1. Increasing volume and variety of data: The exponential growth in the volume and variety of data from various sources such as social media, IoT devices, and sensors is driving the demand for data wrangling solutions.
2. Adoption of cloud-based data wrangling platforms: The growing adoption of cloud-based data wrangling platforms is providing organizations with scalability, flexibility, and cost-effectiveness, thus driving market growth.
3. Rising demand for self-service data preparation: The need for self-service data preparation tools that enable non-technical users to access and prepare data without the involvement of IT professionals is fueling market growth.
4. Advancements in artificial intelligence and machine learning: The integration of advanced AI and ML technologies into data wrangling tools is helping organizations automate and streamline the data preparation process, driving market growth.
Industry
Report Coverage | Details |
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Segments Covered | Business Function, Component, Deployment Model, Organization Size, End User |
Regions Covered | • North America (United States, Canada, Mexico) • Europe (Germany, United Kingdom, France, Italy, Spain, Rest of Europe) • Asia Pacific (China, Japan, South Korea, Singapore, India, Australia, Rest of APAC) • Latin America (Argentina, Brazil, Rest of South America) • Middle East & Africa (GCC, South Africa, Rest of MEA) |
Company Profiled | IBM, Oracle, SAS Institute, Trifacta, Datawatch, Talend, Alteryx, Dataiku, TIBCO Software, Paxata, Mindtech Global. |
1. Data privacy and security concerns: The increasing concerns related to data privacy and security are acting as restraints for the data wrangling market, as organizations are cautious about handling sensitive and confidential data.
2. Lack of skilled workforce: The shortage of skilled professionals with expertise in data wrangling techniques and tools is a major restraint for market growth, as organizations struggle to effectively utilize data wrangling solutions.
3. Integration challenges with legacy systems: The difficulty in integrating data wrangling solutions with existing legacy systems and applications poses a restraint for market growth, as organizations face challenges in optimizing their data preparation processes.