Global Data Wrangling Market: Overview
The data wrangling market could be classified on the basis of enterprise size, deployment model, component, business function, and industry. Based on the deployment model, the market can be segmented into on-premises and cloud-based. Out of these two, the cloud-based segment is likely to increase, as it will help companies to avoid huge expenditure on software, data, staff, and maintenance cost.
Global Data Wrangling Market: Trends and Opportunities
Globally, the demand for data wrangling tools has increased significantly, as it helps in examining user performance, identify the log pattern, and provides extensive analytical solutions to identify the root cause of equipment failures efficiently and effectively. The key functions of data wrangling are standardizing, profiling, and cleaning data that helps in proper analyses of data. One of the factors responsible for the growth of data wrangling is it offers the self-service data preparation model. This model helps organizations to clean the data sets by themselves without involving data scientist. This factor has effectively empowered the executives in an organization to draw business insights without the intervention of IT teams.
On the other hand, the reluctance in shifting from ETL tools to automated tools might affect the growth of the data wrangling market. Moreover, efforts made in to maintain data quality and limited knowledge about the data wrangling tools in SMEs can further limit the growth of the market.
Global Data Wrangling Market: Geographic Analysis:
As per the geography, the data wrangling market covers North America, Asia Pacific, Latin America, Europe, and the Middle East and Africa as its key regions. Among these regions, North America is expected to lead the data wrangling market on the accounts of rising adoption of data wrangling services. Moreover, the rising amount of data that is being collected every day has increased the demand for data wrangling at a large scale. Over the coming years, Asia Pacific is likely to offer lucrative growth opportunities for the data wrangling market. Increasing data collection in countries like China and India is projected to drive this market.
Global Data Wrangling Market: Companies Mentioned
Information provided in this section helps the stakeholders, investors, and various industry players to take better and well-informed decisions. This information also helps in analyzing the current market scenario and making strategies accordingly for the future. Currently, Hitachi Vantara, Oracle, SAS Institute, Dataiku, Datawatch, Talend, Alteryx, TIBCO Software, Informatica, IBM Corporation, Trifacta, and Paxata these are the few prominent market players operating in the market. Research and development, mergers and acquisitions, partnerships, collaboration, and innovation are some of the key starategies used by these players. These help them in expanding their business across nations by penetrating in various untapped regions and increase their customer base.
Market segmentation based on geography:
- North America
- South America
- Asia Pacific
- Middle East and Africa
This report gives access to decisive data, such as:
- Market growth drivers
- Factors limiting market growth
- Current market trends
- Market structure
- Market projections for the coming years
Key highlights of this report include:
- Overview of key market forces propelling and restraining market growth
- Up-to-date analyses of market trends and technological improvements
- Pin-point analyses of market competition dynamics to offer you a competitive edge
- An analysis of strategies of major competitors
- An array of graphics and SWOT analysis of major industry segments
- Detailed analyses of industry trends
- A well-defined technological growth map with an impact-analysis
- Offers a clear understanding of the competitive landscape and key product segments
Note: Although care has been taken to maintain the highest levels of accuracy in TMR’s reports, recent market/vendor-specific changes may take time to reflect in the analysis.