Sapient Global Markets, a division of Sapient (NASDAQ: SAPE), today announced the “Sapient Global Markets Sentiment-Based Data Management Maturity Assessment” (DMMA). Unlike evidence based data management assessments, the DMMA evaluates stakeholder perceptions of data management maturity across the organization.
By taking the DMMA survey from Sapient Global Markets, firms will be able to:
- Benchmark and heat map the maturity of data management across the enterprise.
- Gain insights to help drive your data management strategy forward.
- Use implied prioritizations from the assessment to better inform budgetary
spending decisions. - Predict the effects of certain initiatives or scenarios on data maturity.
With data sitting at the heart of all activities across the capital and commodity markets, market participants, regulators and intermediaries alike need to gain a clearer, deeper understanding of their data management practices across the enterprise. Understanding that every firm’s culture is different; Sapient Global Markets offers variable levels of engagement, from providing the self-assessment to full facilitation and assistance with results interpretation.
“One of the biggest roadblocks to successful data management programs in today’s environment is engagement from senior stakeholders across the organization and the alignment of deliverables against priorities,” said Gavin Kaimowitz, director, Sapient Global Markets. “Historically it has taken companies far too long to understand their current state of data management related programs. Sapient Global Markets DMMA will help overcome some of these roadblocks by providing an expedited framework from which to base strategic data management decisions.”
The DMMA is based on the Enterprise Data Management Council’s (EDM Council) recently developed and industry recognized Data Management Maturity Model (DMM) and is designed to help benchmark the maturity of data management initiatives across the enterprise.Results will help firms get a better view of their data in order to make more informed decisions on alignment of strategy, implementing governance mechanisms, managing operational components, defining dependencies, aligning data with IT capabilities, ensuring data quality and integrating data into business processes.
“Enterprise data governance is a peopleand process-centric practice that guides decision making in regard to creating and using business information, holding data decision makers accountable for their decisions, and providing consequences if decision makers, or their subordinates, fail to abide by governance at an enterprise level,” wrote Mary Knox, research director, Gartner and author of the report, Hype Cycle for Bank Operations Innovation, 2013.“While not itself a technology, it can be enabled by technologies such as database management systems, data integration and data validation tools.”
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