Innovation in data management can improve analytics, generate revenue potential, reduce costs, help companies monetise data and support digital transformation – but there are caveats, including the challenges of legacy systems, lack of budget and skilled resources, and cultural resistance.A recent A-Team Group webinar discussing how to leverage innovation in data management covered these issues and more. The conversation started with the speakers identifying why financial institutions need to innovate data management processes. Linda Coffman, vice president of product management at the SmartStream Reference Data Utility (RDU), noted that the enormous volumes of data that must be managed today have outgrown best practices and that far greater numbers of discerning data users are driving innovation. Also, acceptable margins of data quality have narrowed in recent years and new technologies such as machine learning and artificial intelligence are adding to the buzz.
An early audience poll considering to what extent organisations have innovated data management processes showed 31% of audience members having innovated significantly, 29% somewhat and a further 29% a little. At the top end of the spectrum 6% declared they have innovated to the greatest extent possible, and another 6% not at all.
Considering how to get innovation projects off the ground, Michelle Zhou, enterprise data management and head of the referential data management office at BNP Paribas, said: “You need a fact-based business case. Justify it with statistics and emphasise the necessity of doing the project and the impact of not doing it.” With a business case, management buy-in and funding, a project can get underway, but success is not guaranteed. Coffman said: “Projects fail if delivery is not considered from an holistic standpoint across the organisation. They often look at technology implementation, and at the end of the day, users are unhappy as they don’t get what they wanted.”
The webinar went on to consider innovative technologies and solutions, with an audience poll naming machine learning and pattern and matching tools as current favourites. William Cohee, vice president of data management in the chief data office at HSBC, agreed with the poll results and noted alternatives including big data and cloud. He commented: “If we could combine some of these technologies, it would be a great help in delivering better data management.”
In terms of achieving innovation in data management, the speakers highlighted the need for data quality as a base foundation, and the need to assess how innovation can be integrated into an existing data management environment, perhaps be improving data lineage, developing a data dictionary, and/or implementing machine learning to enhance data quality. As Zhou said, it’s not easy, but the benefits of innovation can be considerable.
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