Data Wrangling Market Witnessed To Flourish the Size of $5,581.18 Million By 2026

As per the report, the global data wrangling market was estimated at $1.45 billion in 2018 and is expected to reach $5.58 billion by 2026, growing at a CAGR of 18.4% from 2019 to 2026.

Rise in the adoption of big data analytics software among multiple organizations and an increase in cloud-based big data analytics software adoption among SMEs drive the growth of the global data wrangling market.

However, lack of awareness related to data wrangling tools among SMEs and issues related to data quality hinder the market growth. On the other hand, advancements in edge computing would offer new opportunities in the industry.

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The research offers a detailed segmentation of the global data wrangling market based on component, organization size, deployment model, business functions, industry verticals, and geography.

Based on component, the solution segment accounted for the major market share in 2018, holding nearly three-fourths of the global market, and is estimated to maintain the highest contribution throughout the forecast period. However, the service segment would register the fastest CAGR of 20.8% during the forecast period.

On the basis of the deployment model, the on-premise segment accounted for nearly three-fifths of the total market share in 2018 and is estimated to maintain its dominance by 2026. Simultaneously, the cloud segment is estimated to maintain the highest CAGR of 20.1% from 2019 to 2026.

Based on geography, North America held nearly two-fifths of the total revenue of the global market in 2018, and is expected to maintain the dominant share during the forecast period. However, Asia-Pacific is projected to witness the fastest CAGR of 20.9% during the forecast period.

Leading players of the global data wrangling market analyzed in the research include IBM Corporation, SAS institute, Oracle Corporation, Hitachi Vantara, Tibco Software, Alteryx, Teradata Corporation, Trifacta Software Inc., Impetus, and Paxata Inc.

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