One of the main challenges that governments face in effectively using their data is the complexity of the data management process that is prevalent in many of these organizations today.
Traditionally, government agencies often delegate the management of these operations to the most prominent system integrators. They pay significant amounts to benefit from the development of software solutions, to which are added additional amounts for the services necessary to maintain the architecture. In addition, they are subject to the opportunity cost of suspension of benefits due to the time required to develop these new processes.
There’s bound to be a better approach.
Data responsibility
According to a study published by firm Korn Ferry, by 2030 a shortage of tech talent could result in a shortfall of around 175 billion euros for the French economy. Since the Covid-19 pandemic, digital transformation projects in the public sector, and in particular in relation to data, have multiplied as all players realized the need for easier access to more reliable data. However, the complexity of architectures, combined with a lack of flexibility, scalability, or resources, makes it difficult for organizations to develop innovative systems that encourage citizens to adopt new services faster and more widely.
In order to ensure the success of governments in their data-centric projects, and before launching the decision-making process, the first important step is to create a data culture and, more broadly, a digital culture, which is supposed to be deepened through training. all employees. Any data literacy program must first be promoted at the highest level of the organization and include both data experts and HR professionals, as well as managers from other departments. Data literacy is even more important for the public sector due to the sensitive nature of the data being processed. Once this data culture is in place and employees are trained, these new data citizens will need to answer the following questions: “What types of data are processed? Where are they from? Where are they stored? And what are they for? This helps ensure that data awareness and a sense of responsibility are developed.
Faster access to reliable data
The complexity of infrastructures, the presence of outdated solutions inherited from old approaches and other management methods, as a rule, do not allow government bodies to become more efficient and provide the level of service expected by the generation of “digital natives”. Simplifying and modernizing access to public data requires greater use of digital tools and training. Failing to capitalize on this, it will be difficult for government agencies to reach a certain level of data and digital excellence. To ensure quality, accuracy, and compliance while building trust in your organization’s data, applications need to be smarter and provide insights and automation from data ingestion to final processing and everything in between.
Automating and extending processes using artificial intelligence (AI), machine learning (ML) and other technologies improves efficiency and enables decision making based on more precise and accurate information. These new technologies are also helping organizations overcome the resource constraints they face, as well as other current data workforce constraints.
Unified data management and integration solutions track and visualize a complete data quality history to help users identify risks and other issues that could render data unusable. This history can then be applied at the micro or macro level by creating logical groups of datasets that meet business needs. Ideally, users should get a more holistic view in the data console that not only highlights data quality and risk issues, but also provides guidance on how to deal with problematic data.
Governance as the basis of data management
Data management is one of the key components for greater agility, productivity, and compliance. Ever-growing data protection regulations, coupled with increasingly sophisticated AI and machine learning models that must be based on comprehensive data sets, are leading to new use cases to mitigate risk and drive data-sharing initiatives.
Governance is not only a fundamental building block for compliance and data privacy; it also allows companies to benefit from a holistic understanding of all their data while highlighting its accuracy and relevance to each organization’s specific business needs. Government agencies should look to platforms that can manage data throughout its lifecycle while offering a data catalog solution that includes metadata management capabilities with data visualization and data origin capabilities. These platforms allow you to have a complete understanding of the data and its use, as well as solve problems at the source. Custom metamodels, for example, provide complete flexibility—instead of relying on predefined templates, users can define and customize requirements that fit perfectly within the context of their organization’s business processes.
Government digital transformation projects, in particular as part of the government’s cloud strategy launched in 2021, are prioritizing data. To achieve this level of sophistication and provide citizens with an optimal digital experience, governments must use unified and scalable platforms that ensure data is reliable, accessible, and monitored.