Asset Management Challenges

About the


A leading U.S. based Asset Management service provider is on a mission to optimize distribution strategies and improve the performance of assets using various data from Investment Accounting System, Trading Systems, Banking, Compliance and Billing systems, etc. Key to this strategy is to leverage modern data preparation, AI/ML based data quality checks to streamline and automate as many processes as possible while maintaining 100% regulatory compliance, privacy, and security.

As a result, they were able to address multiple demands such as new regulatory requirements, new products and asset classes, better risk management, and changing customer expectations. Further costs were kept in lines with high-performance assets.


Financial, Investment and Asset Management

  • Customer 360
  • Personalized experience
  • Outreach and Engagement
  • Data Ingestion
  • Data Preparation
  • Data Quality
  • Data Curation
  • Data Catalog


By using Data Ingestion and Preparation, we consolidated all data to streamline and automate as many processes as possible.


Partner at Investment Team



An asset management company found themselves dealing with an array of challenging and interrelated issues relative to client expectations and distribution and set forth a journey to integrate a data-driven digital transformation around all of its investment management lifecycle. By this way, they shall address the challenges around growing demands of the new regulatory environment, growth of emerging markets, the rise of low-cost providers. With a data-driven strategy, they hoped to create a high-performance asset management business model.

Like most organizations, they faced a number of challenges: the growing amount of wide variety of data sources and disparate systems; changing needs of regulatory and reporting compliance; silos and vested interest; disorganized or inaccurate data; the inability to innovate and scale; manual process and workflow, — the list goes on. All making it impossible to collaborate and leverage the data for their specific needs around engagement and

Without knowing where to start and what can be done for their organization, it was challenging to bring data silos together especially when some are in cloud, on-premise and of different structure, form, and sizes. Further, the underlying data also had quality issues and normalization of data was a big challenge with each system have its own terminologies and taxonomies.

Implementing stand-alone tools were short-lived as the success was very limited and couldn’t be extended into a digital transformation across all business processes around the investment management lifecycle. Further, it required silo efforts, and economies of scale weren’t leveraged. The firm knew they’d need to consolidate and innovate.


Ease in duplicate by cross joining data under one repository

DQLabs gives organizations the ability to manage data smarter and leverage an immediate ROI in weeks, rather than months.


President and chief executive officer at



DQLabs converges all data management capabilities into one platform with aggregated and integrated capabilities as a driver to measure organizational level data quality. By ingesting data from silos and bringing in one consolidated view using out of the box connectors helped the firm for the first time to cross-compare data across silos. Also, with the automated ability to sense and create catalog at all levels of metadata – defined, inferred helped understand what data was available to use, and how internal data assets can be used to improve internal processes.

With DQLabs’ automated data governance capabilities, all diverse digital business requirements were managed under one unified platform. Also enabled the firm to see data as a strategic asset and use to maximize its value through proper management and control.

“We used DQLabs to help aid our digital transformation in automating the investment management life cycle. Data from private assets – direct ownership in companies; startup; private equity; asset-specific investment including real estate and public assets – stocks; bonds; currencies; commodities that are publicly traded were all brought under one platform DQLabs to cross-compare, combine and use it for comprehensive reporting, and the process involved merging a variety of data sources using out of the box connectors including even complex types such as PDF, and further autonomously validating for checks and balances across cross data sources. Upon each data source were cleansed; it was fed to reporting models for further reporting and analytics needs.

Now with all the data in one platform, analysts and other business stakeholders were able to reconcile cash flow, govern the security master for consumption of external application, generate alerts and flag anomalies in data helping to improve the performance of each investments. DQLabs enabled a simpler, innovative way of aggregating data, profile, cleanse for portfolio management and investment analysis. With the integrated data, more reporting and robust predictive analytics supporting “what-if” analysis can forecast impact on fund performance by new combinations of strategies or fund managers to improve overall efficiency and increase business revenue.

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