12-03, 12:00–12:30 (UTC), Talk Track I
Abstract
There are many stories about Data Science hires that end up working in silos, buried in ad hoc business requests. According to Gartner, only 20% of analytic insights will deliver business outcomes in 2022. And a large number of Machine Learning Models never go to production. On top of that, work satisfaction among data professionals is staggeringly low; for instance, 97% of data engineers reported feeling burnt out in a 2021 Wakefield Research Survey. Furthermore, despite living in the era of information, many business executives are making decisions based on guesswork because of the need for more relevant data access in a timely fashion. This talk covers why many data initiatives fail and, more importantly, how to prevent it. I lay out a number of practical approaches based on work experience that will help you to unlock the potential of data and analytics — from how to build the case and gain buy-in to promoting a fact-based decision-making culture. This talk is for you if you are a business leader sponsoring data initiatives, if you work in data applications, or if you would benefit from enhanced analytics.
There is no doubt that unlocking value from data and analytics has a direct impact on business performance metrics, including profitability and customer satisfaction. Furthermore, in an uncertain world, the ability of businesses to harness their data potential is more crucial than ever. Nevertheless, delivering successful data-driven initiatives is not as straightforward as it may appear. This talk will focus on actions you can take to extract value from data and analytics.
No previous knowledge expected
Maria Feria has over 15 years of experience working in the data and analytics space. Her journey in the field began in the intersection of Environmental Sciences and Geographic Information Systems where she created empirical data models and developed distributed geospatial databases.
She specialises in Data Engineering and has worked on leading data initiatives in a number of large corporations as well as startups. From productionisation of Machine Learning Models on a global scale to Big Data Migration, Data Governance and Management.