Data Wrangling is a process of cleaning, blending, and wrangling data into a desired format so that it could be used by the analytics engine to provide insights and help business officials make better business decisions. With the proliferation of data, the data wrangling tools are expected to be adopted at a rapid pace as they are a precursor to the analytics workflows. A typical data wrangling tool encompasses functions, such as reformatting, de-duping, filtering, and cleaning of data such that proper analysis could be done.
Healthcare and life sciences vertical is expected to grow at the highest CAGR during the forecast period
Data wrangling in healthcare is gaining traction. Various types of data related to patients, such as personal information; disease, treatment, and medical history; and payment data are generated, which need to cleaned, prepared and set in a proper format to do analysis. Data wrangling tools would be adopted exponentially with the rise of digitalization in the healthcare and life sciences vertical. A complete end-to-end digitalization in healthcare would only be possible if right tools are adopted to clean, de-duplicate, and format the data to investigate and analyze such datasets.
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The Operations business function is expected to grow at the highest CAGR during the forecast period
Enterprises are deploying comprehensive analytical solutions to recognize the log pattern, identify the root cause, and analyze the user behavior. Data wrangling tools are thus expected to be aggressively used toward cleaning, profiling, and standardizing data. It would help operations team to have a wider control over their systems.
With edge analytics gaining widespread adoption, data wrangling tools are expected to be adopted across verticals. These tools would enable to filter out unwanted data and systemize the data that would be efficiently used by AI and ML algorithms to analyze and provide due insights. Operations being a core function is expected to adopt data wrangling tools significantly in near future.
With the significant increase of data around numerous business functions, such as marketing and sales, finance, operations, HR, and legal, the need to ascertain the quality of data increases rapidly. The major driving factors for the data wrangling market include increasing volume and velocity of data and advancements in AI and ML technologies. The global data wrangling market size is projected to reach USD 3.18 billion by 2023, growing at a Compound Annual Growth Rate (CAGR) of 19.7% during the forecast period (2018–2023). The major vendors in this market include Trifacta (US), Datawatch (US), Dataiku (France), IBM (US), SAS Institute (US), Oracle (US), Talend (US), Alteryx (US), TIBCO (US), Paxata (US), Informatica (US), Hitachi Vantara (US), Teradata (US), Datameer (US), Cooladata (US), Unifi (US), Rapid Insight (US), Infogix (US), Zaloni (US), Impetus (US), Ideata Analytics (India), Onedot (Switzerland), IRI (US), Brillio (US), and TMMData (US).
Players in this market have adopted different strategies, such as new product launches, product developments, partnerships and collaborations, and acquisitions, to expand their presence and enhance their market shares. Moreover, acquisitions and partnerships are the major growth strategies adopted by the vendors in this market. IBM, Oracle, and Trifacta are some of the key players existing in this market that are having their presence across the globe.
IBM is one of the eminent players in the data wrangling market. It has a significant presence in more than 175 countries and serves its customers across the globe. IBM has undertaken both organic and inorganic growth strategies to gain a leading market edge. As a part of its organic growth strategy, in July 2017, IBM unveiled its Watson-based service platform that is built on IBM Cloud to supplement human intelligence by offering the cognitive technology that helps users to enhance their productivity. The company also adopted inorganic strategies, such as partnerships and collaborations, to boost its revenue growth. For instance, in April 2017, IBM partnered with Rocket Fuel, an ad technology company, to enable Rocket Fuel to integrate IBM’s Watson Discovery platform with its Predictive Marketing platform.
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Trifacta, another prominent player in the market, has also adopted both organic and inorganic strategies to sustain its position in the market. It has established strategies, such as business expansion, and partnership and acquisitions, in the recent past to serve its customers better. In May 2017, Trifacta launched a new product, Spring ’17 Wrangler Enterprise, to leverage various benefits across the enterprise, including advancements in self-service scheduling, sharing, and sampling. This would enhance the growth of data wrangling projects in the enterprise environment. Moreover, in March 2017, Trifacta collaborated with Google to create Google Cloud Dataprep. This solution would enable Google to help enterprises shift their data to the cloud, where in Trifacta would help them resolving their data wrangling challenges.
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