In order to more effectively transform data into competitive advantage, French international banking group BNP Paribas built a state-of-the-art Enterprise Data Nerve Center. Simply put, the goal of this center of excellence (CoE) is to democratize data analytics, artificial intelligence (AI) and machine learning (ML).
By equipping everyone across the enterprise with easily accessible, comprehensible and actionable data-driven insights, this “nerve center” has unleashed a new era of data-backed decision making. However, democratizing such tools in an organization as massive, complex and regulated as BNP Paribas requires a high level of synchronization, cross-functional communication and a well-constructed, modernized data architecture framework.
To shed light on the strategic thinking and technical innovation behind BNP Paribas’ enterprise-wide data & AI strategies, we’ve invited Adri Purkayastha, Global Head of AI and Digital Risk Analytics, BNP Paribas to share his perspective at the upcoming AI & Data Democratization Virtual Event taking place this April 27 - 28, 2021.
Below are 5 key themes and points of discussion Mr. Purkayastha will be diving into during his session titled “Winning With AI Through The Enterprise Data Nerve Center.”
Internal and external data integration
By integrating internal data with external data, data scientists and business users alike can perform more sophisticated, meaningful analysis.With BNP Paribas’ enterprise data nerve center, users can mine data from thousands of sources and leverage it to develop predictive insights.
Enabling data-driven decision making
Though the long-term goal of data democratization is to create “citizen analysts” - a.k.a. business users who possess the skills necessary to properly analyze data and potentially even develop their own models. However, especially at a company as large and multifaceted as BNP Paribas where analytics expertise varies greatly across various teams, accomplishing that goal may not be totally realistic is not completely in reach at this point.
This is why it is so important to work closely with internal customers to ensure that analytics offerings are accessible, align with business objectives, and are actionable no matter the level of technical expertise. The ultimate goal is to deliver a seamless user experience that allows the customer to focus on decision making vs. worrying about the technical aspects of data analytics (i.e. modeling, architecture, privacy etc.)
Capturing data in motion to accelerate decision making
Like many organizations, BNP Paribas is experimenting with new ways to deliver data-driven insights in real time (or as close to it as possible). Using streaming analytics and other various data architecture approaches, they’re enabling the business to make decisions as events occur.
Leadership buy-in has a direct impact on AI success
AI requires a long-term investment and a high level of strategic alignment; two things that aren’t really possible to achieve without support from the C-suite. As Adri puts it, just because “you have a cool data science team, that doesn't mean you have the best analytics driven organization, because it has to tie down to apps development, architecture development, privacy, governance and so on.” In other words, the strategic direction for AI has to come from the top level to ensure people, process and technology are harmonized.
Data Monetization
Companies are increasingly embracing data monetization - the process of using data to obtain quantifiable economic benefit. Especially for companies with smaller data budgets, data monetization initiatives can be a highly effective way to not only generate more capital but establish data analytics as a key driver of revenue growth.
However, to monetize the information, you have to think about your information architecture, mission, vision, culture, and data strategy to ensure they’re all aligned.
……….And this is just the tip of the iceberg. Register for AI & Data Democratization FREE Virtual Event taking place this April 27 - 28, 2021 to hear the full story.