Tell us what leadership means to you in the context of your role as a senior data leader.
Leadership means to aspire, inspire, innovate and execute strategic thinking leading to action. In today’s world, data has tremendous, often untapped potential. As a data leader, identifying areas of opportunities is key to the success of any business. An important aspect of it lies in creating awareness about the potential of data and its by-products. Data coupled with machine learning, deep learning and powerful data analytics and reporting yields a plethora of prospects across an organization. Identification and effective communication of the unexplored avenues is a challenging and a salient leadership opportunity.
Furthermore, creating a flexible and transparent work environment with diverse teams is an important leadership aspect while creating products that serve the community. Presenting younger individuals with opportunities for learning and receiving mentorship for shaping their skills and career path is a worthy investment for any leader.
And what about the skills of your data teams and of your business stakeholders? How are you developing data literacy across the company/organization?
Communicating the meaning of the complex technicalities of data models can be highly challenging. Stakeholders may or may not have a technical background. The key lies in effective communication and leading it based on the audience. For instance, how you would explain artificial intelligence to an individual with a sales background, a computer engineer, a fresh graduate, and a machine learning researcher of ten years would all be very different. Conducting “brown bags” has been helpful in spreading data literacy throughout the organization.
Different teams within an organization comprise different expertise, perspectives and backgrounds. Creating a two-way communication helps understand the business use cases, solve critical needs with the right evaluation metrics, and unlock the true power of data. A constant communication loop further helps receive early feedback and provide quicker and effective algorithm iterations. Furthermore, presenting the data and model outcomes in a manner that can be understood and interpreted by different teams aids in simplifying the process.
