Data challenges demand integration, privacy, literacy, and democratization…

Suresh Chandrasekaran, Executive Vice President at Denodo, highlights the significance of data literacy and the skilled workforce, underscoring the necessity for a culture of data democratization that empowers individuals across organizational hierarchies to harness data effectively.

In an era where data is as valuable as currency, the complexities of managing, securing, and leveraging this vital asset are escalating. Suresh Chandrasekaran, Executive Vice President at Denodo, interacted with Nisha Sharma, Principal Correspondent at CIO&Leader, to delve into the intricate world of data management and governance. Their conversation traverses the evolving landscape, addressing the paramount importance of data privacy and security in a time when artificial intelligence (AI) and machine learning (ML) are becoming ubiquitous in business processes. 

From the perennial issue of data silos and the burgeoning need for comprehensive integration strategies to the critical concerns around data privacy, security, and the ever-present threat of bias within AI algorithms, he outlines the hurdles that organizations worldwide are grappling with. Moreover, 

Suresh Chandrasekaran
Executive Vice President

As we stand at the cusp of this transformative era, Chandrasekaran’s insights into the future of data governance, the impact of AI and ML on data management, and the emerging trends that will shape the analytics landscape are invaluable. This Q&A session sheds light on the complexities of the current data management ecosystem. It charts a path forward for industry leaders looking to navigate the intricacies of this ever-evolving domain.

CIO&Leader: As data privacy and security become paramount, what direction do you see data governance heading in the future?

Suresh Chandrasekaran: The trajectory of data governance is steering towards more integrated and centralized frameworks. This evolution aims to tackle the challenges of managing a dispersed data landscape. Future governance models must seamlessly span various data sources and applications, ensuring that data policies, privacy standards, and regulatory compliance are uniformly applied and managed from a singular vantage point.

CIO&Leader: What proactive steps should industry leaders take to adapt to the evolving data governance landscape?

Suresh Chandrasekaran: Leaders should begin by setting up comprehensive policies that govern data access, usage, and delivery. These policies should be adaptable to accommodate diverse roles, regional jurisdictions, and industry regulations. This includes mechanisms for handling data-subject requests effectively and establishing transparent data lineage. Adopting a centralized management strategy encompassing all data assets becomes essential to navigating the growing complexity. This strategy should facilitate governance at the point of data consumption, enabling real-time compliance and risk management.

CIO&Leader: How can organizations manage data governance across an increasingly fragmented data environment?

Suresh Chandrasekaran: To overcome the fragmentation, organizations should adopt a unified governance approach that eliminates the reliance on disparate, siloed governance models. Centralizing governance efforts through a single platform or framework can provide a holistic view and control over data, irrespective of its source. This centralized approach ensures that governance, compliance, and risk management practices are consistently applied, reducing manual intervention and minimizing the risk of oversight.

CIO&Leader: In light of stringent regulations like GDPR, how can organizations ensure compliance while maintaining operational efficiency?

Suresh Chandrasekaran: Ensuring compliance with regulations like GDPR requires organizations to directly integrate governance and compliance mechanisms into their data management processes. By implementing governance at the data access layer, organizations can streamline enforcing privacy and security policies, making it easier to respond to data-subject requests and monitor and report compliance. This integrated approach not only aids in maintaining regulatory compliance but also supports operational efficiency by allowing for agility in how data is accessed and used within the organization.

CIO&Leader: In what ways do you anticipate AI and machine learning impacting data management and business decision-making in the near future?

Suresh Chandrasekaran: AI and machine learning are set to revolutionize data management and business decision-making by automating complex processes previously prone to human error. With the advent of generative AI, we’re seeing a broadening of use cases—from creating customer-facing materials to generating code and marketing videos. This advancement is expected to drive cost reduction and process efficiency and enhance revenue generation and service quality across various industries.

Integrating AI and ML technologies is becoming increasingly popular for data management, driven by the surge in data volumes and the challenge of sourcing skilled data professionals. Major IT vendors now embed AI capabilities into cloud data platforms to improve data organization and access. This includes enhancements to data cataloging, metadata management, anomaly detection, and data governance, making foundational tasks like data identification and classification more efficient and accurate.

CIO&Leader: Can you provide examples of major IT vendors incorporating AI into their data management platforms?

Suresh Chandrasekaran: Certainly. We’ve seen significant moves by leading IT vendors to infuse their cloud data platforms with AI capabilities. For instance, Amazon has upgraded DataZone for better enterprise data handling, integrated advanced AI models into Bedrock, and enhanced Redshift’s query performance. Microsoft launched the Fabric, featuring strong AI capabilities like Copilot, which allows users to create pipelines, write SQL statements, or build ML models using natural language. Google and IBM have also made strides with generative AI and AI-driven data management developments through tools like Gemini, Duet AI, Vertex AI, and WatsonX.

CIO&Leader: How does Augmented Data Management (ADM) fit into this evolving landscape?

Suresh Chandrasekaran: Augmented Data Management represents a significant leap in leveraging AI and ML for data management. Platforms like Denodo are at the forefront, offering features like centralized data discovery, secure access, and governed collaboration through their Data Catalog. ADM utilizes active metadata to learn from user behavior, providing personalized insights and recommendations for data consumption. An exciting aspect of generative AI within ADM is the ability to translate natural language queries into SQL code, democratizing data access for business professionals without SQL knowledge.

CIO&Leader: Could you give a practical example of how generative AI facilitates Data Democratization in ADM?

Suresh Chandrasekaran: A practical application of generative AI in ADM is its ability to interpret and execute natural language queries. For example, a sales manager could simply ask for customer details using everyday language, and the platform, through generative AI, would translate this query into SQL code, execute it, and provide the results. This simplifies data access for non-technical users and ensures that businesses can leverage their data assets more effectively for decision-making.

CIO&Leader: Given the rapid evolution in the tech landscape, what emerging trends do you believe will significantly shape data management and analytics in the near future?

Suresh Chandrasekaran: Emerging trends poised to impact data management and analytics significantly encompass managing AI risks through effective governance and responsible AI practices to build trust and accelerate adoption. Data sharing is evolving, moving beyond internal exchanges to external collaborations, with practical applications of Data Fabric and Data Mesh architectures facilitating this shift. Generative AI is revolutionizing the creation of customer-facing materials and code generation, promising cost reductions and enhanced process efficiency across industries. Meanwhile, as organizations increasingly migrate data and analytics to the cloud, managing cloud costs effectively becomes crucial. Tools and practices like FinOps are being implemented to optimize cloud usage and expenditures. Lastly, data security and governance complexity are prompting a move towards simplified, centralized approaches. These strategies aim to reduce data breach risks and enhance governance across distributed data sources, ensuring data management remains agile yet compliant in a more interconnected and data-driven world.

CIO&Leader: What are the key challenges in global data management today, and what strategies can address these issues effectively?

Suresh Chandrasekaran: The landscape of global data management is navigating through several critical challenges:

1. Data siloes and integration needs: The proliferation of data across fragmented infrastructures presents significant barriers to leveraging data effectively. A logical data management approach that integrates disparate data into a unified access layer is essential. This allows all data consumers within an organization to easily discover and utilize the data they need, overcoming the obstacle of data sprawl.

2. Data privacy and security: With the rise of AI, ensuring the privacy and security of data, especially unvetted training data that may contain sensitive information, is paramount. Implementing privacy-enhancing technologies and adhering to regional regulations and guidelines are crucial steps. For instance, employing a single logical point of access for data can mitigate risks by centralizing and strictly enforcing access security restrictions, thereby protecting sensitive information from potential leaks.

3. Data quality and bias: The issue of poor data quality and inherent biases in training data can significantly impact the outcomes of AI models, leading to incorrect or unfavorable results. Addressing these challenges requires a ‘shift-left’ approach, where data duplication, inaccuracies, and biases are resolved as close to the source as possible. Utilizing platforms that offer robust data transformation, cleansing, and validation functionalities can help maintain high data quality and mitigate biases.

4. Lack of data literacy and skilled workforce: Enhancing data literacy across all organizational roles is fundamental to leveraging data’s full potential. Data democratization, which ensures unrestricted access to data without bottlenecks, is a crucial enabler. Supporting technologies that allow non-technical users to query data using natural language can significantly contribute to this goal, fostering a culture of independence, self-service, and community practice around data.

5. Ethical considerations and algorithmic fairness: The development and deployment of AI must be guided by ethical principles and regulatory frameworks to ensure fairness, transparency, and accountability. Addressing potential biases, especially those that may disadvantage minorities, requires collaboration between public and private entities in developing responsible AI practices and solutions.

Addressing these challenges involves a multifaceted strategy encompassing technological solutions, regulatory compliance, organizational culture shifts, and ongoing education and training. Organizations can navigate the complexities of global data management by adopting integrated data management platforms, prioritizing data security and privacy, ensuring data quality, promoting data literacy, and adhering to ethical AI practices.

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