Big Data In Healthcare

Big Data In Healthcare Market Outlook

The global Big Data in Healthcare Market is projected to reach approximately USD 78 billion by 2035, growing at a compound annual growth rate (CAGR) of around 18% during the forecast period of 2025-2035. This significant growth can be attributed to the increasing prevalence of chronic diseases, the rising demand for personalized medicine, and the growing focus on patient-centered care. Additionally, the ongoing advancements in technology, including artificial intelligence and machine learning, are enabling healthcare providers to analyze large volumes of data effectively, leading to improved patient outcomes and operational efficiencies. Furthermore, the accelerating digitization of healthcare records and the adoption of electronic health records (EHRs) are contributing to the expansion of big data applications within the healthcare sector. With these factors in mind, it is clear that the market is set for substantial growth as healthcare systems globally seek to leverage data for better decision-making and enhanced patient care.

Growth Factor of the Market

The growth of the Big Data in Healthcare market is influenced by several critical factors that are transforming the way healthcare is delivered and managed. Firstly, the rising volume of healthcare data generated from various sources such as electronic health records, medical imaging, and wearable devices is driving the need for effective data management solutions. As healthcare providers strive for improved patient outcomes and operational efficiencies, the integration of big data analytics becomes essential. Secondly, the increasing adoption of telemedicine, which has seen a significant uptick post the COVID-19 pandemic, is creating a demand for robust data analytics capabilities that can process and analyze patient interactions remotely. Moreover, advancements in artificial intelligence and machine learning are further propelling the adoption of big data solutions, as these technologies enable predictive analytics and facilitate personalized treatment plans. Additionally, government initiatives aimed at enhancing healthcare infrastructure and promoting data interoperability are paving the way for significant investments in big data technologies. As a result, these growth factors are creating a conducive environment for the expansion of the Big Data in Healthcare market.

Key Highlights of the Market

• Significant expansion of big data analytics applications in predictive healthcare.
• Increasing investments in healthcare IT infrastructure to facilitate data management.
• Rising consumer demand for personalized healthcare services driving data analytics adoption.
• Growing regulatory support for electronic health records and data interoperability.
• Technological advancements in cloud computing and artificial intelligence enhancing data storage and analytics capabilities.

By Component

Hardware :

The hardware segment of the Big Data in Healthcare market includes the physical devices and infrastructure required to store, process, and analyze large volumes of healthcare data. This segment encompasses servers, storage devices, and networking equipment specifically designed to handle the substantial data loads typical in healthcare settings. As hospitals and clinics increasingly adopt electronic health records and other digital solutions, the demand for high-performance hardware is increasing. Moreover, the shift towards cloud computing is prompting healthcare organizations to invest in scalable hardware solutions that can support data storage and processing needs efficiently. In addition, advancements in technologies such as edge computing are prompting healthcare facilities to enhance their hardware capabilities to ensure real-time data processing and analytics, further driving the growth of this segment.

Software :

Software is a pivotal component of the Big Data in Healthcare market, enabling healthcare organizations to analyze and manage vast amounts of data efficiently. This segment includes various software solutions such as data analytics platforms, data integration tools, and machine learning algorithms tailored to healthcare needs. The growing complexity of healthcare data, which includes structured, unstructured, and semi-structured data types, requires robust software solutions capable of providing actionable insights. Furthermore, the increasing focus on predictive analytics and population health management is driving demand for software that can support advanced analytics capabilities. As providers seek to enhance patient outcomes and operational efficiencies, the software segment is expected to witness significant growth, particularly in applications focusing on clinical decision support and patient engagement.

Services :

The services segment of the Big Data in Healthcare market encompasses a range of offerings aimed at supporting healthcare organizations in implementing and managing their big data initiatives. This segment includes consulting services, implementation services, and data analytics services, among others. As healthcare entities increasingly recognize the value of data-driven decision-making, the demand for professional services that can guide them through the complexities of big data initiatives is on the rise. Moreover, as more organizations adopt cloud-based solutions, there is a growing need for managed services that can ensure data security and compliance with regulatory standards. The services segment is critical in facilitating the successful adoption of big data technologies, and its growth is driven by the need for expertise in healthcare analytics and data management.

By Data Type

Structured Data :

Structured data refers to highly organized information that is easily searchable and typically resides in fixed fields within a record or file, such as databases. In healthcare, structured data is critical for operational efficiency and decision-making processes. Patient records, billing information, and lab test results are often stored in structured formats, allowing for easier data retrieval and analysis. The increasing use of electronic health records (EHRs) and standardized coding systems such as ICD-10 has further propelled the utilization of structured data in healthcare analytics. As healthcare organizations look to enhance their operational metrics and patient safety initiatives, the emphasis on leveraging structured data for analytics is expected to grow, driving the overall market forward.

Unstructured Data :

Unstructured data represents information that does not adhere to a predefined data model, making it more complex to analyze. In the healthcare sector, unstructured data includes various forms of information such as clinical notes, radiology images, and social media interactions. The ability to analyze unstructured data is becoming increasingly important, as it can provide valuable insights into patient behavior, treatment efficacy, and overall healthcare trends. Advanced analytics techniques and natural language processing (NLP) are being employed to extract meaningful information from unstructured data, enabling healthcare providers to make more informed decisions. As the volume of unstructured data continues to grow, the demand for tools and services that can facilitate its analysis is expected to significantly influence the market.

Semi-structured Data :

Semi-structured data is a form of data that does not conform to a rigid structure but still contains tags or markers to separate data elements, making it more organized than unstructured data. In healthcare, semi-structured data can include XML files, JSON data, and certain types of electronic health records. The significance of semi-structured data lies in its ability to bridge the gap between structured and unstructured data, offering flexibility in analysis and storage. As healthcare organizations strive for a more comprehensive understanding of patient care and outcomes, the ability to analyze semi-structured data is becoming increasingly vital. The growth of big data technologies that can effectively manage semi-structured data will continue to shape the landscape of healthcare analytics.

By Application

Clinical Data Analytics :

Clinical data analytics focuses on analyzing patient health records and clinical outcomes to improve patient care and operational efficiencies. This application utilizes big data tools to identify patterns and trends in patient care, providing healthcare providers with insights that can lead to improved clinical outcomes. By leveraging clinical data analytics, organizations can optimize treatment plans, reduce readmission rates, and enhance overall patient satisfaction. The increasing emphasis on evidence-based practices and the growing availability of data from multiple sources, such as wearables and telehealth services, are propelling the demand for clinical data analytics solutions. As healthcare providers look to enhance quality and efficiency in patient care, clinical data analytics will continue to be a driving force in the big data in healthcare market.

Financial Analytics :

Financial analytics in healthcare focuses on analyzing financial data to optimize revenue cycle management, reduce costs, and enhance profitability. This application of big data enables healthcare organizations to track expenses, anticipate revenue trends, and manage financial risks effectively. With the increasing pressure on healthcare providers to improve financial performance amid changing reimbursement models, the demand for financial analytics is on the rise. By utilizing data analytics tools, organizations can identify inefficiencies in their financial processes, optimize billing practices, and enhance cash flow management. The growing complexity of healthcare financing and reimbursement structures makes financial analytics essential for sustainable growth in the industry.

Operational Analytics :

Operational analytics involves analyzing the internal processes of healthcare organizations to improve efficiency and effectiveness. This application of big data focuses on optimizing resource allocation, streamlining workflows, and enhancing clinical operations. By leveraging operational analytics, healthcare providers can identify bottlenecks in service delivery, enhance staff productivity, and improve patient flow. As the healthcare sector increasingly faces challenges related to cost containment and resource optimization, the need for operational analytics solutions is expected to grow. Organizations deploying operational analytics can achieve significant improvements in their operational metrics, leading to enhanced service delivery and patient satisfaction.

Population Health Management :

Population health management involves analyzing health data from various sources to improve health outcomes for specific populations. This application of big data enables healthcare organizations to identify at-risk patient groups, track health trends, and implement targeted intervention strategies. By utilizing population health management tools, providers can enhance care coordination, reduce healthcare disparities, and improve overall public health outcomes. The increasing focus on value-based care and preventive health measures is driving the demand for population health management solutions. As healthcare stakeholders seek to improve health outcomes while controlling costs, population health management will play a crucial role in the ongoing transformation of the healthcare landscape.

By Deployment

On-premise :

The on-premise deployment model refers to the installation and management of big data solutions within the organization’s own IT infrastructure. This deployment method is favored by healthcare organizations that prioritize data security and compliance with regulations such as HIPAA. On-premise solutions allow organizations to maintain full control over their data and analytics processes, ensuring that sensitive patient information remains protected. However, this approach often requires significant capital investment in hardware and IT resources, which can be a barrier for smaller organizations. Despite this, many established healthcare providers continue to adopt on-premise solutions to leverage the benefits of big data analytics while adhering to strict regulatory requirements.

Cloud-based :

Cloud-based deployment of big data solutions offers healthcare organizations a flexible and scalable alternative to on-premise systems. This model allows providers to access advanced analytics capabilities without the need for significant upfront investments in hardware and infrastructure. Cloud-based solutions facilitate data storage, management, and analysis, enabling healthcare organizations to focus on utilizing data for improved patient care rather than managing IT resources. Moreover, the cloud enables real-time collaboration among different stakeholders, enhancing communication and information sharing across the healthcare continuum. As organizations increasingly recognize the benefits of cloud technology, the demand for cloud-based big data solutions is expected to rise significantly in the coming years, facilitating more agile and responsive healthcare operations.

By End User

Hospitals and Clinics :

Hospitals and clinics represent one of the largest end-user segments in the Big Data in Healthcare market. These organizations utilize big data analytics to enhance patient care, streamline operations, and improve financial performance. By analyzing vast amounts of patient data, hospitals can identify trends and patterns that inform clinical decision-making and operational efficiencies. The growing need for data-driven insights in patient management and resource allocation is driving the adoption of big data solutions in this segment. Moreover, the increasing prevalence of chronic diseases necessitates the use of big data analytics to monitor patient conditions and tailor treatment plans effectively. As a result, hospitals and clinics are investing heavily in big data technologies to maintain competitive advantages and improve patient outcomes.

Research Organizations :

Research organizations are key end users of big data in healthcare, leveraging analytics to support clinical trials, epidemiological studies, and public health research. The ability to analyze large datasets enables researchers to uncover valuable insights into disease trends, treatment efficacy, and patient demographics. As healthcare research becomes increasingly data-driven, the demand for big data analytics solutions in research organizations is growing. Additionally, the collaboration between research institutions and healthcare providers is facilitating the exchange of data and insights, further enhancing research capabilities. With funding for healthcare research on the rise, research organizations are poised to play a significant role in the advancement of big data analytics in the healthcare sector.

Healthcare Payers :

Healthcare payers, including insurance companies and managed care organizations, are increasingly utilizing big data analytics to optimize claims processing, fraud detection, and risk management. By analyzing large volumes of claims data, payers can identify trends and patterns that inform their underwriting and reimbursement decisions. The growing focus on value-based care and cost-containment strategies is driving the demand for analytics solutions that can enhance operational efficiencies. Additionally, by leveraging big data insights, healthcare payers can better understand patient populations, develop targeted health plans, and improve member engagement. As the healthcare landscape continues to evolve, the role of big data analytics in the payer segment is expected to expand significantly.

By Region

The North American region dominates the Big Data in Healthcare market, accounting for a significant share of the global market due to advanced healthcare infrastructure, high adoption rates of technologies, and substantial investments in research and development. The North American market is projected to grow at a CAGR of approximately 17% during the forecast period. The presence of major healthcare IT companies, coupled with a supportive regulatory environment for electronic health records, has further propelled the growth of big data analytics in the region. Additionally, the increasing focus on personalized medicine and data-driven healthcare solutions is expected to boost demand for big data applications among healthcare providers and payers alike.

In Europe, the Big Data in Healthcare market is also witnessing steady growth, driven by an increasing emphasis on improving healthcare quality and patient outcomes. The European market is characterized by the growing adoption of electronic health records and a progressive shift toward digital health solutions. With significant government initiatives aimed at enhancing healthcare infrastructure and promoting data interoperability, the demand for big data analytics is set to rise. The region is expected to capture a notable share of the market, supported by various initiatives aimed at improving research capabilities and clinical outcomes. The Asia Pacific region is anticipated to experience rapid growth in this market due to increasing healthcare expenditures, a rising patient population, and a growing focus on healthcare digitization.

Opportunities

The Big Data in Healthcare market presents numerous opportunities driven by the advancements in technology and the increasing focus on data-driven decision-making. One significant opportunity lies in the integration of artificial intelligence and machine learning algorithms with big data analytics. These technologies can enhance the ability to analyze complex healthcare datasets and extract actionable insights, thereby accelerating clinical decision-making and improving patient care outcomes. As healthcare organizations look to optimize their operations and enhance patient engagement, the demand for AI-driven analytics solutions is expected to soar. Additionally, the growing trend of telehealth and remote patient monitoring opens new avenues for big data applications. By analyzing data collected from wearables and telehealth platforms, providers can gain insights into patient behavior and health status, leading to better management of chronic diseases and more personalized care approaches.

Another promising opportunity in the Big Data in Healthcare market is the increasing emphasis on population health management. With healthcare systems shifting towards value-based care, there is a growing need for data analytics solutions that can support population health initiatives. Organizations can leverage big data tools to identify at-risk populations, track health trends, and implement targeted intervention strategies. By focusing on prevention and early intervention, healthcare providers can reduce costs and improve health outcomes. Furthermore, government initiatives aimed at promoting data interoperability and encouraging healthcare organizations to adopt advanced analytics solutions will create a positive environment for growth in the market. As these opportunities continue to evolve, stakeholders in the healthcare sector must remain agile and innovative to capitalize on the potential of big data analytics.

Threats

While the Big Data in Healthcare market offers numerous growth opportunities, it also faces several threats that could impact its expansion. One of the main threats is the increasing concern around data security and patient privacy. With the rise of data breaches and cyberattacks in the healthcare sector, organizations must invest heavily in securing their data and ensuring compliance with stringent regulations such as HIPAA. The potential for significant financial and reputational damage resulting from data breaches can deter healthcare providers from fully embracing big data solutions. Additionally, the complexities of data integration from various sources pose another significant challenge. The healthcare landscape is characterized by diverse data formats and systems, making it difficult to achieve seamless data interoperability. If these challenges are not addressed, they could hinder the effectiveness of big data analytics initiatives in the healthcare sector.

Another threat to the Big Data in Healthcare market is the potential for regulatory changes that could impact the use of data analytics in clinical practices. Healthcare regulations are constantly evolving, and any changes that restrict the use of patient data for analytics could significantly limit market growth. Furthermore, the high costs associated with implementing and maintaining big data solutions can be a restraining factor, particularly for smaller healthcare organizations that may lack the resources to adopt advanced technologies. As a result, it is vital for stakeholders in the healthcare industry to remain informed about regulatory developments and to seek ways to mitigate the risks associated with data security and integration challenges.

Competitor Outlook

• IBM Corporation
• Oracle Corporation
• McKesson Corporation
• Epic Systems Corporation
• Cerner Corporation
• Siemens Healthineers
• Allscripts Healthcare Solutions
• Philips Healthcare
• Microsoft Corporation
• Verisk Analytics
• SAP SE
• GE Healthcare
• DXC Technology
• Tableau Software
• Inovalon Holdings

The competitive landscape of the Big Data in Healthcare market is characterized by a diverse range of companies that offer various solutions and services to address the evolving needs of healthcare providers. Major players in this market are focusing on strategic partnerships, mergers, and acquisitions to enhance their product offerings and expand their market presence. Companies such as IBM and Oracle are investing heavily in research and development to innovate their big data analytics solutions, while also targeting specific healthcare domains such as clinical analytics and operational efficiencies. The increasing competition in this sector is also leading to the introduction of advanced analytics capabilities, including predictive and prescriptive analytics, which are becoming essential for healthcare organizations seeking to improve patient outcomes and streamline operations.

Among the leading companies in the Big Data in Healthcare market, Epic Systems Corporation and Cerner Corporation stand out due to their comprehensive electronic health record systems and advanced analytics capabilities. Both companies are known for their commitment to improving patient care through data-driven insights and have developed robust platforms that enable healthcare providers to effectively manage and analyze patient data. Additionally, McKesson Corporation and Siemens Healthineers are also making significant strides in the market by offering integrated solutions that combine data analytics with operational management tools. These companies are continuously evolving their offerings to provide more value to healthcare organizations and to better support their data analytics initiatives.

Furthermore, emerging players such as Tableau Software and Inovalon Holdings are gaining traction in the market by offering user-friendly analytical tools specifically tailored for healthcare data. These companies are enabling healthcare organizations to harness the power of big data without requiring extensive technical expertise, thus democratizing access to advanced analytics solutions. As the market continues to grow, it will be essential for existing players to adapt their strategies to remain competitive, while new entrants must find innovative ways to differentiate themselves in a crowded marketplace. Overall, the competitive landscape of the Big Data in Healthcare market will continue to evolve as organizations strive to leverage data for improved healthcare delivery and operational excellence.

• 1 Appendix

  • 1.1 List of Tables
  • 1.2 List of Figures

• 2 Introduction

  • 2.1 Market Definition
  • 2.2 Scope of the Report
  • 2.3 Study Assumptions
  • 2.4 Base Currency & Forecast Periods

• 3 Market Dynamics

  • 3.1 Market Growth Factors
  • 3.2 Economic & Global Events
  • 3.3 Innovation Trends
  • 3.4 Supply Chain Analysis

• 4 Consumer Behavior

  • 4.1 Market Trends
  • 4.2 Pricing Analysis
  • 4.3 Buyer Insights

• 5 Key Player Profiles

  • 5.1 SAP SE
    • 5.1.1 Business Overview
    • 5.1.2 Products & Services
    • 5.1.3 Financials
    • 5.1.4 Recent Developments
    • 5.1.5 SWOT Analysis
  • 5.2 GE Healthcare
    • 5.2.1 Business Overview
    • 5.2.2 Products & Services
    • 5.2.3 Financials
    • 5.2.4 Recent Developments
    • 5.2.5 SWOT Analysis
  • 5.3 DXC Technology
    • 5.3.1 Business Overview
    • 5.3.2 Products & Services
    • 5.3.3 Financials
    • 5.3.4 Recent Developments
    • 5.3.5 SWOT Analysis
  • 5.4 IBM Corporation
    • 5.4.1 Business Overview
    • 5.4.2 Products & Services
    • 5.4.3 Financials
    • 5.4.4 Recent Developments
    • 5.4.5 SWOT Analysis
  • 5.5 Tableau Software
    • 5.5.1 Business Overview
    • 5.5.2 Products & Services
    • 5.5.3 Financials
    • 5.5.4 Recent Developments
    • 5.5.5 SWOT Analysis
  • 5.6 Verisk Analytics
    • 5.6.1 Business Overview
    • 5.6.2 Products & Services
    • 5.6.3 Financials
    • 5.6.4 Recent Developments
    • 5.6.5 SWOT Analysis
  • 5.7 Inovalon Holdings
    • 5.7.1 Business Overview
    • 5.7.2 Products & Services
    • 5.7.3 Financials
    • 5.7.4 Recent Developments
    • 5.7.5 SWOT Analysis
  • 5.8 Cerner Corporation
    • 5.8.1 Business Overview
    • 5.8.2 Products & Services
    • 5.8.3 Financials
    • 5.8.4 Recent Developments
    • 5.8.5 SWOT Analysis
  • 5.9 Oracle Corporation
    • 5.9.1 Business Overview
    • 5.9.2 Products & Services
    • 5.9.3 Financials
    • 5.9.4 Recent Developments
    • 5.9.5 SWOT Analysis
  • 5.10 Philips Healthcare
    • 5.10.1 Business Overview
    • 5.10.2 Products & Services
    • 5.10.3 Financials
    • 5.10.4 Recent Developments
    • 5.10.5 SWOT Analysis
  • 5.11 McKesson Corporation
    • 5.11.1 Business Overview
    • 5.11.2 Products & Services
    • 5.11.3 Financials
    • 5.11.4 Recent Developments
    • 5.11.5 SWOT Analysis
  • 5.12 Siemens Healthineers
    • 5.12.1 Business Overview
    • 5.12.2 Products & Services
    • 5.12.3 Financials
    • 5.12.4 Recent Developments
    • 5.12.5 SWOT Analysis
  • 5.13 Microsoft Corporation
    • 5.13.1 Business Overview
    • 5.13.2 Products & Services
    • 5.13.3 Financials
    • 5.13.4 Recent Developments
    • 5.13.5 SWOT Analysis
  • 5.14 Epic Systems Corporation
    • 5.14.1 Business Overview
    • 5.14.2 Products & Services
    • 5.14.3 Financials
    • 5.14.4 Recent Developments
    • 5.14.5 SWOT Analysis
  • 5.15 Allscripts Healthcare Solutions
    • 5.15.1 Business Overview
    • 5.15.2 Products & Services
    • 5.15.3 Financials
    • 5.15.4 Recent Developments
    • 5.15.5 SWOT Analysis

• 6 Market Segmentation

  • 6.1 Big Data In Healthcare Market, By End User
    • 6.1.1 Hospitals and Clinics
    • 6.1.2 Research Organizations
    • 6.1.3 Healthcare Payers
    • 6.1.4 Others
  • 6.2 Big Data In Healthcare Market, By Component
    • 6.2.1 Hardware
    • 6.2.2 Software
    • 6.2.3 Services
  • 6.3 Big Data In Healthcare Market, By Data Type
    • 6.3.1 Structured Data
    • 6.3.2 Unstructured Data
    • 6.3.3 Semi-structured Data
  • 6.4 Big Data In Healthcare Market, By Deployment
    • 6.4.1 On-premise
    • 6.4.2 Cloud-based
  • 6.5 Big Data In Healthcare Market, By Application
    • 6.5.1 Clinical Data Analytics
    • 6.5.2 Financial Analytics
    • 6.5.3 Operational Analytics
    • 6.5.4 Population Health Management
    • 6.5.5 Others

• 7 Competitive Analysis

  • 7.1 Key Player Comparison
  • 7.2 Market Share Analysis
  • 7.3 Investment Trends
  • 7.4 SWOT Analysis

• 8 Research Methodology

  • 8.1 Analysis Design
  • 8.2 Research Phases
  • 8.3 Study Timeline

• 9 Future Market Outlook

  • 9.1 Growth Forecast
  • 9.2 Market Evolution

• 10 Geographical Overview

  • 10.1 Europe - Market Analysis
    • 10.1.1 By Country
      • 10.1.1.1 UK
      • 10.1.1.2 France
      • 10.1.1.3 Germany
      • 10.1.1.4 Spain
      • 10.1.1.5 Italy
  • 10.2 Asia Pacific - Market Analysis
    • 10.2.1 By Country
      • 10.2.1.1 India
      • 10.2.1.2 China
      • 10.2.1.3 Japan
      • 10.2.1.4 South Korea
  • 10.3 Latin America - Market Analysis
    • 10.3.1 By Country
      • 10.3.1.1 Brazil
      • 10.3.1.2 Argentina
      • 10.3.1.3 Mexico
  • 10.4 North America - Market Analysis
    • 10.4.1 By Country
      • 10.4.1.1 USA
      • 10.4.1.2 Canada
  • 10.5 Middle East & Africa - Market Analysis
    • 10.5.1 By Country
      • 10.5.1.1 Middle East
      • 10.5.1.2 Africa
  • 10.6 Big Data In Healthcare Market by Region

• 11 Global Economic Factors

  • 11.1 Inflation Impact
  • 11.2 Trade Policies

• 12 Technology & Innovation

  • 12.1 Emerging Technologies
  • 12.2 AI & Digital Trends
  • 12.3 Patent Research

• 13 Investment & Market Growth

  • 13.1 Funding Trends
  • 13.2 Future Market Projections

• 14 Market Overview & Key Insights

  • 14.1 Executive Summary
  • 14.2 Key Trends
  • 14.3 Market Challenges
  • 14.4 Regulatory Landscape

Segments Analyzed in the Report

The global Big Data In Healthcare market is categorized based on

By Component

• Hardware
• Software
• Services

By Data Type

• Structured Data
• Unstructured Data
• Semi-structured Data

By Application

• Clinical Data Analytics
• Financial Analytics
• Operational Analytics
• Population Health Management
• Others

By Deployment

• On-premise
• Cloud-based

By End User

• Hospitals and Clinics
• Research Organizations
• Healthcare Payers
• Others

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa

Key Players

• IBM Corporation
• Oracle Corporation
• McKesson Corporation
• Epic Systems Corporation
• Cerner Corporation
• Siemens Healthineers
• Allscripts Healthcare Solutions
• Philips Healthcare
• Microsoft Corporation
• Verisk Analytics
• SAP SE
• GE Healthcare
• DXC Technology
• Tableau Software
• Inovalon Holdings