I recently participated on a panel at a Wall Street Technology Association event titled Innovation, Disruption and You, and wanted to share some highlights and key takeaways from our discussion. The panel of six included representatives from financial institutions (FIs) and technology-solution vendors. The panelists were asked to address the moderator’s questions by first talking about the operational/human issues, and then by covering the role of technology in innovation.
The majority of panelists, and the audience when polled, answered “no” to this question. The primary reasons offered for this answer were:
More and more, larger FIs are announcing how they are transforming into technology businesses in response to external and internal challenges, and to create competitive advantage. This means that the once-clear lines separating business and technology functions within FI organizational structures are fading fast.
To bridge this gap, many financial services organizations are training (or retraining) employees in business lines on how to think more like technologists in an attempt to accelerate digital transformation. A good example of this is the recent announcement from J.P. Morgan about teaching junior asset managers and bankers how to write code.
Beyond coding, FIs will need employees with strong data-science and analytics skills, with the ability to link analytics to creating value for the organization. Job candidates who demonstrate skills related to problem-solving in the workplace, including soft skills such as communication, creativity and teamwork, will be in the highest demand.
While many different “emerging” technologies—such as blockchain, AI and machine learning—were raised, there was general agreement from the panel that for these programs to be valuable, FIs must invest in an optimal data architecture to ensure the highest quality data governance. Poor data governance results in dirty data that derails the development of advanced technology applications, such as predictive analytics and artificial intelligence.
For next-generation technologies to work optimally, a modern data architecture combined with best-in-class data governance practices must first be in place.
The key to measuring the success of innovation initiatives is to create and align around the right metrics for data-driven transformation. Often organizations focus on business case or product metrics, such as the number of customers, adoption rate, revenue, etc., and fail to consider data-management metrics. Data-management metrics can help address fundamental data-governance issues and critical delivery questions like how long is it taking us to deploy new or updated applications and how much is it costing us.
MarkLogic not only provides a complete set of technology solutions to increase the performance of your data assets, but also the solutions engineering and consulting resources to guide you on how to best enable your data-driven transformation efforts.
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