Sun, 26 Jan 2020

Data Wrangling market size is forecast to reach $4.74 billion by 2025, after growing at a CAGR of 19.5% during 2020-2025. Data wrangling is primarily preferred to transform and map data into a valuable analytical insight by using advanced analytics algorithms.This process is popularly known as data munging. This deals with the data visualization, and data aggregation. Rising prevalence ofanalyticsdriving the usage of data wrangling tools across multiple verticals, not only to filter out unwanted data but also to standardize the data which would be efficiently used for AI and ML algorithms, thereby aiding to provide the best insights. The increase in the acceptance of IoT devices along with growing smart cities,worldwide impacts the global data wrangling market revenue during the forecast period.

Key Takeaways

• North America dominates the data wrangling market owing to an increasing number of agreement and partnership strategies by big data analytics solution providers along with the rising implementation of digital technologiesto introduce advanced data model in this region.

• Data wrangling has become the choice of business analysts to reduce the time taken to collect, organizeand wrangle the data and to increase the focus on analyzingthe data in order to make effective decisions which will increase the market demand for data wrangling in the near future.

• The growing popularity of data wrangling tools across large enterprises to analyze ideal data among the datasets is likely to aid in the market growth of data wrangling.

Business Function- Segment Analysis

Finance business function segment held the largest share in the data wrangling market as of 2018.This business function needs analytics to determine risk factors,increase business processes,invest judiciously, access profitability, identify target customers, and predict future events. Thus, the data wrangling tool utilization propel the power of analyticsmainly in finance business function. Besides, the analytics software assists to improve client relations, driving revenue, meeting regulatory obligations, managing risks, streamlining back-office processes, and emerging high-quality products & services.Therefore, the extensive necessity and use of data wrangling in the finance business is burgeoning the growth of the global data wrangling market.

Organization Size- Segment Analysis

Large enterprises has been the primary market for data wrangling. Presently, enterprises are deploying data wrangling tools as a part of comprehensive analytical solutions to gain cleaned, profiled and standardized data. This data is helpful to the operations team to have a grip on their systems and business model as well. The continuous flow of data different verticals of the enterprises has led to a shift from traditional ETL tools to advanced automated tools which is advantageous even before its integration across the AI and ML technologies. Additionally, the rise of digitalization is augmenting the adoption of data wrangling tools across the regions,toinvestigate and analyze the data among the datasets.

Geography- Segment Analysis

North America dominated the data wrangling market with a share of more than 35%,followed by Europe and APAC.This region is elevating as the largest contributor specifically, related to the adoption of data wrangling tools and services. This is because of the presence and operations of some major vendors of data analytics and data wrangling tools, namely IBM, Hitchi Vantara, Trifacta,SAS, and Oracle in this region.Furthermore, the growing number of agreement and partnership strategiesare indicating the rise in the adoption rate of digital technologies in this region.

Drivers –Data Wrangling Industry Outlook

• Growing demand foradvanced analytical algorithm – "data wrangling"to pick up valid insights to further improve business entity at multiple industry verticals

Multifunctional data wrangling tool is an effective method to examine the user performance, for far-reaching analytical solutions, to identify the log pattern and also the root cause of equipment failure efficiently. Moreover, it offers a self-service data preparation model and supports the organizations to clean the data sets and avoid the need for a data scientist. Thus, this feature of data wrangling is attracting the executives in the organization to gather business insights by analyzing both structured and unstructured data without the intervention of IT teams.

• Rapid pace of data generation and technological advancements in machine learning and AI are boosting the data wrangling

Data wrangling implementation has resulted in improved work efficiency thatplays a crucial role in widening the scope for business. In addition to this, increasing regulatory pressure along with the growth in edge computing are providing significant number of opportunities for analytics solution providers during the forecast period.

Challenges –Data WranglingIndustry

• Time-Intensive nature of data wranglingis a challenge

The detailed structure nature of the process including analysis base table, de-normalized transactions, time series and document library, which is consuming overtime in producing analytical data sets.Added workload, shifting priorities are some of the challenges that arise during the implementation of data wrangling. Thus, all these factors together diminishing the growth of the data wrangling market.

MarketLandscape

Acquisitions, Product launches, and R&D activitiesare key strategies adopted by players in the data wrangling market to stand ahead of the curve in the data wrangling market share. In 2018, the market of data wrangling has been consolidated by the top five players accounting for 32% of the share. Alteryx, Dataiku, Hitachi Vantara, IBM, Informatica, Oracle, Paxata, Trifacta, TIBCO Software, and Talendare the data wrangling top 10 companies.

Acquisitions/Product Launches

  • In January 2018, Datawatch Corporation signed an agreement of acquisition with Angoss Software Corporation for an amount of $24.5 million. This acquisition will enhance the capabilities of Datawatch's Monarch data intelligence offering which supportsdata scientists to perform predictive and prescriptive analytics forvaried enterprise applications.
  • In July 2018, Trifacta announced that the increasing usage of wrangler products is visible at financial services organizations to address compliance initiatives which include CCAR, AML, and BCBS 239. This also indicates the growth of the company especially, in the financial services industry by optimizing analytics and operational use cases.

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