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The data wrangling market size is forecast to increase by USD 1.4 billion at a CAGR of 14.8% between 2023 and 2028. The market is experiencing significant growth due to the numerous benefits provided by data wrangling solutions, including data cleaning, transformation, and enrichment. One major trend driving market growth is the rising need for technology such as the competitive intelligence and artificial intelligence in the healthcare sector, where data wrangling is essential for managing and analyzing patient data to improve patient outcomes and reduce costs. However, a challenge facing the market is the lack of awareness of data wrangling tools among small and medium-sized enterprises (SMEs), which limits their ability to effectively manage and utilize their data. Despite this, the market is expected to continue growing as more organizations recognize the value of data wrangling in driving business insights and decision-making.
The market is experiencing significant growth due to the increasing demand for data management and analysis in various industries. The market is experiencing significant growth due to the increasing volume, variety, and velocity of data being generated from various sources such as IoT devices, financial services, and smart cities. Artificial intelligence and machine learning technologies are being increasingly used for data preparation, data cleaning, and data unification. Data wrangling, also known as data munging, is the process of cleaning, transforming, and enriching raw data to make it usable for analysis. This process is crucial for businesses aiming to gain valuable insights from their data and make informed decisions. Data analytics is a primary driver for the market, as organizations seek to extract meaningful insights from their data. Cloud solutions are increasingly popular for data wrangling due to their flexibility, scalability, and cost-effectiveness.
Furthermore, both on-premises and cloud-based solutions are being adopted by businesses to meet their specific data management requirements. Multi-cloud strategies are also gaining traction in the market, as organizations seek to leverage the benefits of multiple cloud providers. This approach allows businesses to distribute their data across multiple clouds, ensuring business continuity and disaster recovery capabilities. Data quality is another critical factor driving the market. Ensuring data accuracy, completeness, and consistency is essential for businesses to make reliable decisions. The market is expected to grow further as organizations continue to invest in big data initiatives and implement advanced technologies such as AI and ML to gain a competitive edge. Data cleaning and data unification are key processes in data wrangling that help improve data quality. The finance and insurance industries are major contributors to the market, as they generate vast amounts of data daily.
In addition, real-time analysis is becoming increasingly important in these industries, as businesses seek to gain insights from their data in near real-time to make informed decisions. The Internet of Things (IoT) is also driving the market, as businesses seek to collect and analyze data from IoT devices to gain insights into their operations and customer behavior. Edge computing is becoming increasingly popular for processing IoT data, as it allows for faster analysis and decision-making. Self-service data preparation is another trend in the market, as businesses seek to empower their business users to prepare their data for analysis without relying on IT departments.
Moreover, this approach allows businesses to be more agile and responsive to changing business requirements. Big data is another significant trend in the market, as businesses seek to manage and analyze large volumes of data to gain insights into their operations and customer behavior. Data wrangling is a critical process in managing big data, as it ensures that the data is clean, transformed, and enriched to make it usable for analysis. In conclusion, the market in North America is experiencing significant growth due to the increasing demand for data management and analysis in various industries. Cloud solutions, multi-cloud strategies, data quality, finance and insurance, IoT, real-time analysis, self-service data preparation, and big data are some of the key trends driving the market. Businesses that invest in data wrangling solutions can gain a competitive edge by gaining valuable insights from their data and making informed decisions.
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.
The BFSI segment is estimated to witness significant growth during the forecast period. In today's digital age, the banking, financial services, and insurance (BFSI) sector is witnessing a significant shift towards data-driven decision-making. Data wrangling, a crucial process in handling and preparing data for analysis, is increasingly being adopted by both large enterprises and Small and Medium-sized Enterprises (SMEs) in the BFSI sector. With the rise of hybrid cloud solutions, data can be accessed and managed from anywhere, making data wrangling an essential tool for optimizing operational processes and enhancing customer interactions. Data security and governance are paramount in the BFSI sector, and data wrangling solutions provide the necessary framework for managing and structuring large volumes of data generated through digital channels.
Moreover, big data analytics has become a game-changer for BFSI institutions, enabling them to gain valuable insights into consumer behavior and market trends. Data integration is another key benefit, allowing for a unified view of customer data across various touchpoints. The adoption of data wrangling in the BFSI sector is driven by the need to provide superior customer service through digital channels. As consumers increasingly prefer self-service options, BFSI institutions must be able to process and analyze data in real-time to offer personalized and efficient services. Data wrangling solutions enable these institutions to prepare data for analysis quickly and accurately, reducing the time spent on data preparation and allowing for more informed decision-making.
In conclusion, data wrangling plays a vital role in the digital transformation of the BFSI sector. By enabling efficient data processing, analysis, and integration, data wrangling solutions help BFSI institutions gain a competitive edge and better serve their customers. The future of the market in the BFSI sector looks promising, with continued growth expected due to the increasing adoption of digital channels and the generation of large volumes of data.
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The BFSI segment accounted for USD 300.10 million in 2018 and showed a gradual increase during the forecast period.
North America is estimated to contribute 43% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.
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In the market, North America held the largest market share in 2023. This dominance can be attributed to the widespread adoption of advanced technologies in various industries, including healthcare, telecommunications, financial services, and insurance, in the region. The use of data munging tools and services is expected to increase significantly in North America due to the growing application of big data and the emergence of Industry 4.0 services. The US, in particular, is at the forefront of the big data revolution, with companies from diverse sectors, such as healthcare and financial services, leveraging data wrangling to gain valuable insights from vast amounts of data sourced from multiple channels. The importance of data quality, information mapping, and edge computing in enterprise mobility further underscores the significance of data wrangling in the US market.
Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.
Numerous benefits provided by data wrangling solutions is the key driver of the market. Big data analytics software is gaining significant traction in the business world due to its capacity to facilitate swift and informed decision-making, thereby offering a competitive edge. The market for this technology is thriving, driven by the advantages it provides, such as easy access to crucial business metrics and valuable insights into customer behavior, which in turn contribute to revenue growth and operational efficiency. Additionally, data wrangling solutions are being adopted by companies for real-time forecasting and monitoring of potential business disruptions. These solutions enable organizations to mitigate risks and make complex decisions about unforeseen events, such as cyberattacks or crises, more effectively and expeditiously.
Furthermore, cloud-based analytics, data lineage, metadata management, data cataloging, data cleaning, and data unification are essential components of data wrangling solutions that enhance their value. These technologies offer benefits such as easy access to critical business metrics, customer behavior insights, real-time forecasting, and risk mitigation. Cloud-based analytics, metadata management, data cataloging, data cleaning, and data unification are key features of data wrangling solutions that contribute to their value.
Rising need of technology in healthcare sector is the upcoming trend in the market. In the healthcare industry, managing and analyzing vast amounts of data have become essential for addressing challenges such as disease outbreaks and operational efficiency. This data, which includes patient details, medical histories, treatment records, and payment information, must be prepared, cleaned, and formatted for insightful analysis. The recent pandemic has highlighted the importance of data wrangling, as the influx of patient data required immediate organization and cleaning to enable healthcare professionals to make informed decisions swiftly. Artificial intelligence and machine learning have significantly impacted data analytics in the healthcare sector. Data wrangling, a critical process in data analytics, transforms raw data into structured formats, making it easier to analyze.
Furthermore, healthcare organizations can leverage on-premises and cloud-based solutions or adopt multi-cloud strategies to manage their data. Real-time analysis of data is crucial in healthcare, and data wrangling tools enable this by ensuring data is clean, organized, and ready for analysis. Effective data management is crucial in the healthcare sector, and data wrangling plays a vital role in this process. By transforming raw data into structured formats, healthcare organizations can gain valuable insights, improve patient care, and make informed decisions. With the increasing adoption of AI and machine learning in healthcare, data wrangling will continue to be a crucial component of data analytics in this sector.
Lack of awareness of data wrangling tools among SMEs is a key challenge affecting the market growth. Data wrangling, also known as data munging, refers to the process of transforming and cleaning raw data to make it usable for analysis. This is an essential step in gaining valuable insights from big data and addressing time-sensitive business needs. Self-service data preparation tools, which incorporate machine learning and big data analytics technologies, are increasingly popular for this purpose. However, small and medium-sized enterprises (SMEs) continue to rely on traditional Extract, Transform, Load (ETL) tools due to their simplicity and affordability. The limited functionality of these tools for data wrangling among businesses may impede the growth of the market in the coming years.
Additionally, the lack of awareness about data wrangling techniques in developing countries like China and India, which have a vast number of organizations, will further hinder market expansion during the forecast period. Data loss prevention is another critical concern for businesses adopting data wrangling solutions, especially in sectors like finance, life sciences, smart cities, and IoT devices.
The market forecasting report includes the adoption lifecycle of the market, covering from the innovator's stage to the laggard's stage. It focuses on adoption rates in different regions based on penetration. Furthermore, the report also includes key purchase criteria and drivers of price sensitivity to help companies evaluate and develop their market growth analysis strategies.
Customer Landscape
Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.
Alteryx Inc - The company offers data wrangling products such as Alteryx APA Platform.
The market research and growth report includes detailed analyses of the competitive landscape of the market and information about key companies, including:
Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.
The market is experiencing significant growth due to the increasing demand for data analytics in various industries. Businesses are recognizing the value of their data and are investing in solutions to manage, prepare, and analyze it effectively. Data wrangling, also known as data munging, involves the process of cleaning, structuring, and integrating data from various sources to make it ready for analysis. On-premises and cloud-based solutions are popular choices for data wrangling, with multi-cloud strategies and hybrid cloud solutions gaining traction. Large enterprises and SMEs across sectors such as IT and telecom, e-commerce, healthcare, telecommunications, finance, insurance, and life sciences are adopting data wrangling solutions.
Moreover, data security, governance, and digital transformation are key drivers for data wrangling. Real-time analysis, big data analytics, and self-service data preparation are essential features of modern data wrangling solutions. Data quality, data lineage, metadata management, and data cataloging are critical aspects of data wrangling that ensure accurate and reliable analysis. Professional services and consulting firms provide expertise in data encryption, access controls, data protection laws, and technology hubs to help businesses optimize their data wrangling processes. The Internet of Things (IoT) devices, smart cities, and digitalization are creating new opportunities for data wrangling in various industries. Anomaly detection and cloud-based analytics are emerging trends in data wrangling, offering real-time insights and improved data accuracy.
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Market Scope |
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Report Coverage |
Details |
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Page number |
146 |
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Base year |
2023 |
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Historic period |
2018-2022 |
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Forecast period |
2024-2028 |
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Growth momentum & CAGR |
Accelerate at a CAGR of 14.8% |
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Market growth 2024-2028 |
USD 1.4 billion |
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Market structure |
Fragmented |
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YoY growth 2023-2024(%) |
12.5 |
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Regional analysis |
North America, Europe, APAC, Middle East and Africa, and South America |
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Performing market contribution |
North America at 43% |
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Key countries |
US, UK, Germany, China, and Japan |
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Competitive landscape |
Leading Companies, Market Positioning of Companies, Competitive Strategies, and Industry Risks |
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Key companies profiled |
Altair Engineering Inc., Alteryx Inc., Dataiku Inc., DataRobot Inc., Dell Technologies Inc., eXalt Solutions Inc., Hitachi Ltd., Ideata Analytics, Impetus Technologies Inc., Innovative Routines International (IRI) Inc., International Business Machines Corp., Medallia Inc., Microsoft Corp., Oracle Corp., Rapid Insight Inc., SAS Institute Inc., Teradata Corp., TIBCO Software Inc., Wipro Ltd., and Zoho Corp. Pvt. Ltd. |
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Market dynamics |
Parent market analysis, market growth inducers and obstacles, market forecast, fast-growing and slow-growing segment analysis, COVID-19 impact and recovery analysis and future consumer dynamics, market condition analysis for the forecast period |
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Customization purview |
If our market report has not included the data that you are looking for, you can reach out to our analysts and get segments customized. |
We can help! Our analysts can customize this market research report to meet your requirements. Get in touch
1 Executive Summary
2 Market Landscape
3 Market Sizing
4 Historic Market Size
5 Five Forces Analysis
6 Market Segmentation by Sector
7 Customer Landscape
8 Geographic Landscape
9 Drivers, Challenges, and Opportunity/Restraints
10 Competitive Landscape
11 Competitive Analysis
12 Appendix
Research Framework
Technavio presents a detailed picture of the market by way of study, synthesis, and summation of data from multiple sources. The analysts have presented the various facets of the market with a particular focus on identifying the key industry influencers. The data thus presented is comprehensive, reliable, and the result of extensive research, both primary and secondary.
INFORMATION SOURCES
Primary sources
Secondary sources
DATA ANALYSIS
Data Synthesis
Data Validation
REPORT WRITING
Qualitative
Quantitative
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