The data mining tools market is expected to grow from USD 591.2 Million in 2018 to USD 1,039.1 Million by 2023, at a Compound Annual Growth Rate (CAGR) of 11.9%
Northbrook, IL -- (SBWIRE) -- 05/30/2018 -- According to a new market research report "Data Mining Tools Market by Component (Tools and Services), Business Function (Marketing, Finance, Supply Chain and Logistics, and Operations), Industry Vertical, Deployment Type, Organization Size, and Region - Global Forecast to 2023", published by MarketsandMarkets™, the data mining tools market size is expected to grow from USD 591.2 Million in 2018 to USD 1,039.1 Million by 2023, at a Compound Annual Growth Rate (CAGR) of 11.9% during the forecast period. The significant increase in data volume and increased awareness among enterprises to leverage the benefits of available data assets are expected to be the major drivers of the data mining tools market.
Browse 70 market data Tables and 44 Figures spread through 151 Pages and in-depth TOC on "Data Mining Tools Market"
North America is expected to have the largest market size during the forecast period.
North America is expected to be the largest contributor to the data mining tools market in terms of its regional revenue. This region comprises developed countries, such as the US and Canada. North America is open to the adoption of new and emerging technologies, and its strong financial position enables the region to invest majorly in leading tools and technologies for effective business operations. The North American region would be in the leading position over the next few years, due to the increasing usage and early adoption of digital technologies and cloud-based platforms across different industry verticals, such as telecom and IT, Banking, Financial Services, and Insurance (BFSI), healthcare, manufacturing, and retail and eCommerce. The early adoption of digital technologies and cloud-based platforms gives organizations in North America a competitive edge over other organizations from other regions.
The BFSI industry vertical is expected to hold the largest market size during the forecast period.
The BFSI industry vertical includes commercial banks, insurance companies, non-banking financial companies, stock brokerage firms, and payment gateway providers. The BFSI industry vertical deals with large volumes of customer and transactional data from various sources. The diversity and dimensions of financial data are exploding at a faster rate, due to the ever-increasing advancements in internet connectivity. Moreover, the need to gather customer insights from channels, such as Point of Sale (PoS) and Automated Teller Machines (ATMs), enhanced the adoption of data mining tools in the BFSI industry vertical.
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The large enterprises segment is expected to hold the larger market share during the forecast period.
Organizations with more than 1,000 employees are categorized as large enterprises. The use of data mining tools in large enterprises is relatively higher as compared to that of Small and Medium-sized Enterprises (SMEs). This is due to the affordability and high economies of scale, which enable large organizations to benefit from data mining tools. Large enterprises leave no stone unturned to compete in today's cut-throat competitive market. In large enterprises, actionable insights play a major role in evaluating the overall performance of organizations. Good quality data is always a key enabler for organizations to focus on enhancing their core competencies that ultimately lead them to have a competitive edge over their competitors.
Major vendors in the data mining tools market include IBM (US), Microsoft (US), SAS Institute (US), Oracle (US), Intel (US), SAP SE (Germany), RapidMiner (US), KNIME (Switzerland), Teradata (US), MathWorks (US), H2O.ai (US), Alteryx (US), FICO (US), Angoss (Canada), Salford Systems (US), BlueGranite (US), Megaputer (US), Biomax Informatics (Germany), Frontline Systems (US), Dataiku (France), Wolfram (US), Reltio (US), SenticNet (Singapore), Business Insight (Belgium), and SunTec India (Delhi).
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