Deepesh Singh

Deepesh Singh

Principal consultant data management

Banks have the big responsibility for storing and securing huge amount of compliant and sensitive information behind their firewalls. Capitalizing on big data allows them to analyze vast amounts of information in real-time, enabling them to detect patterns and anomalies that may indicate potential risks or fraudulent activity. The proactive approach with AI not only helps in preventing losses but also ensures that they are meeting all necessary regulations and standards set by governing bodies.

On the real-time data, this has a big impact which could potentially help in identifying fraudulent activities and then focus on high-risk anomalies. The ability of a bank to manage risk also affects growth and profits which enables banks to pursue new business models and areas of customer-centric opportunities. Having the appropriate data management with AI strategy in place is key to operational efficiencies, good application performance, and regulatory compliance.

Pinpointing the problems areas

Referring European Data governance act and other bodies, data management challenges can affect a host of concerns. Poor risk management decisions, data loss, data breaches, illegal access, data silos, noncompliance with legislation, an unregulated environment, and a limited number of resources to list some.

Data management pattern

Data management is typically comprised with these key element, and the pattern influences most of the quality attributes. Data is typically hosted in different locations and across multiple servers for reasons such as performance, scalability, or availability, and this can present a range of challenges.

Data management pattern
Figure 1. Data management pattern

ETL service for scale-out serverless data integration and data transformation

For an instance, Microsoft comes up with an eco-system, it provides an ease to integrate with their own products and are tightly coupled. For this Fabric is there to build a stronger data foundation for the AI era.

Without proper oversight and regulation, there is a potential for misuse and unintended consequences in the development and deployment of generative AI technologies. It is crucial for stakeholders to address these challenges proactively to ensure the responsible and ethical advancement of this technology. This includes establishing clear guidelines for data usage, implementing transparency measures, and fostering collaboration between industry, government, and academia to promote ethical practices. By taking these steps, we can harness the benefits of generative AI while minimizing risks and protecting individuals' rights and privacy.            

Custom-built data collectors

Data is necessary, while the creation of the data pipelines is based on a variety of common sources, including mainstream databases and cloud storage services, there is a need to write custom code to configure nonstandard data sources. This leads to a lot of challenges; for example, you need to rely on proprietary databases that can't integrate with Azure or available with the prebuilt connectors. This can result in longer development times and add complexity to the data integration process.

Pivot on Cloud leaders

The in-house services helps in building the integration pipeline that connects within or other resources. The extended features provides more flexibility in choosing the best tools for the job, regardless of any challenges  such as where the data is hosted and the transformations. Utilizing a third-party offering can help prevent vendor lock-in and provide a more customizable solution for complex data integration needs.

Nevertheless, a multi-cloud strategy could be an option but also has a downside, to stay ahead of the competition and drive innovation in the rapidly evolving technology landscape leveraging AI capabilities, companies can streamline operations, enhance customer experiences, and unlock new revenue streams.


It is about Data and data governance practice. Without the three components – algorithm, computing and data; AI systems would not be able to function effectively. GenAI simply represents a more advanced and efficient way of utilizing these elements to achieve desired outcomes.

Many organizations build AI models, but that has to be production-level ready. Those who understand the importance of data governance and successfully add an AI flavor - will be successful in the long run.

Over de auteur

Deepesh Singh

Deepesh Singh

Principal consultant data management

Deepesh is a principal consultant with extensive knowledge in the field of data management. He has successfully led cross-functional teams in delivering complex projects. His ability to think strategically and drive innovation has consistently resulted in exceeding client expectations. Since 2010 he has built up ...