Oracle Data Platform for Public Sector

Public sector—social services needs assessments

Understand social service needs and deliver better targeted care with advanced analytics

The social care needs assessment process plays an important role in ensuring the well-being and support of individuals who require assistance due to physical, mental, or social challenges by helping to identify and understand their specific requirements. By assessing an individual's physical, emotional, and social needs, professionals can develop a broader and more nuanced understanding of their circumstances. This allows them to create personalized care plans that address each person’s unique challenges, which can foster a more effective and client-centered approach to social care.

The assessment process is important for several reasons, including the following:

  • Promoting and preserving independence: By identifying the areas where individuals may require assistance, care providers can focus on empowering them to maintain autonomy in their daily lives. This can involve implementing support systems that enhance their ability to perform everyday tasks, which can contribute to their self-sufficiency and sense of dignity.
  • Tailoring support while controlling costs: The assessment process is a means of early intervention. Identifying potential issues or challenges in their early stages allows for timely and targeted interventions, which can help prevent problems from escalating. This proactive approach not only helps improve the overall quality of care, but it can also result in cost savings by avoiding more extensive and expensive subsequent interventions.
  • Enabling collaboration among different stakeholders within the healthcare system: The assessment process promotes effective communication between healthcare professionals, social workers, and other relevant parties. This collaborative approach helps ensure that all aspects of an individual's well-being are considered, which can lead to a more comprehensive and integrated care plan.
  • Allocating resources efficiently, fairly, and equitably: By accurately gauging the level of support an individual or cohort requires, social care policymakers can allocate resources efficiently, targeting areas of greater need. This not only optimizes the use of available resources but also helps prevent the under- or over-provisioning of services, making the entire social care system more sustainable and responsive.

Social care resources are often limited, and they must be allocated efficiently to those who need them most. Analytics-driven needs assessments help identify individuals and communities with the greatest needs, which can help ensure that resources are directed where they can have the most significant impact. Identifying needs early is crucial in social care, as it allows for timely intervention and support. Early intervention can prevent problems from becoming more severe and costly to address. And by analyzing the outcomes of different interventions and assessing their impact on an individual’s well-being, care providers can continue to refine and optimize their approaches.

In recent years, the use of data and analytics has emerged as a transformative force in social care, offering unparalleled opportunities to improve the effectiveness and efficiency of social care needs assessments. Together, data and analytics provide a powerful toolset for social care professionals, helping them understand, predict, and respond to the diverse needs of individuals and empowering them with the evidence-based insights they need to make informed decisions.

A data-driven approach to assessing social care needs allows care providers to develop detailed, accurate personalized intervention strategies to help enhance the precision and effectiveness of social care. By aggregating data from various sources, including health records, social interactions, and demographic information, care teams can develop a comprehensive understanding of an individual's circumstances and needs. Advanced analytics allows for the development of predictive models that can identify individuals at risk of deteriorating health or social well-being. By analyzing historical data and patterns, these models can help care workers anticipate potential issues before they escalate, enabling early intervention and helping to prevent crises and reduce the overall burden on social care services.

Data and advanced analytics also enable care teams to take a proactive approach to social care, which can benefit not only individuals but also the overall social care system. For example, machine learning algorithms can analyze vast data sets to identify patterns and correlations that may not be immediately apparent to human observers. Care providers can use this information to offer interventions that are both tailored to the individual's current needs and anticipatory of future challenges, which can help create a more dynamic and responsive social care system.

Additionally, real-time data streams from devices such as wearables and smart home sensors can provide valuable insights into an individual's daily activities and care status and make it possible to continuously monitor their needs and the effectiveness of interventions in real time. This allows for both reactive intervention and adaptive planning, where care teams can adjust interventions in response to changing circumstances.

Identify and provide optimized and individually targeted social care services with a comprehensive data platform

A data platform that can ingest, curate, process, and analyze data related to care needs and service delivery can empower stakeholders in the social care sector with data-driven insights to help them identify, assess, and address the diverse and evolving needs of individuals and communities. Data analytics, artificial intelligence, and machine learning have the potential to help organizations optimize resource allocation, enhance service delivery, and ultimately improve outcomes for vulnerable populations. The architecture presented here demonstrates how we can combine recommended Oracle components to build an analytics architecture that covers the entire data analytics lifecycle and is designed to help social service providers better identify their clients’ needs.

Public sector—social services needs assessments diagram, description below

This image shows how Oracle Data Platform for Public Sector can be used to improve Social Services Needs Assessments and enable proactive intervention.

  1. 1. Data Sources, Discovery
  2. 2. Ingest, Transform
  3. 3. Persist, Curate, Create
  4. 4. Analyze, Learn, Predict
  5. 5. Measure, Act

The data sources, discovery pillar includes four categories of data.

  1. 1. Applications comprises data from social service records, education records, client interviews and case notes, child welfare data.
  2. 2. Business Records comprises data from employment data, taxation data, public health data, community surveys, population analytics.
  3. 3. Technical Input comprises data from social data.
  4. 4. 3rd Party Data comprises census and demographic data, environmental data.

The connect, ingest, transform pillar comprises four capabilities.

  1. 1. Batch ingestion uses OCI Data Integration, Oracle Data Integrator, DB Tools.
  2. 2. Bulk transfer uses OCI Fast Connect, OCI Data Transfer, MFT, OCI CLI.
  3. 3. Change data capture uses OCI GoldenGate, Oracle Data Integrator.
  4. 4. Streaming ingest uses Kafka Connect.

All four capabilities connect unidirectionally into the serving data store and cloud storage within the Persist, Curate, Create pillar.

Additionally, streaming ingest is connected to streaming processing within the analyze, learn, predict pillar.

The persist, curate, create pillar comprises five capabilities.

  1. 1. The serving data store uses Oracle Autonomous Data Warehouse, Exadata Database Cloud Service, and Exadata Cloud@Customer.
  2. 2. Managed Hadoop uses Oracle Big Data Service
  3. 3. Cloud storage uses OCI Object Storage.
  4. 4. Batch processing uses OCI Data Flow.
  5. 5. Governance uses OCI Data Catalog.

These capabilities are connected within the pillar. Cloud storage is unidirectionally connected to both the serving data store and managed Hadoop; it is also bidirectionally connected to batch processing.

Managed Hadoop is unidirectionally connected to the serving data store.

Two capabilities connect into the Analyze, Learn, Predict pillar. The serving data store connects to both the analytics and visualization capability and also to the data products, APIs capability. Cloud storage connects to the machine learning capability.

The Analyze, learn, predict pillar comprises four capabilities.

  1. 1. Analytics and visualization uses Oracle Analytics Cloud, GraphStudio, and ISVs.
  2. 2. Data Products, API’s uses Autonomous Data Sharing, API Gateway, and Functions.
  3. 3. Machine learning uses Oracle Machine Learning, and Oracle ML Notebooks.
  4. 4. Streaming Processing uses OCI Goldengate Stream Analytics, and 3rd Party.

The Measure, act pillar captures how the data analysis may be used: by people and partners, social care analysts and intervention alerts.

Peoples and Partners comprises social profiling (demographics, socioeconomic indicators, and health-related data), risk assessments, social trends analysis, cohort analysis.

Social Care Analysts comprises Root cause analysis, pattern identification, natural language processing sentiment analysis, classification modeling, clustering, anomaly detection.

Intervention alerts is connected to stream processing.

The three central pillars—ingest, transform; persist, curate, create; and analyze, learn, predict—are supported by infrastructure, network, security, and IAM.

Connect, ingest, and transform data

Our solution is composed of three pillars, each supporting specific data platform capabilities. The first pillar provides the capability to connect, ingest, and transform data.

There are three primary ways to inject data into an architecture to help service delivery organizations identify and assess social needs.

  • To start our process, we’ll enable the frequent, real-time, or near real-time extracts that are typically required to identify specific events or intervention needs from operational systems such as case management, intervention management, and educational record systems. We’ll ingest data from case management, HCM/resource, and service systems using Oracle Cloud Infrastructure (OCI) GoldenGate. Event data, service availability, and eligibility requirements must be ingested in near real time (also referred to as “right-time” ingestion) to help social care providers identify and deliver the necessary services. This data is commonly relational in nature and sourced from enterprise applications. OCI GoldenGate uses change data capture to detect change events in the underlying structure of the systems that deliver the operational processes that need to be serviced (for example, the creation of a case, flagging of a client issue, intervention, and so on) and sends the data in real time to a persistence layer and/or the streaming layer. OCI GoldenGate provides a change data capture mechanism that can process source changes noninvasively by processing log files of completed actions/transactions and storing these captured changes in external trail files independent of the database. Changes are then reliably transferred to a staging database. The Journaling Knowledge Module (JKM) uses the metadata managed by Oracle Data Integrator to generate all OCI GoldenGate configuration files and processes all OCI GoldenGate–detected changes in the staging area. These changes will be loaded into the target data warehouse using Oracle Data Integrator’s declarative transformation mappings. This architecture enables separate real-time reporting on the normalized staging area tables in addition to loading and transforming the data into the analytical data warehouse tables.
  • Next, we’ll enable the bulk transfer of historical operational transaction data for model training and service delivery analytics. Bulk transfer services are used in situations where large volumes of data need to be moved to Oracle Cloud Infrastructure for the first time—for example, data from existing on-premises analytic repositories or other cloud sources. The specific bulk transfer service we’ll use will depend on the location of the data and the transfer frequency. For example, we may use OCI Data Transfer service or OCI Data Transfer Appliance to load a large volume of on-premises data from historical planning or data warehouse repositories. When large volumes of data must be moved on an ongoing basis, we recommend using OCI FastConnect, which provides a high-bandwidth, dedicated private network connection between a customer’s data center and OCI.
  • The ability to analyze community, cohort, or client data from multiple sources in real time is becoming increasingly important for identifying early intervention opportunities. In this use case, we use streaming ingest to ingest all the data read from client or internal events through mobile interactions, IoT, machine-to-machine communications, and other means. Streams can originate from a variety of internal (telematics and monitoring) and external (social) sources and can include location data, client interaction data, movement data, and social media data. Data (events) will be ingested, and some basic transformations/aggregations will occur before it’s stored in OCI Object Storage. Additional streaming analytics can be used to identify correlating location events and initiate actions such as client interventions, and any identified patterns can be fed back (manually) so the raw data can be examined using OCI Data Science.
  • While real-time needs are evolving, the most common extract from case, transactional, resource planning, client, demographic, and risk and compliance management systems is some kind of batch ingestion using an ETL process. Batch ingestion is used to import data from systems that can’t support data streaming (for example, most legacy case management systems and registers). These extracts can be ingested frequently, as often as every 10 or 15 minutes, but they are still batch in nature as groups of transactions are extracted and processed rather than individual transactions. OCI offers different services to handle batch ingestion, such as the native OCI Data Integration service and Oracle Data Integrator running on an OCI Compute instance. The choice of service would primarily be based on customer preference rather than technical requirements.

Persist, process, and curate data

Data persistence and processing is built on three components. Some customers will use all of them, others a subset. Depending on the volumes and data types, data could be loaded into object storage or loaded directly into a structured relational database for persistent storage. When we anticipate applying data science capabilities, data retrieved from data sources in its raw form (as an unprocessed native file or extract) is more typically captured and loaded from transactional systems into cloud storage.

  • Cloud storage is the most common data persistence layer for our data platform. It can be used for both structured and unstructured data. OCI Object Storage, OCI Data Flow, and Oracle Autonomous Data Warehouse (ADW) are the basic building blocks. Data retrieved from data sources in its raw format is captured and loaded into OCI Object Storage. OCI Object Storage is the primary data persistence tier, and Spark in OCI Data Flow is the primary batch processing engine. Batch processing involves several activities, including basic noise treatment, missing data management, and filtering based on defined outbound data sets. Results are written back to various layers of object storage or to a persistent relational repository based on the processing needed and the data types used.
  • We’ll now use a serving data store to persist our curated data in an optimized form for query performance and provide a comprehensive view of social service care demands and needs. The serving data store provides a persistent relational tier used to serve high-quality curated data directly to end users via SQL-based tools. In this solution, Oracle Autonomous Data Warehouse is instantiated as the serving data store for the enterprise data warehouse and, if required, more-specialized domain-level data marts. It can also be the data source for data science projects, or the repository required for Oracle Machine Learning. The serving data store may take one of several forms, including Oracle MySQL HeatWave, Oracle Database Exadata Cloud Service, or Oracle Exadata Cloud@Customer.

Analyze data, learn, and predict

The ability to analyze, learn, and predict is facilitated by three technology approaches.

  • Advanced analytics capabilities are critical for identifying current and future social service needs. In this use case, we rely on Oracle Analytics Cloud to deliver analytics and visualizations. This enables the organization to use descriptive analytics (describes current trends with histograms and charts), predictive analytics (predicts future events, identifies trends, and determines the probability of uncertain outcomes), and prescriptive analytics (proposes suitable actions to support optimal decision-making).

    Applying predictive models to historical data holds significant potential to improve social care needs assessments in the following ways:
    • Anticipating future demand: Predictive analytics algorithms can analyze historical data on social care utilization, demographic trends, and socioeconomic indicators to forecast future demand for various services. An understanding of the projected needs of communities can help social service agencies proactively plan and allocate resources to meet those demands efficiently.
    • Identifying at-risk populations: Service providers can use predictive analytics to identify individuals or communities at higher risk of needing social care based on a combination of factors, including age, income level, health conditions, and past service utilization patterns. By targeting interventions toward these at-risk populations, social service agencies can take action to prevent crises and provide early support to mitigate potential challenges.
    • Tailoring interventions: Social service organizations can optimize care delivery by using predictive analytics to identify the most appropriate and effective interventions based on an individual’s specific needs and characteristics. Tailoring services to the unique requirements of each individual or group can help social care providers maximize the impact of their interventions, support improved outcomes, and increase the cost-effectiveness of their programs.
    • Optimizing resource allocation: Social care workers can use analytics to inform resource allocation and strategic planning by identifying areas with the greatest need for services. By prioritizing investment in these high-need areas, social service agencies can help ensure resources are allocated efficiently and effectively to address the most-pressing challenges within communities.
    • Adapting to evolving care needs: Predictive analytics enables social service agencies to continuously monitor and refine their interventions based on real-time data and feedback. By analyzing outcomes and adjusting strategies in response to changing needs and circumstances, organizations can maintain the effectiveness and responsiveness of their social care programs as the needs of their communities change.
  • In addition to advanced and streaming analytics, data science, machine learning, and artificial intelligence are increasingly used to look for anomalies, predict where process latency might occur, and optimize the client experience and outcome. For example, machine learning models can be used for client context identification, population analysis, and outcome segmentation. By continuously learning from new data, these models can adapt and enhance their performance over time, which can support increased operational efficiency and better decision-making. OCI Data Science, OCI AI Services, and Oracle Machine Learning can be used in the databases.

    We use machine learning and data science methods to build and train our predictive models. These machine learning models can then be deployed for scoring via APIs or embedded as part of the OCI GoldenGate stream analytics pipeline. In some cases, these models can even be deployed in the database using the Oracle Machine Learning Services REST API (to do this, the model needs to be in Open Neural Network Exchange format). Additionally, OCI Data Science for Jupyter/Python-centric notebooks or Oracle Machine Learning for the Zeppelin notebook and machine learning algorithms can be deployed within the serving or transactional data store. Similarly, Oracle Machine Learning and OCI Data Science, either alone or in combination, can develop recommendation/decision models. These models can be deployed as a service, and we can deploy them behind OCI API Gateway to be delivered as “data products” and services. Finally, once built, the machine learning models can be deployed into applications that are part of an operational decisioning system (if permitted).
  • The final yet critical component is data governance. This will be delivered by OCI Data Catalog, a free service providing data governance and metadata management (for both technical and business metadata) for all the data sources in the data platform ecosystem. OCI Data Catalog is also a critical component for queries from Oracle Autonomous Data Warehouse to OCI Object Storage as it provides a way to quickly locate data regardless of its storage method. This allows end users, developers, and data scientists to use a common access language (SQL) across all the persisted data stores in the architecture.

Gain a comprehensive understanding of community needs and enable proactive, evidence-based decision-making

A data-driven approach that applies advanced analytics to social care needs can enhance the way social services are developed, provided, and customized to meet the requirements of both individuals and communities. Data-driven analysis makes it possible to understand the needs of various demographic groups in a community more thoroughly. Social service providers can obtain valuable insights into health indicators, socioeconomic determinants, demographic trends, and patterns of service utilization by using a diverse range of data sources, including health records, community surveys, social assistance program data, and census data. By adopting a holistic perspective, organizations are better equipped to pinpoint service delivery gaps, focus interventions on underprivileged groups, and more wisely distribute resources to meet the most-urgent needs.

Here are some of the ways a modern data platform can help organizations improve the quality, efficiency, and effectiveness of social care needs assessment and care delivery.

  • Consolidate diverse data sets from various sources, including census data, public health records, social assistance program data, and community surveys. Integrating these data sets into a centralized repository can make it easier to analyze all the information and identify correlations between different variables.
  • With advanced analytics capabilities, such as predictive modeling and data visualization tools, decision-makers can identify trends, patterns, and disparities in social care needs. For example, predictive modeling techniques can forecast future demand for specific social services based on population demographics and socioeconomic factors.
  • Machine learning algorithms can analyze large volumes of data to identify hidden patterns and relationships that might not be apparent through traditional analysis methods. These algorithms can detect clusters of individuals with similar social care needs or predict individuals at risk of certain challenges, enabling proactive intervention strategies.
  • Geospatial analysis tools make it possible to visualize social care needs on maps, enabling policymakers to identify geographic areas with higher concentrations of vulnerable populations or limited access to social services. This spatial understanding helps in resource allocation and service planning.
  • Real-time monitoring of social care indicators supports timely interventions and adjustments to service delivery strategies. Continuous feedback loops allow social service providers to evaluate intervention effectiveness and refine social care programs based on observed outcomes.
  • Take steps to help safeguard that data collection, storage, and analysis adhere to strict ethical guidelines and privacy regulations to safeguard individuals' sensitive information. Modern data platforms incorporate robust security measures and anonymization techniques that can help organizations protect privacy while leveraging data for social care needs assessments.

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