Data Management

Data Management

Data may be the new gold, but extracting business value from unmanaged data can be a complex and frustrating exercise.

  • Without data management data quality suffers. Duplicate records abound and information is scattered. With it, your data is under control.
  • Without data management your data is opaque. With it, you can easily find information. You can ask questions and get answers.
  • Without data management, your data integrity is suspect — anyone can change it. With data management you see where the data is coming from, who added it, who changed it, when, why and how it is used. Regulators are increasingly demanding this auditability.
  • Compared to modern data management implementations, most legacy data architectures perform slower, have a higher cost of ownership and have weaker security.
  • Most importantly, it’s becoming impossible to compete effectively without accurate and actionable data and analytics. It is also becoming impossible to obtain those without effective data management.

Data Extraction & Management Challenge:

A global credit analytics firm needed to improve the process of extracting data from the many disparate sources it relied upon. The mostly manual methods used were inefficient and error prone. In addition, the extracted data was stored sub-optimally with little auditability.


Based on a comprehensive review of the firm’s information requirements and workflow, DataArt built a next generation data intake and analytical platform. It features:

  • Automated data extraction from tabular and textual sections of PDF financial reports.
  • Data mapping to a standardized taxonomy, with accommodation for non-standardized data.
  • Greater availability of services and resources (built in disaster recovery).
  • Custom processing engine incorporating machine learning.
  • Full auditability of data back to original source.
  • Harmonized platform including a web based user interface.


  • Quicker processing yet twice as precise as previous system.
  • Non-standard data captured and available for analytic purposes.
  • More efficient taxonomy mapping.
  • Harmonized platform is ‘shovel ready’ for other business lines.
  • Reduced risk and increased regulatory compliance due to audit features.

Data Integrity Challenge:

A large private equity firm wanted to improve its strategic reporting and data analytics capability. However, there were issues with the integrity and quality of the data in its reporting platform. The platform played a central role in key business activities such as investment management, fund monitoring and external communications. The firm had a data warehouse and a range of Business Intelligence and reporting tools. But the data sourced from it was not fully trusted nor was it easily understood and used.


DataArt conducted a thorough analysis of the data management and reporting architecture. It identified many issues, including multiple, slightly different, versions of key calculations.

Based on this, DataArt methodically evolved the data warehouse and reporting platform towards a service-oriented, metadata driven, layered design. It implemented data governance processes to define the responsibility for various data elements and establish solid data lineage. It also introduced metadata analytics that identified data usage patterns.


  • Significantly lower time required for data discovery and integration.
  • Dramatically Increased the business’ trust in the data.
  • More robust and consistent data governance.
  • Enabled traceable data lineage and comprehensive data usage analytics.
  • Platform ready for use by other departments in firm.

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