Data Wrangling Market Size, Share & Trends Analysis Report By Component (Solution, Service), By Deployment Mode (On-premises, Cloud-based), By Organization Size (Large Enterprises, SMEs), By Business Function (Finance, Marketing and Sales, Operations, Human Resources), By Industry Vertical (BFSI, Manufacturing, Healthcare, Government, Retail and E-Commerce, IT and Telecom, Education) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2023-2031

Data Wrangling Market Size

The global data wrangling market size was valued at USD 2,818.50 million in 2022. It is estimated to reach USD 12,406.31 billion by 2031, growing at a CAGR of 17.9% during the forecast period (2023–2031).

Businesses are implementing big data analytics to enhance consumer experiences and boost organizational efficiency, creating a profitable prospect for market expansion.

Transforming unusable data into a useful form is known as data wrangling. Data munging and data cleanup are some names for it. Data wrangling is the term used to describe a set of procedures used to examine, restructure, and evaluate raw datasets to produce high-quality data from their cluttered and complicated forms. Wrangled data is utilized to provide insightful knowledge and direct business decisions. Data wrangling's main objective is to assist companies in shortening the time spent gathering and organizing data.

Additionally, data wrangling enables data scientists to concentrate primarily on analysis instead of data wrangling. "data wrangling" refers to transforming the available raw data into the desired format by cleaning, reorganizing, and improving it. Data wrangling has also changed the process, replacing time-consuming, laborious attempts to master diverse data sources. Data wrangling has many benefits, including processing large volumes of data and organizing enormous amounts easily. Data wrangling typically involves six iterative processes: discovery, structuring, cleaning, enriching, validating, and publishing.

Data Wrangling Market Growth Factors

Increased adoption of big data analytics by multiple organizations

The use of big data analytics is fueled by an increase in investments in data-wrangling tools among numerous enterprises to spur revenue growth and enhance service efficiencies. Furthermore, as big data analytics gain prominence across various geographies, senior executives of several firms are rapidly adopting various styles of analytics to address their business imperatives, accelerating market growth.

• For instance, 53% of businesses in the United States and Canada, according to a 2017 Forbes poll in North America, use big data analytics to boost service productivity and revenue growth. Additionally, businesses are implementing big data analytics to enhance consumer experiences and boost organizational efficiency, creating a profitable prospect for market expansion.

Rises in demand for cloud-based big data analytics software in smes 

Small and medium-sized firms now face lower upfront investment expenses thanks to the increased accessibility of cloud vendors' affordable data centers, lowering the entry barrier. Small- and medium-sized businesses now have a higher demand for big data analytics software hosted in the cloud.

• For instance, one of the leading market participants, Oracle Corporation, stated that 75% of its 400,000 clients are small- to medium-sized firms, which presents a wide range of commercial options. Additionally, cloud services have made it simpler for businesses to use their big data analytics solutions by resolving the difficulty of storing and accessing data.

By employing cloud-based big data analytics, businesses can store all their data on a single platform, extending information consistency to all devices while spending less on various sources for each device.

Restraining Factors

Lack of awareness of data wrangling tools among smes

The market's expansion is hampered by low product awareness and a strong prevalence of conventional extract, transform, and load (ETL) methods. With many emerging technologies like machine learning and big data analytics, data wrangling addresses time-sensitive business scenarios and acquires valuable insights from raw data. As a result, organizations lack the necessary functionality for enterprises to use this technology, which hinders the market's expansion. Additionally, the market's expansion is hampered by organizations in developing nations like China and India, unaware of data wrangling procedures. The high implementation costs of wrangle technology further hamper the market's expansion.

Market Opportunities

Growth of edge computing

The way data is stored, processed, and distributed to millions of consumers is changing due to edge computing. Additionally, edge computing helps real-time applications analyze and handle the data that has been acquired, which is another important element that creates profitable market opportunities. Additionally, edge computing in data handling is developing due to its varied characteristics of cloud storage, which enables businesses to readily access their data and provide various security features to the companies' critical data. As a result, the development of edge computing has aided numerous firms in achieving data security and dependability. Additionally, the market is anticipated to benefit from the growing demand to analyze massive volumes of data that result from the development of internet-of-things (IoT) by end users.

Regional Insights

North america: dominant region with a 15.1% market share

North America is the most significant Global Data Wrangling Market shareholder and is estimated to exhibit a CAGR of 15.1% over the forecast period. It is a valuable center for market innovation because it is home to some of the key players in performance analytics. Data wrangling is a practice business in North America that efficiently prepares data for accurate analytics and wise decision-making. Additionally, the BFSI organizations have been increasingly utilizing data wranglers, which help them to streamline processes and inform agents on how to connect with clients online while cutting down on time spent on data preparation by 15x. As a result, these firms can access their clients' full profiles.

• For instance, The Royal Bank of Scotland uses big data, primarily unstructured and semi-structured data from online client web chats, to ensure its 30 million customers receive great service worldwide. To quickly extract insights from unstructured data, it uses data wranglers. Over the projected period, this adoption is anticipated to increase demand for data wranglers.

Additionally, the availability of several data-wrangling suppliers and ongoing technological improvements are fostering the expansion of this market. The expansion of the data-wrangling industry is further aided by the rapid uptake of big data analytics across various industries, including manufacturing, professional services, banking, and federal and central government.

Europe: fastest growing region with the highest cagr

Europe is anticipated to exhibit a CAGR of 18.4% over the forecast period. Although there is a sizable on-premises performance analytics deployment in Europe, there may be huge prospects for expanding the data-wrangling industry, given the prevalence and accessibility of cloud computing for mass users. In addition, several government laws and regulations designed to improve an organization's security and privacy are fueling the expansion of the data-wrangling business.

• For instance, to guarantee proper protection for the personal data of the employees of the enterprises throughout the European states, the European government passed a law in 2016. Furthermore, the development of the data-wrangling industry in Europe is being fueled by the significant move toward cloud deployments, inexpensive storage, higher levels of automation, and data processing platforms.

Asia-Pacific has emerged as one of the fastest-growing regions in the global market. The large firms in this region are concentrating on looking for and implementing solutions that will allow their company to apply sophisticated data preparation and cleaning methods. Additionally, enterprises need an enabler solution that drastically cuts the time and expense needed for raw data processing. So that it can aid businesses in developing a collaborative data culture that hastens the production of new value based on data. Additionally, businesses are updating their methods for building and managing data pipelines. They are no longer solely reliant on legacy, segregated data integration to handle the available data's speed, scale, and diversity. The market for data wrangling in the Asia-Pacific region is expanding due to all these factors.

In LAMEA, the factors propelling the growth of this market are the expansion of big data technologies across numerous industry verticals and the increased adoption of artificial intelligence by organizations to acquire a competitive edge. The LAMEA region currently sees low adoption of data-wrangling tools and related services compared to other regions. However, this segment is predicted to increase at a moderate rate throughout the projection year due to the rising adoption of big data technologies, cloud computing, and rising awareness of digitalization. The data-wrangling market is also anticipated to be driven by large organizations' growing investments to expand into the undeveloped region.

Data Wrangling Market Segmentation Analysis

By components

The solution supplements and nutraceuticals segment dominates the global market and is projected to exhibit a CAGR of 16.8% over the forecast period. For enterprises, solutions for handling enormous volumes of data and extracting actionable insights from various data sources are specifically created. For associative datasets, these methods are frequently quicker, more closely correspond to the architecture of object-oriented programs, and scale larger data sets. Solutions for "data wrangling" are made to handle enormous amounts of unstructured data from many operational systems within an organization.

Additionally, the volume of data is always growing, making it challenging for businesses to manage it using a traditional relational database infrastructure. As a result, demand for data-wrangling technology is projected to surge. Furthermore, data-wrangling platforms will likely become key components of corporate technology stacks, integrating various data sources and building custom graph-based applications. This is expected to accelerate the technology's widespread adoption.

By deployment mode

The on-premises segment dominates the global market and is predicted to exhibit a CAGR of 16.5% during the forecast period. The solution's installation and the ability for organizations of all sizes to run on systems already existent on an organization's premises rather than putting them on server space or the cloud are made possible by the on-premises deployment paradigm for data wrangling tools. These solutions have improved security features, encouraging their use in significant financial institutions and other companies that handle sensitive data where security is a top priority. On-premises solutions are renowned for superior server maintenance, and continuous systems make installing these data wrangling easier.

Additionally, the on-premises deployment approach is very helpful in large businesses because it requires a sizable investment and necessitates the acquisition of connected servers and a system management solution. The need for this sector of the data-wrangling market is also driven by greater data security compared to cloud-based solutions, which encourages adoption among enterprises.

By organization size

The large enterprise segment owns the highest market share and is predicted to exhibit a CAGR of 16.1% over the forecast period. Large businesses are those that employ more than 10,000 people. Due to servers and other important resources inside the network premises, they typically concentrate significant sections of their IT security budgets at the perimeter. Large businesses also have a specialized IT team to oversee security operations and ensure procedures like patch management, standards compliance, and routine policy changes are followed. Numerous big businesses increasingly use data-wrangling solutions, including retail, pharmaceutical, finance, oil and gas, healthcare, and government.

These businesses tend to have a variety of data sources as well as a genuine need for data discovery and analysis. The raw data is combined and presented with business context and meaning using the semantic layer made possible by graph database technology. Customers utilize data wrangling to gain fresh perspectives on vastly different data, including historical and contemporary data. It works well for applying algorithms and analysis to a huge amount of data to discover pertinent links, entities, and insights. Data wrangling also is gaining popularity as more big businesses realize its transformative ability to reveal the linkages hidden in their massive data, which is advantageous for the market.

By business function

The operations segment is the most significant contributor to the market and is estimated to exhibit a CAGR of 16.4% over the forecast period. Ordering, accounting, stock control, warehouse management, refunds, and logistics are all part of the operations and supply chain functions. Each operation calls for transmitting and receiving data, typically in files in XML, CSV, or other formats. It requires careful administration because it includes transmitting and receiving many data. Most of the information in these exchanged files is encrypted. It pertains to products, items that are newly available for sale, items that are out of stock, price changes, and items that have been ordered and need to be delivered to a customer's delivery location. All these various data types and structures are aligned through data wrangling so the system functions effectively.

Data transformation and type-to-type mapping are included in these operations. Due to the necessity to quickly turn a variety of consumer data sources, including text files, Excel files, access databases, and more, for analysis, businesses are now required to employ data-wrangling technologies in their operations and supply chain functions. These factors are driving the market for data wrangling for operation functions.

By industry verticals

The BFSI segment owns the highest market share and is predicted to exhibit a CAGR of 16% over the forecast period. The ongoing digital revolution has led to increased fraud and data breaches. While fraud cannot be entirely avoided, there are several measures institutions may take to reduce the problem considerably.

• For instance, banks increasingly use graph database technology to focus on banking data relationships and find potentially fraudulent conduct patterns. The market is expanding primarily due to the increase in financial institutions employing graph database solutions to address various data issues, particularly to detect fraud scenarios in advance.
• For instance, PayPal does advanced fraud detection on eBay and StubHub transactions specifically, for this reason, using graph approaches. This has already allowed PayPal to undertake predictive fraud research while saving the corporation more than $700 million.

Additionally, standard database systems, essential for some forms of protection, are not built to catch the most sophisticated fraud schemes. As a valuable complement to any financial services firm's security toolkit, however, graph databases offer the singular capacity to identify significant fraud trends in groups or individually quickly. Because of this, the BFSI industry should anticipate an increase in data-wrangling technology deployment in the next years.

Recent Developments

• February 2023- Alteryx has generally made its Analytics Cloud GA Platform available. It features a new decision intelligence function under Auto Insights and a revised user interface for Designer Cloud. The platform has been updated with new versions of products like Designer Cloud and Auto Insights.
• November 2023- Amazon SageMaker now includes geospatial and governance features from AWS. The industry leader in cloud computing updated Data Wrangler with 40 new data connections and added geographic and data governance tools to its machine learning platform.