future reference. In data archiving, using AI can automate categorization, deduplication, and predictive archiving. There are some NLP-powered tools like IBM Watson that you can use for quick searches and retrieval of archived data, enhancing data accessibility.
How Does AI Data Management Work?
Let’s look into how you can deploy AI technologies in enterprise data management in a step-by-step manner.
Step 1: Data Ingestion
The first step in data management is ingesting data from various sources, such as databases, APIs, or cloud platforms, using a suitable AI data extraction tool. The AI solutions automate and optimize the process of extracting data from diverse sources. You can opt to gather data in batches or in real time according to your workflow requirements.
Step 2: Data Cleaning
The data collected is usually in raw form, and you can convert it into a standardized format through data cleaning and transformation techniques. This involves detecting and fixing duplicate, missing, and inconsistent data records.
Step 3: Data Storage
After cleaning, you can store data in highly scalable data warehouses, databases, or data lakes. AI data management systems support different storage solutions, including cloud-based like Azure Data Lake or on-premises to help you ensure that data is organized for retrieval.
Step 4: Data Analytics
You can conduct AI-assisted analytics on cleaned data to analyze data, recognize patterns, and gain actionable insights. You can further perform predictive analytics and anomaly detection using AI features.
Step 5: Data Governance
AI-based governance tools help frame policies related to data architecture, accessibility, modeling, and security. Such AI platforms also offer audit trails and data lineage tracking features. These solutions ensure transparency and allow you to track the flow of data across various systems in your organization. This enables you to comply with data regulatory frameworks like GDPR and HIPAA.
Tools for AI Data Management
After understanding what is data management and how AI can improve it, let’s explore some AI-driven data management tools:
1. IBM Cloud Pak for Data
IBM Cloud Pak for Data is a cloud-based solution that offers effective AI data management and data governance capabilities. It is built on data fabric technology and supports AI-driven data discovery, profiling, and cataloging through IBM Knowledge Catalog Software. The AI data management features of IBM Cloud Pak for Data include the AI Factsheets, useful for tracking data lineage. You can also use IBM Match 360 with Watson for data matching to get a distinct and unified view of your enterprise datasets.
2. Ataccama ONE
Ataccama ONE is a unified platform that offers Master Data Management (MDM), data governance, data quality, and data lineage solutions. It is a high-performance software that you can deploy on-premise, in the cloud, or in a hybrid environment. The Ataccama MDM service provides various features, including comparing, merging, and AI-driven matching, to assist you in managing master data. It is the data related to critical business aspects such as customers, products, or employees. Ataccama ONE also supports GenAI capabilities to allow you to build data quality rules with natural language prompts quickly.
3. Informatica Intelligent Data Management Cloud
Informatica Intelligent Data Management Cloud is an AI-powered data management platform. It supports CLAIRE GPT, a GenAI-based data management assistant. You can provide it with natural language prompts to discover data and build data pipelines efficiently. While developing pipelines, you can also avoid data drifting with the help of CLAIRE GPT. Apart from this, to facilitate robust metadata management, the Informatica Intelligent Data Management Cloud supports the metadata knowledge graph. Using this feature, you can get a better semantic understanding of enterprise data.
4. Syniti Knowledge Platform
The Syniti Knowledge Platform (SKP) is a unified tool that offers data management, quality, governance, and catalog services. SKP allows you to work within the SAP ecosystem and leverage SAP Business AI capabilities. Emphasizing the importance of transforming your data to build highly functional AI applications, SKP is based on the Data First approach. To maintain high data quality, SKP supports data extraction and migration using advanced machine learning algorithms.
Benefits of Using AI in Data Management
According to a report by Markets And Markets, the global market for AI data management will expand from USD 25.1 billion in 2023 to USD 70.2 billion in 2028, with a CAGR of 22.8%. The key factor behind this growth is that the AI-based data management practices offer several benefits, such as:
1. Automation
Using AI for data management facilitates faster processing, querying, and analysis of your datasets. Due to this, you can accelerate the data-based operations in your organization and gain timely business insights for better decision-making. According to a Statista report, AI adoption will improve labor productivity by 1.5 percentage points in the next ten years. In this trend, AI-based data management can become a significant contributor as most organizations today work with data.
2. Enhanced Data Quality
AI data management solutions accelerate the data cleaning processes, such as handling duplicate or missing values and identifying outlier points. The algorithms in the AI tools can help quickly track errors that you can fix to ensure data accuracy. All these capabilities of AI provide high-quality datasets that you can utilize to develop advanced software applications.
3. Robust Data Security
AI data management includes the use of AI-powered data security tools such as CrowdStrike Falcon, Cylance PROTECT, and Vectra Cognito. The algorithms in these tools detect unusual data patterns, enabling you to take faster preventive action in case of a data breach or cyberattack. Some AI security tools utilize trigger mechanisms like blocking suspicious IP addresses on threat detection. Such measures help you to effectively protect sensitive customer data and fulfill the guidelines of data protection frameworks like GDPR.
4. Cost Reduction
By automating repetitive tasks and improving productivity, AI data management can reduce overall operational costs. As AI algorithms minimize errors, the expenditure on resources required for correction is eliminated. These cost-saving benefits of AI data management allow you to redirect your monetary resources toward more critical business tasks.
Future Trends in AI Data Management
Due to the numerous benefits, the combination of AI and data management is being increasingly adopted to perform intelligent data operations. Here are some trends that you will see in the future in AI data management:
Augmented Data Management
Augmented data management is the process of using AI and machine learning to enhance various operations within the data management lifecycle. The augmented approach for data management is a human-centered technique. Its intent is to allow you to use AI in collaboration to fine-tune data management. This makes augmented AI a prominent trend in the landscape of AI data management.
Future Forecasting
Future forecasting involves analyzing current and historical data to identify trends and make predictions. With the help of the future forecasting process, you can conduct predictive data management to optimize resources and data infrastructure in advance. Future forecasting trends can also help you understand customer demands and changes in market conditions beforehand so you can refine your data management strategy.
Explainable AI
Explainable AI is a set of techniques that allows you to understand how an AI algorithm generates a response. By adopting explainable AI practices, you can foster transparency to build a robust data governance framework, which is a critical step in data management.
Implementation of explainable AI practices provides you with interpretations, helping you comply with data regulatory frameworks like GDPR and HIPAA. As it is becoming mandatory to follow the data regulation guidelines worldwide, you must adopt explainable AI techniques in your data management lifecycle.
How Does Airbyte Help with AI Data Management?
The data integration process involves extracting data from varied sources and consolidating it at a unified destination. It is a critical part of data management as it helps eliminate data silos and improve data accessibility within your business organization.
To effectively integrate data, you can use a suitable data movement platform like Airbyte. It offers a library of 550+ pre-built connectors to assist in extracting data from any source and loading it to a destination of your choice. If the connector you want is not in the existing set of connectors, you can build one on your own. To develop custom connectors, Airbyte provides various options, such as Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.
After loading data to the destination, you can integrate Airbyte with dbt, a command line tool, to transform your data into a standardized format. By transforming data, you can enhance the data quality to perform accurate business intelligence operations.
Some other prominent features of Airbyte are:
- AI-powered Connector Builder: While building custom connectors with the help of Airbyte Connector Builder, you can use its AI assistant. It automatically pre-fills the necessary fields for connector configuration, reducing setup time. The AI assistant also provides intelligent suggestions to fine-tune the connector configuration process.
- Change Data Capture (CDC): Airbyte’s CDC feature allows you to incrementally capture changes made to the source data system and replicate them into the destination. This enables you to maintain data consistency and integrity.
- Build Developer-Friendly Pipeline: PyAirbyte is an open-source Python library that offers a set of utilities for using Airbyte connectors in a Python ecosystem. With the help of PyAirbyte, you can extract data from diverse sources and load them to SQL caches such as PostgreSQL or BigQuery. You can then clean and transform this data using Python libraries like Pandas and analyze it to gain useful business insights.
- Streamline GenAI Workflows: While using Airbyte, you can directly load semi-structured and unstructured data to vector store destinations. Airbyte supports popular vector databases such as Pinecone, Milvus, Weaviate, and Chroma. You can integrate these vector databases with LLMs to retrieve contextually accurate information and improve the efficiency of your GenAI-based workflows.
Conclusion
AI-enabled data management is essential for accessing data in a secure manner to conduct data-related functions. This blog explains AI data management and how it works in detail.
To systematically manage data, you can use any of the several available AI-powered data management tools, such as IBM Cloud Pak for Data or Ataccama ONE. These tools offer numerous benefits, including automation and better data quality for downstream operations. By implementing the AI data management process, you can automate and streamline your data workflows and improve the financial performance of your organization.