Integrating AI and Human Intelligence for Effective Data Management

As data volumes grow and sources diversify, businesses are transitioning from traditional to modern data management approaches. Previously, the focus was on managing structured data within monolithic or on-premises relational databases.

However, companies now leverage scalable architecture, cloud-based infrastructure, and advanced tools to process and maintain vast amounts of structured, unstructured, and semi-structured data from diverse sources (including social media and IoT devices) across various environments.

Despite significant investments in AI technology to automate data pipelines, a key question persists: How reliable can these automated tools be if they work without human intervention?

Many organizations advocate that a human-in-the-loop approach is more effective for secure and responsible data handling. But is that true? Let’s find out.

Modern Data Management – Key Pillars

Modern data management focuses on several key aspects that ensure efficient and secure data handling, turning information into a strategic asset that drives business success.

Data Quality and Security

An effective data management approach is one that ensures consistency, completeness, accuracy, and timeliness across datasets.

This is only possible when the data is regularly audited, cleansed, and validated (through automated and manual techniques) to remove inconsistencies, duplicates, and obsolete information.

Equally important is data security: protecting sensitive information against breaches, unauthorized access, and corruption.

Advanced data management should provide for real-time monitoring through a compliance-driven protocol and integrate advanced encryption to ensure data quality and security at each stage.

Data Integrability

Integrability should be a priority for businesses when it comes to data. This ensures that even though the information is collected from diverse sources, it can be easily integrated, processed, and analyzed across different environments without compatibility issues.

Microservices architecture and containerization play a crucial role in this process, enabling independent data components to integrate and interact effortlessly, regardless of the underlying technology stack.

Decentralized Data Governance

The modern approach favors decentralized data governance, where data ownership and management responsibilities are distributed across the organization.

Data cataloging, automation, and distributed ledger systems help maintain consistency, compliance, and visibility across the organization, even in a decentralized model.

Data Democratization

Now businesses promote data democratization, ensuring that data is accessible to diverse teams across various departments within an organization, not just IT specialists.

This is achieved through cloud-based data platforms and centralized data lakes or warehouses. These tools often include collaboration features, allowing teams to easily share insights, dashboards, and reports and access all relevant information available in one place.

Metadata Management and Data Lineage

The modern approach strongly emphasizes metadata management and data lineage to provide transparency into where data comes from, how it’s transformed, and how it’s used, ensuring trust and accountability across data pipelines.

How AI is Helping Organizations Manage Big Data

As per Statista, global data creation and consumption are forecasted to grow by over 180 Zettabytes by 2025. [Source]

To process, manage, and analyze such big data, organizations must invest in advanced technologies, such as artificial intelligence and machine learning.

Here are some modern data management approaches in which AI is playing a big part:

1. Data Fabric

This modern data management architecture focuses on integrating various data sources across diverse environments utilizing AI and machine learning tools.

Unlike data warehouses or lakes, data fabric doesn’t require replacing or duplicating data infrastructure. It leverages existing storage infrastructure and tools to create a unified, centralized data access layer.

AI & ML plays a crucial role in enhancing data fabric by:

Automating metadata management (creation or extraction) based on data structure and content
Identifying and mapping relationships between data across distributed sources
Providing intelligent recommendations on which data to use for specific use cases based on content-based filtering and data characteristics like lineage and similarity
Automating the process of connecting and harmonizing data from disparate sources
Facilitating on-demand data provisioning, allowing users to access relevant and actionable information in real-time using conversational queries.

2. Data Lakehouse

Lakehouses offer a flexible and cost-effective solution for storing unstructured, semi-structured, and structured data within a centralized repository.

This architecture combines the scalability of data lakes with the data management capabilities of traditional data warehouses, making it ideal for business intelligence, analytics, and purposes.

Leading tools like Snowflake, Amazon RedShift, and Google BigLake leverage AI capabilities to enhance the functionality of lakehouses, transforming them from basic storage solutions into intelligent, self-managing systems.

These tools can store data at any scale and process it by:

Automatically classifying and tagging incoming information, making it easier to organize, retrieve, and analyze data
Detecting anomalies, duplicates, and inconsistencies in the datasets and rectifying issues in real time to maintain data integrity
Facilitating data normalization and transformation to ensure a coherent format and compatibility with analytical models

3. Data Mesh

Data Mesh is a conceptualized framework referred to as the “Sociotechnical Approach” by Zhamak Dheghani (the creator of this concept).

This approach focuses on bringing data experts to decentralize and manage domain-specific datasets, promoting ownership and accountability.

Along with data experts, this framework demands advanced technologies like AI and ML to mitigate the complexities of managing the data lifecycle. These technologies can contribute to data mesh by:

Automating data discovery and classification within each domain
Inferring relationships between datasets across different domains, automatically building a knowledge graph that connects master data for interoperability and insight generation
Identifying and classifying sensitive data to associate privacy policies and enforce rules dynamically

4. Master Data Management (MDM)

This discipline enables organizations to create a master record of data (that serves as a single source of truth) to be accessed and utilized by different departments.

By consolidating data from multiple sources, MDM ensures data consistency, accuracy, and reliability across the organization, making it a cornerstone for future-proofing data repositories.

The integration of AI and ML technologies into MDM can significantly enhance its effectiveness by:

Automating schema matching with core attributes, simplifying master data modeling
Matching and merging records from multiple sources, identifying duplicates and inconsistencies to keep the master data up-to-date, accurate, and complete
Automating the identification and categorization of field types within data sources, and then mapping them with an existing master data model.

Areas Where AI Fails Without Human Oversight

Indeed, artificial intelligence and machine learning are transforming how businesses manage and process their critical data.

Despite these advancements, there are still areas where these technologies are not able to gain user trust, such as:

Data Quality

The foundation of any AI/ML tool is its training data. If the data it ingests is of poor quality (inaccurate, obsolete, or incomplete), the outcomes it will generate will not be reliable.

As emphasized by Rohit Bhateja, the Director of the Data Division at SunTec India – “For every enterprise trying to implement AI across its architecture, focusing on data quality is crucial”.

By prioritizing high-quality data, businesses can maximize the effectiveness of AI systems and achieve better results in data management.

Data Security

Another significant concern with using AL/ML tools for data management is the safety of information.

No matter how advanced these systems become, stakeholders always hesitate to share sensitive details (including personal data, financial records, and proprietary business information) publicly.

There is a risk of AI systems unintentionally exposing sensitive data through algorithmic biases or vulnerabilities in the models themselves.

Failure to secure AI-driven data management processes can lead to non-compliance with data privacy regulations such as HIPAA & GDPR, legal penalties, and reputational damage.

Artificial and Human Intelligence Must Go Hand-in-Hand for Effective Data Management

While AI tools bring efficiency, they lack the nuanced understanding and critical thinking that only subject matter experts can provide.

That is why it is crucial to utilize the strengths of both through an integrated approach – Human-in-the-loop (HITL).

AI-powered tools can be utilized to automate repetitive tasks like data standardization, data deduplication, and data extraction.

While subject matter experts can be utilized for more critical tasks such as implementing data security protocols, performing data enrichment and validation, and creating data annotation guidelines for AI/ML models.

This balanced approach to data management will help organizations ensure scalability, cost-effectiveness, relevance, data quality, and security compliance, for sustainable business growth.

About the Author!

Nick Pegg is a content strategist & a technology enthusiast working at SunTec.AI, a leading data support company for AI/ML. He has extensive experience writing about various transforming and advanced technologies like artificial intelligence and machine learning. In his spare time, he loves to explore and learn about new tools and technologies shaping the various industries- data science, eCommerce, robotics, and healthcare, among others. He keeps himself updated on all the new trends, innovations, and advancements happening around AI/ML technologies and pens down his knowledge to present well-researched and informative articles to help businesses leverage these technologies to their advantage.