Big Data Platforms

The World Is Big Data-Driven, — Market Stats Show

According to the 2024 Data and AI Leadership Executive Survey, 73.8% of organizations have identified data and AI ethics as a top corporate priority. This number aligns closely with the 87.9% of organizations prioritizing investment in data and analytics. Also, over the last five years, there was a notable rise in organizations that:

• Use data and analytics as a competitive edge (with numbers growing from 40.8% to 50%).
• Manage data as a business asset (from 39.5% to 49.1%).
• Successfully cultivated a data-driven culture (from 23.9% to 48.1%).

Big Data Platform: The Essence

A big data platform is a tailor-made integrated software solution that helps your organization collect, process, and capitalize on petabytes of high-velocity and high-variety data. Among the famous examples of big data platforms are Netflix and Uber.

An Example of a Big Data Platform Architecture in Healthcare

Below, ScienceSoft's software engineering experts outline the key building blocks of a big data platform for the healthcare industry.

Data producers continuously feed raw data into the data acquisition layer. This layer's diversity in terms of data types (structured, semi-structured, unstructured), data velocity (batch vs. real-time), and data sources makes big data solutions complex and powerful. Efficient handling, filtering, and preprocessing at this layer are essential to ensure that the downstream components of the big data platform can effectively store and process the collected data.

The data acquisition layer bridges raw data producers and the data platform. It collects data from various sources, handles event sequencing, timestamping, and routing. Depending on the data source, this integration might require specific connectors or APIs. For off-the-shelf software, they’ll be often provided by vendors. Custom software or legacy software products can require custom integration solutions.

The data platform acts as a central repository that supports all other components with necessary data. This layer:

• Stores a vast pool of raw data in its native format (including binary, text, images, etc.) that can be processed either immediately or at a later time.
• Processes — i.e., cleans, enriches, transforms, verifies, and standardizes — raw data into a format suitable for analysis.
• Stores processed data in a structured format optimized for complex queries and analytics.

Custom data quality and ETL tools can be used for more sophisticated data processing operations, e.g., data verification and handling conflicting information from different data sources.

The analytical zone is responsible for generating insights, predictions, and recommendations. To derive value from data, this component can employ various techniques ranging from statistical analysis to custom machine learning algorithms and data mining.

The core services layer translates analytical insights into practical, real-time actions that enhance patient care and operational efficiency.

• The decision center is the brain of the operation. It integrates insights from analytics, assesses resource availability (beds, staff, equipment) through the data platform, and makes critical decisions on what actions are necessary (e.g., regarding bed allocation, ambulance routing, staff allocation, equipment distribution, and more). This can be automated decision-making systems, which apply business rules or machine learning models to make choices without human intervention, or decision support systems that help human users make informed decisions.
  Given its central role, the decision center may require substantial custom development to tailor functionality to specific organizational needs and processes.
• The workflow engine creates, assigns, orchestrates, and tracks tasks across different teams or departments based on predefined rules and analytics insights. For example, if the decision center determines that certain patients require immediate attention based on analytics insights, the workflow engine can prioritize these patients in the care queue and assign them to the appropriate healthcare professionals.
  Building a custom workflow engine is an option when specific processing needs or integration requirements cannot be met by existing tools (like Apache Airflow). This could be due to unique business logic, performance considerations, or proprietary technologies and legacy systems involved.
• The routing engine is used to implement decisions that require the physical movement of items or patients. It relies on algorithms to determine the most efficient routes and schedules based on real-time data such as traffic conditions, location of ambulances, and availability of beds.
  Existing routing and logistics optimization software can be adapted to healthcare-specific needs, such as ambulance dispatching. However, the unique constraints of healthcare logistics (e.g., emergency prioritization, hospital capacity) might necessitate custom algorithm development.
• The communication center disseminates information from the decision center, workflow and routing engines to healthcare staff, patients, and external partners. It can use various channels such as SMS, email, web and mobile apps, and dashboards. The telehealth service can also be a part of the communication center.
  Existing communication platforms (e.g., Twilio for SMS, SendGrid for email) can be integrated to handle notifications and communications. The direct integration with patient apps, employee portals, and external systems will often require custom-built connections.

Inspiring Examples of Big Data-Driven Companies

How Much Will Your Big Data Platform Cost?

Our team will be happy to provide a cost estimate for your case, as well as handle the questions like:

• What will be the ROI of your initiative?
• What is the payback period?
• Which sourcing model is the most feasible for your needs?
• How to optimize project costs?