Host
Dianne Douglas
Podcast Content
Conversations about data science typically contain buzzwords and generalizations that make it difficult to understand their relevance in governance and policy. Even if well-worded, private-sector data science applications can sound pretty alien to public officials. Many aspects of the business can be strongly influenced by analysis, but not all in the same way.
This is understandable, because the problem Netflix and Google are trying to solve is the same as the problems that think tanks and other data science companies in the private sector are focusing on. There is a priority of public protection, which means that if you work with very sensitive information and you are not able to be completely open about how it works, you find yourself in a very different situation from the private sector.
Data science projects should always start with a clear political and operational need, which is important for the first point for two reasons. First, participants are more likely to support a data science project when there are clear public benefits and when they can see the benefits of the project in terms of benefits to the public, not just the private sector.
Participants are more likely to support a data science project if the government uses sensitive data to fight terrorism, for example.
In public research, high technological competence does not necessarily lead to support for data-based methods. Inborn trustworthiness is important because not everyone wants to get involved, and not only does everyone want their data to be used appropriately by the government, but everyone wants to know that it is being used appropriately. Publishing an ethical framework for data science is an important first step, but engagement is inherently trustworthy. By engaging in building trust in data science methods, we can use it to understand how people value the entire policy area and whether they would be willing to use people's data to solve it. Data science methods are often used with scepticism and sometimes suspicion, owing to their lack of transparency and transparency requirements.
The biggest challenge for the government is to double the amount of citizen data that invalidates data analysis results due to duplication and lack of transparency. This alone does not solve all of the challenges identified, but it demonstrates the need for improved trust in data science methods and a greater focus on improving trust in data.
There are many technologies related to data analysis that have received a lot of hype, including IoT, AI and machine learning. In any area of IT, too much focus can be placed on following the hype, and Howard urges government CIOs not to engage in hype themselves by promising new technologies, but to develop targeted and strategic use cases and business requirements that can be effectively addressed with improved data and analysis.
By creating programs that address data scientists who consider government positions, cities can attract dynamic, data-competent personnel who can embed and distribute data competences at various levels of city government. Whether by creating a CDO, setting up an analysis team, or simply embedding a data scientist in another department, the ability to establish a data literacy role with the support of the executives allows city officials to develop the analysis projects they need most. By engaging data protection professionals with expertise and developing training programs that provide critical career development opportunities for City Hall staff, a city can improve its internal capacity.
Know how to develop data science skills and knowledge through self-directed learning and understand how data science products can be deployed based on business and user needs. Communicate the benefits of Data Science products, including data analysis and visualization, with media tailored to your audience by using a variety of media such as video, audio, text or other media.
Understand that data governance is necessary and you can apply data science ethical framework to your work. As an industrial manager, you will gain more knowledge and be prepared for a wider range of different employment opportunities. To make use of data, the University of New South Wales in Australia offers a Master of Data Science that can give you further insights into the future of big data.
See how you can create a dynamic career in the field of data and discover what the Master of Data Science has to offer. Since most jobs as a data scientist require a strong understanding of data science, data management and data analysis, you need to be convinced of that yourself.