Datafication Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Type (Behavioral Datafication, Social Datafication, Geospatial Datafication, Transactional Datafication, Sensor Datafication), By Application (Blockchain, AIOps, Cognitive Computing, Edge Computing, FinOps, Others), By Vertical (BFSI, Healthcare, IT and Telecom, Retail, Government and Defe
Published Date: November - 2024 | Publisher: MIR | No of Pages: 320 | Industry: ICT | Format: Report available in PDF / Excel Format
View Details Buy Now 2890 Download Sample Ask for Discount Request CustomizationDatafication Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Type (Behavioral Datafication, Social Datafication, Geospatial Datafication, Transactional Datafication, Sensor Datafication), By Application (Blockchain, AIOps, Cognitive Computing, Edge Computing, FinOps, Others), By Vertical (BFSI, Healthcare, IT and Telecom, Retail, Government and Defe
Global Datafication Market is expected to grow at a healthy CAGR during the forecast period. The term "datafication" refers to the process of transforming various types of information into digital data, which can then be analysed and used to drive business decisions. The datafication market refers to the growing industry that specialize in collecting, analysing, and leveraging data for business purposes. Datafication has become increasingly prevalent due to advancements in technology, particularly in areas such as data collection, storage, and analytics. With the proliferation of digital devices, sensors, and online platforms, vast amounts of data are generated continuously. This data can come from sources such as social media interactions, online transactions, IoT devices, sensors, and other digital sources. Datafication has the potential to revolutionize industries, enable data-driven decision-making, and bring about transformative changes in various aspects of society.
The datafication market has seen tremendous growth in recent years, as more and more companies seek to leverage data to gain a competitive advantage. This market includes a wide range of players, from data analytics firms and software companies to data brokers and consulting firms.
Some of the key drivers of the global datafication market include the increasing availability of data, the growing importance of data-driven decision-making, and the rise of advanced analytics technologies such as artificial intelligence and machine learning. As these trends continue to shape the business landscape, it is likely that the datafication market will continue to grow and evolve, offering new opportunities and challenges for businesses of all sizes..
Increasing Availability of Data is Fueling the Market Growth
The availability of large amounts of data is one of the key drivers of the growth of datafication. With the rise of the internet and digital technologies, more and more data is being generated every day. This data can come from a variety of sources, such as social media, sensors, connected devices, and more.
Datafication refers to the process of turning this data into valuable insights and knowledge that can be used to drive business decisions and improve performance. By analyzing and interpreting this data, businesses can gain a deeper understanding of their customers, their operations, and their markets, and use this knowledge to make better decisions.
As more and more data become found at, businesses are increasingly turning to datafication to gain a competitive advantage. The market for datafication and data-driven decision making is growing rapidly, with companies investing heavily in technologies and tools that can help them extract insights from their data.
Overall, the increasing availability of data is driving the growth of datafication, and this trend is expected to continue in the years to come.
Download Free Sample ReportGrowing Importance of Data-Driven Decision-Making Leads to a Higher Demand for Datafication
The growing importance of data-driven decision-making has led to a higher demand for datafication. Datafication refers to the process of transforming various types of information into structured digital data that can be analyzed using computer algorithms. As businesses and organizations increasingly rely on data to make their decisions, the need for datafication has become essential.
Datafication allows businesses to collect and analyze data from various sources, including social media, customer feedback, and market trends, to gain insights and make more informed decisions. This can lead to better outcomes, increased efficiency, and cost savings. For example, datafication can help businesses identify areas of improvement in their operations, optimize their supply chain, and target their marketing efforts more effectively.
The demand for datafication has grown exponentially in recent years as data-driven decision-making has become a fundamental part of many industries, including finance, healthcare, retail, and manufacturing. With the advent of big data and advanced analytics tools, businesses now have access to vast amounts of data that can be used to drive insights and make informed decisions. As a result, the ability to collect, store, and analyze data has become a critical skill for organizations looking to remain competitive in today's fast-paced, data-driven world.
Increasing demand for high-speed and low-latency wireless connectivity for various applications
The rise of advanced analytics technologies such as artificial intelligence (AI) and machine learning (ML) has played a significant role in the growth of datafication. These technologies have made it possible to process and analyze large amounts of data more quickly and accurately than ever before. AI and ML algorithms are designed to learn from patterns and insights in data, and to make predictions and recommendations based on that learning. As a result, businesses and organizations are using these technologies to gain a deeper understanding of customer behavior, market trends, and other important factors that affect their operations. Datafication also enables AI and ML to work more effectively by providing them with large amounts of structured and unstructured data to learn from. By feeding these algorithms with more data, they become more accurate and effective at predicting outcomes and identifying patterns that humans may not be able to see. Overall, the rise of AI and ML has accelerated the trend towards datafication, as businesses seek to leverage these technologies to gain a competitive advantage and better understand their customers and operations.
Poor data quality can lead to incorrect insights and decisions
Poor data quality can have a significant impact on the accuracy and reliability of insights and decisions based on that data. If the data being analyzed is inaccurate or incomplete, it can lead to incorrect insights and decisions. For example, if data is missing key information or is outdated, it may not reflect the current state of the business or industry being analyzed. Data that is biased can also lead to incorrect insights and decisions. Bias can occur if the data being analyzed is not representative of the population or if it reflects the biases of those who collected the data. If the data being analyzed is inconsistent or contradictory, it can lead to incorrect insights and decisions. For example, if different data sources provide conflicting information, it may be difficult to determine which source is more accurate. Data errors such as duplicates, incorrect entries, or formatting inconsistencies can also affect the accuracy of insights and decisions based on that data. In the absence of proper data governance, data can become disorganized, difficult to access, or unreliable, leading to incorrect insights and decisions. Therefore, it is essential to ensure that data is accurate, complete, unbiased, consistent, and governed properly to avoid making incorrect insights and decisions based on that data. This can be achieved through proper data quality control measures, including data profiling, data cleansing, and data validation.
Recent Developments
- In 2021,Google launched a new suite of analytics and data visualization tools calledGoogle Analytics 4. This new version of the platform is designed to providemore detailed insights into user behavior across multiple channels and devices,including mobile apps and web browsers. It also incorporates machine learningalgorithms to help businesses identify trends and opportunities more quickly.
- IBM has beenleveraging its expertise in AI and data analytics to develop new solutions forindustries like healthcare, finance, and manufacturing. In 2021, the companylaunched a new platform called IBM Cloud Pak for Data, which integratesmultiple data sources and provides tools for data governance, analysis, andmachine learning.
- In 2020,Oracle launched a new platform called Oracle Analytics Cloud, which allowsusers to easily create and share data visualizations and insights. The platformalso incorporates machine learning and AI capabilities to help users discoverhidden patterns and trends in their data.
Market Segmentation
Based on Type, the market is segmented into Behavioral Datafication, Social Datafication, Geospatial Datafication, Transactional Datafication, and Sensor Datafication. Based on Application, the market is further segmented into Blockchain, AIOps, Cognitive Computing, Edge Computing, FinOps, and Others. Based on vertical, the market is further split into BFSI, Healthcare, IT and Telecom, Retail, Government and Defense, Manufacturing, and Media and Entertainment.
Company Profiles
IBM Corporation, Oracle Corporation, Microsoft Corporation, SAP SE, Google Inc., Amazon Web Services, SAS Institute Inc., Teradata Corporation, Dell EMC, Hewlett-Packard Enterprise (HPE) are among the major players operating in the global datafication market.
| Attribute | Details |
| Base Year | 2022 |
| Historical Years | 2018 – 2021 |
| Estimated Year | 2023 |
| Forecast Period | 2024 – 2028 |
| Quantitative Units | Revenue in USD Billion and CAGR for 2018-2022 and 2023E-2028F |
| Report Coverage | Revenue forecast, company share, competitive landscape, growth factors, and trends |
| Segments Covered | By Type By Application By Vertical By Region |
| Regional Scope | Asia-Pacific, North America, Europe, Middle East & Africa, and South America |
| Country Scope | China, Japan, India, Australia, South Korea, United States, Canada, Mexico, United Kingdom, Germany, France, Spain, Italy, Israel, Turkey, Saudi Arabia, UAE, South Africa, Brazil, Argentina, Colombia |
| Key Companies Profiled | IBM Corporation, Oracle Corporation, Microsoft Corporation, SAP SE, Google Inc., Amazon Web Services, SAS Institute Inc., Teradata Corporation, Dell EMC, Hewlett-Packard Enterprise (HPE) |
| Customization Scope | 10% free report customization with purchase. Addition or alteration to country, regional & segment scope. |
| Pricing and Purchase Options | Avail of customized purchase options to meet your exact research needs. Explore purchase options |
| Delivery Format | PDF and Excel through Email (We can also provide the editable version of the report in PPT/pdf format on special request) |
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