Big data analytics: The case of the social security administration

The paper analyses the the efforts undertaken by the United States Social Security Administration (SSA) regarding the adoption of big data analytics. Thereby, the paper aims to foster a strong understanding of the opportunities and challenges associated with the adoption of big data analytics in the public sphere.

The authors conclude that the SSA has made great strides in the burgeoning big data space to improve administration and delivery of services. This has included: (1) improving its arcane legacy system, (2) developing employee and end-user capability, (3) implementing data management strategies and organizational architecture, (4) managing security and privacy issues, and (5) advocating for increased investment in big data analytics.

Type of content: Assets
Type of asset:
Use case
Big data potential
Policy domains: Agriculture, Fisheries, Forestry & Foods Economy & Finance Education, Youth, Culture & Sport Employment & Social Security Foreign Affairs and Defence Health Innovation, Science & Technology Justice, Legal System & Public Safety
Phase in the policy cycle:
Agenda Setting
Policy Design and Analysis
Policy Implementation
Policy Monitoring and Evaluation
Open license availability
Ease of use
Tags: BI Data analytics IT IT processes
SWOT Analysis for
Big data analytics: The case of the social security administration
Helpful Harmful
Strengths• Transform organizational decision making
• Increase process efficiency
• Identify future areas for innovation -Engage citizens in the policy analysis, design and implementation process
• Address social issues
• Seize economic and social values
• Growing interest in the use of Big Data
• Large amounts of data is being collected and liberated
Weaknesses• Modernizing legacy IT systems is complex. Public agencies considering IT investments should allocate significant resources
• Public agencies are not prepared to take advantage of big data
• Developing of new data governance needed
• Public agencies have a poor track record of investment in IT
• Risk of poor quality conclusions and insights gathered from the data
Opportunities• The advances in information and communication technologies (ICT) have provided public institutions, businesses, non-governmental organizations (NGOs) and citizens with new platforms and media for generating, sharing and applying data
• Online platforms are increasingly becoming important mediums to communicate and share information in the policy realm
• Developing systems architecture,
• Cultivating a culture of cross-agency collaboration: use of similar platforms, makes it easy to collaborate with one another, cross-verify sources, reduce redundancies, and provide enhanced service delivery
• Consolidating databases
• Adopting crowd-centric approaches: many federal agencies have moved beyond their organizational limits and have begun to engage the public in decision-making processes
• Managing issues of data security
• Investing in employee training and capacity building,
• Developing collaborative leadership and management support
• Creating resources to streamline service delivery to end-users
• Developing metrics to measure performance: evaluate the actual outcome of investments and the impact they have on the organization’s capacity to deliver services
• Granting public access to administrative data can potentially
• Allow for the Development of innovative tools public agencies must consider all applicable data governance laws and regulations
Threats• Confidentiality: issues related to confidentiality of big data analytics. Although public agencies take steps to mask and de-identify personal information, recent research in the big data sphere suggests that it is possible to re-identify individuals in public datasets.
• Data privacy: Given the granularity of data collected to derive evidence-driven customized solutions, data leaks can lead to disclosure of sensitive information, loss of public trust.
• Unintended use of big data analytics
• A sub-section of the population might benefit at the cost of others
• Deriving patterns from a large volume of datasets depends on the analysts’ capability to explore the datasets several times to understand discover relationships and test different hypotheses (use different approaches to analyse the same datasets. Analysis of big data requires new kinds of significance tests or other validation techniques that gauge the temporal variability to discover patterns and relationships.

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