One of the main challenges that public bodies face in effectively leveraging their data is the complexity of the data management process that is prevalent in many of these organizations today.
Traditionally, public bodies often delegate the management of these operations to the most prominent system integrators. They pay substantial sums in order 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 opportunity costs related to the suspension of benefits due to the time required to develop these new processes.
There is bound to be a better approach.
Responsibility for data
According to a study published by the firm Korn Ferry, the shortage of talent in the field of technology could represent a shortfall of around 175 billion euros for the French economy by 2030. Since the pandemic of Covid-19, digital transformation projects in the public sector, and in particular around data, have multiplied as all players have become aware of the need for easier access to more reliable data. However, the complexity of the architectures, coupled with a lack of flexibility, scalability or resources, made it more difficult for organizations to develop innovative systems to encourage citizens to adopt new services faster and more widely.
In order to guarantee the success of public bodies in their data-oriented projects and before launching the process of selecting solutions, the first essential step consists in creating a culture of data, and more broadly of digital, intended to be deepened thanks to the training of all employees. Any data literacy program must first be promoted at the highest level of the organization and involve both data experts, human resources and managers from other departments. Data literacy plays an even more crucial role for the public sector, due to the sensitive nature of the data processed. Once this data culture has been implemented and the employees trained, these new data citizens will have to be able to answer the following questions: “What types of data are processed? Where do they come from? Where are they stored? And what are they for? This helps ensure the development of data awareness and a sense of responsibility.
Faster access to reliable data
The complexity of infrastructures, the existence of obsolete solutions inherited from old approaches and other management methods generally prevent public bodies from becoming more efficient and providing the level of service expected by the generation of “digital natives”. Simplifying and modernizing access to public data requires increased use of digital tools and training of talent. Failing to benefit from it, it will be complicated for public bodies to achieve a degree of excellence in the field of data and digital technology. To ensure quality, accuracy, and compliance while building trust in data across the organization, applications need to be smarter and deliver insights and automation capabilities from the point of data ingestion through to final processing, as well as all the steps in between.
Automating and augmenting processes using artificial intelligence (AI), machine learning (ML) and other technologies drives efficiency and enables decision-making supported by more accurate and accurate information. more reliable. These new technologies further help organizations overcome the resource constraints they face, as well as other current data workforce limitations.
Unified data management and integration solutions track and visualize the complete history of data quality to help users identify risks and other issues that could render data unusable. Then, this history can be applied at the micro or macro scale by creating logical groups of datasets that correspond to business needs. Ideally, users should benefit from a more holistic view within a data console that not only highlights data quality and risk issues, but also provides recommendations for taking action on the data. which are problematic.
Governance as the backbone of data management
Data governance is one of the key components to ensuring greater agility and productivity, as well as ensuring regulatory compliance. Ever-increasing data protection regulations, coupled with ever more complex AI and ML models that must be based on comprehensive sets of sound data, are giving rise to new use cases to eliminate risks and encourage data sharing initiatives.
Governance is not only a fundamental building block for compliance and data privacy efforts; it also allows companies to benefit from a holistic understanding of all their data, while emphasizing its accuracy and relevance to the specific business needs of each organization. Government agencies should look to platforms that can manage data across its entire lifecycle, while offering a data catalog solution that includes metadata management capabilities with data visualization and lineage capabilities. data. These platforms make it possible to have a complete view of the data and their use as well as to deal with problems at source. Custom metamodels, for example, ensure complete flexibility – rather than relying on pre-determined templates, users are then able to define and configure requirements that fit perfectly into the context of their organization’s business processes.
The State’s digital transformation projects, in particular through the government’s cloud strategy initiated in 2021, devote a primordial place to data. To achieve this level of excellence and provide citizens with an optimal digital experience, public bodies must use unified and scalable platforms that guarantee the reliability, accessibility and monitoring of data.