(US & Canada) | CDOs Should Understand Business Strategy to Be Outcome-focused — Interac Corp. VP Head of Data Analytics and Fraud
Palash Thakur, VP, Head of Data Analytics and Fraud at Interac Corp., speaks with Nazar Labunets, Product Marketing Manager, Ataccama, in a video interview about the role of communication in the success of data analytics function, tips to CDOs, and the organizational path to creating data products.
Interac is a Canadian interbank network that links financial institutions and other enterprises to exchange electronic financial transactions.
Thakur believes that the success of a CDO or a data analytics function is driven by promoting and communicating smart work. He begins by stating that the aspect of storytelling and communication is critical at Interac, and the organization is involved in knowledge-sharing sessions.
To ramp up the communication, organizations must reach out externally to academia or partner organizations, says Thakur. Both internal and external marketing are crucial to driving the data-centric culture and helping to find the right talent. He states that promoting good work makes it easier to attract the right talent.
Sharing advice for CDOs or those in leadership roles, Thakur lists out his top three tips:
Be outcome-focused
Focus on foundational capabilities
Create a solid engagement model
To be outcome-focused, the CDO has to prioritize understanding the business or corporate strategy, he says. In addition, one needs to comprehend the organizational aspirations and how to deliver on key business outcomes, which could include monetization of commercial opportunities, risk mitigation, cost savings, or providing client value.
Next, Thakur advises leaders to focus on the foundational data and analytic capabilities to drive business outcomes. There must be a well-organized data and analytic strategy to start with, a good tech stack, an analytic environment, data management, and governance principles.
While delivering on some of the use cases may take time, it is imperative to have quick wins along the way, says Thakur. He recommends CDOs create reusable data products and assets while having an agile operationalization process.
Then, Thakur suggests data leaders create a solid engagement model to ensure that the data analytics team is in sync with business and product owners. He urges leaders to put an effective ideation and opportunity management framework into action to capture business ideas and prioritize use cases.
In continuation, Thakur reiterates the importance of acquiring and building the right talent to foster a data-driven culture within the organization.
Moving forward, he sheds light on the data product management framework, emphasizing data products and innovation. Elaborating, Thakur says that Interac has embedded incremental innovation and a continuous improvement mindset to better its products and services by leveraging data analytics.
From using principles of design thinking for ideation to following agile delivery methods, the organization follows a systematic approach and stays abreast of technological advances while creating data products.
An organization should focus on investing well in internal research and assessing trusted partner networks, affirms Thakur. It is necessary to understand what takes the business forward, what strategies lead to competitive advantage, and whether it brings efficiency and drives automation.
Organizations must consider all such aspects and, in the process, start building data products and assets, says Thakur. He continues that organizations must examine how data products can be created in the same way as business products and align the opportunity with the demand.
Before embarking on the journey, organizations also need to have the right data quality and data privacy measures, asserts Thakur. The path to data productization is long and must be trodden carefully with the right intent and awareness.
According to Thakur, a data product is defined by the type of data an organization has and the governance around that. Speaking of payments and the financial industry, there is massive transactional data that must be considered.
For instance, deriving insights for end clients by looking at spending patterns and behavior based on payment transactional data would be compelling, says Thakur. The next use case he refers to is around fraud detection and prevention, wherein data is the enabler.
Interac looks at transactions on a real-time basis using advanced machine learning techniques, transactional behavior, user and sender behavior, and more external data. Consequently, combining all the factors to come up with a fraud detection score is probably one of the most powerful products in the financial industry, Thakur concludes.
CDO Magazine appreciates Palash Thakur for sharing his insights with our global community.