Best Data Mining Software
Data mining is the process of extracting useful data from a large set of raw data and analyzing hidden data patterns for different perspectives to categorize the data into useful information. The collected data is stored in a centralized location, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision management, and other information requirements to optimize costs and business operations. Data mining software empowers users across various industry verticals by helping them gather valuable information about their customers and developing effective strategies for different business functions. This, in turn, enables users to leverage the resources in an optimal and insightful manner.
Top 10 Data Ming Software
- SPSS Modeler
- Wolfram Mathematica
- SAS Enterprise Miner
- Oracle Data Mining
The data volume is increasing exponentially owing to the tremendous rise in connected devices, sensor grids, user-generated content, and internet services. Furthermore, the advent of the Internet of Things (IoT) and increased adoption of advanced technologies by enterprises would add up significantly to the global data volume. Enterprises are now realizing the importance of leveraging the generated data to optimize their business processes and efficiencies. Organizations across different industry verticals have huge volumes of data, collected from their stakeholders and business operations. With this increase in data, enterprises have realized the importance of extracting hidden data patterns for actionable insights from every business aspect.
Best Data mining software helps in structuring the data sets from large volumes of unstructured raw data for knowledge discovery. It helps enterprises by enabling them to gather valuable information about their customers and developing effective strategies for different business functions. This, in turn, enables users to leverage the resources in an optimal and insightful manner.
The Best Data Mining solution helps businesses to collect valuable information from a vast collection of raw data and analyse secret data trends to categorise it into valuable information. In the international data mining software industry, data mining controlled services is projected to be used widely by companies and gain momentum. Managed services also assist companies to concentrate on customer-centred aspects and to bring value to corporate practises.
Mostly during the predicted period in the worldwide data mining software industry, the data mining advertising business feature is projected to have the majority of the market share. In addition, the right data analysis solution gives insights into consumer purchasing habits and lets companies navigate the demand and supply match when giving their customers personalised targeted deals. Due to the higher need for accountability between vendors, distributors, and consumers, the production process and distribution market functions are expected to gain momentum during the projected timeline.
Data mining solution helps in collecting, analyzing, and storing data on a single reporting interface to extract hidden data patterns for maintaining consistency and facilitating the decision-making process. Furthermore, it enables the manufacturers and suppliers to maintain transparency of their demands and requirements using a single integrated platform. The advent of electronic commerce is further expected to boost the adoption of data mining solutions for effective supply chain and logistics operations.
Retail, manufacturing, telecom and IT, BFSI, and healthcare and life sciences are among the industry verticals that have shown tremendous adoption of data mining solutions. This is because of the data-centric characteristics of these industry verticals. The retail industry vertical is expected to have the largest market size during the forecast period. Data mining solution helps enterprises in the arena of retail in target marketing different categories of customers based on how recently, frequently, and how much customers spend. It helps organizations in promoting customer-based tailored offerings to increase sales and forecast stock availability.Cloud-based data mining is likely to be adopted at a faster pace during the forecast period, as it reduces the IT cost required for storing and computing large volumes of data on-premises. The enterprises in the region are early technology adopters and have adopted the data mining solutions aggressively to leverage the data generated by their own business processes for future business growth. The APAC region is expected to grow at the fastest rate during the forecast period, owing to the increased awareness among enterprises to adopt data mining solutions for better productivity and smooth business process functioning.
This section identifies the best data mining solution providers based on two different parameters: product maturity and company maturity. Product maturity includes parameters such as product portfolio, set of features and functionality, support and services provided, applications and licensing type. Company maturity includes parameters such as regional reach and geographies covered, industries catered, end-users, partner network and source of revenue.
Best Data Mining Solution vendors in this category are Mozenda, Wolfram Mathematica, SPSS Modeler and RapidMiner
Best Data Mining Solution vendors in this category are Orange and Octoparse
Best Data Mining Solution vendors in this category are SAS Enterprise miner and Oracle Data Mining
Best Data Mining Solution vendors in this category are ExportData and FS.net
The market is expected to grow further in North America and Europe and is projected to grow at a significant pace in APAC, Latin America, and MEA. The key factors driving the growth rate of the market are a significant increase in data volume and increased awareness among enterprises to leverage the available data assets. These factors are fuelling the demand for effective data mining solutions. However, the varying structure of government rules and regulations is expected to limit the growth opportunities for proprietary solution vendors in the data mining software market. Apart from the drivers and restraints, there are a few lucrative opportunities for best data mining solution providers. Opportunities such as increasing need to create insights from raw data and the need for embedded intelligence to gain competitive advantage are expected to present new market growth prospects for the data mining solution vendors.
Potential increase in the volume of data
Businesses are generating huge volumes of data with the advent of IoT and cloud technologies. Moreover, connected devices, linked to the internet, are considered to be one of the significant factors that are increasing the data volume. The use of these devices is expected to grow in the future, which would also result in the generation of huge amounts of data. The big data has provided substantial benefits but has also produced several risks pertaining to its quality and security. According to the recent study released by IBM, nearly 2.5 quintillion bytes of data are created each day. It is also estimated that the volume of big data would be 40 zettabytes by 2020, with nearly an increase of 300 times since 2005. Because of these factors, the need to manage and integrate data from various sources has become vital for businesses, as this data would result in actionable business insights. Businesses can produce insights from large and diverse data. Data mining provides a core set of technologies that helps organizations predict future outcomes, discover new revenue streams, and enhance business performance.
Better understanding among companies to exploit the information resources available
Organizations are making use of data to make critical business decisions. Organizations are also collecting data from variable sources to better understand their market and gain competitive advantage. The increasing need to analyze relevant data from companies across industry verticals has driven the need to find answers for business questions from the raw data. Organizations have access to data and are working to optimize it to make good relevant decisions. With such a huge data asset at disposal, organizations are eager to leverage it and convert it into actionable insights, which would ultimately help them maximize their revenue.
Laws and regulations set by the government
With the explosion of data, data-driven services have become universal and, apparently, this is elevating dependencies of the user and business on these technologies. The dynamic technological trends of smart connected devices, collection of data, and data storage are anticipated to accelerate the computing capabilities of organizations. Organizations are restricted from using the data fully to get actionable insights. The varying structure of regulatory policies, along with the changing macroeconomic factors, makes it difficult for vendors to meet the end-user requirements. Due to this, organizations must constantly make changes in their data mining architecture to keep up with the changing regulation policies. Hence, the varying structure of regulation policies may act as a restraint to the growth of the data mining software market.
A growing need to derive ideas from raw data
Data is being generated on a large scale from various data sources. Be it Fitbit, Google, or Facebook, the use of data has evolved and is being considered as an integral part of digital transformation for any business. The use of IoT continues to increase, and the generation of insights from all incoming data is the need of the hour. In today’s business environment, organizations are using data mining to extract the data from a pool and to find patterns in the extracted data, which would enable enterprises to process the data more quickly and efficiently. Most of the unstructured data, obtained from various sources, is not being used. Unstructured data, such as email correspondences, account information, and old versions of related documents, is not being used to create value in business decisions. For instance, banks can pay heed to how an online customer landed on the application page so that they can take more such measures to attract customers. The utilization of raw data from different sources can help companies gain valuable insights for driving their business. Best Data mining software integrates and refine all the data, whether structured or unstructured, from various data sources. Hence, the increasing need to generate insights from raw data is expected to create an opportunity for the growth of the data mining software market.
To achieve a competitive edge, the need for embedded intelligence
Embedded intelligence comprises the integration of self-service intelligence tools to support enhanced experience with visualization, real-time analytics, and interactive reporting. It also comprises dashboards that can be used to display relevant data via charts, graphs, or reports. Many players in the data mining software market are providing visualization tools as an add-on feature to data mining. Organizations are finding alternatives not to switch between platforms, but for getting accustomed to different user interfaces and frameworks to carry out mining activities. It would be easier for organizations to work within the same application than to buy, install, or familiarize themselves with other additional tools. Embedded intelligence would help organizations shorten the time spent, from ingestion of data to its analysis, which would lead to effective analytics.
Privacy, confidentiality, and dependability of data
In today’s digital and connected world, a massive amount of data is being processed and transferred constantly. With the increasing number of applications, services, and users utilizing data mining solution, the reliability and security of data are prone to internal as well as external threats, such as cybercrimes. A data mining tool extracts the data from several data centres, applications, and services, and is simultaneously accessed by multiple users. Thus, it is necessary to enhance the security and reliability of systems by constantly updating the security and privacy features of the tool. In information-critical applications, such as financial services, data scientists are liable for customers’ critical data and confidential company information. Hence, it is necessary to monitor the process continues to minimize security risks and address privacy concerns.
The emergence of BI and AI raises questions for providers of data mining
With the advent of BI and AI, organizations are implementing BI to gain a competitive advantage over their counterparts. Most of the BI vendors are offering best data mining solutions as a part of the BI solution itself. Core data mining tool providers are facing difficulties while offering their tools to enterprises, as they must compete with BI solution providers. Data mining tool vendors had to come up with distinguished offerings to provide value-added services along with data mining. Enterprises tend to implement solutions that cater to the entire process of business decision-making. As more and more enterprises are adopting AI and BI solutions, it is a rising concern for data mining tool vendors. Data mining tool vendors must come up with innovative offerings to sustain in the market.
Best Data Mining Software, By Component
The significant increase in data volumes and the increased awareness among enterprises to leverage actionable insights from data has led to an increase in demand for data mining solution. The scarcity of data scientists on one end and the massive amounts of data being generated on the other is expected to have provided significant traction to the data mining software market. The ability to process raw data (structured and unstructured) and provide results in the form of actionable insights has proven to be the most adopted practise among various organizations. The rapid advancement of various technologies, such as big data analytics, IoT, and machine learning, has significantly increased the demand for utilizing data to make quicker decisions. The global data mining software market is expected to be driven by various factors, such as the growing demand for data-driven technologies among organizations and the advent of big data. This chapter provides detailed information about the complete value chain of the data mining software market.
Data mining software comprise standalone software and platforms that facilitate organizations to analyze data from various perspectives and dimensions. Best Data mining software enables organizations to identify patterns in large data sets and extract informational insights from them. Additionally, these tools facilitate organizations to classify and group data and identify relationships among the data. The data mining tools finds its applications across various industry verticals, including BFSI, retail, telecommunications, and manufacturing. Large enterprises and SMEs use data mining software to gain insights from various factors, such as demographics, promotions, pricing, business risks, competition and economic factors, operations, revenues, business models, and customer relationships.
The amount of data that needs to be analyzed is increasing day-by-day, due to the increasing number of data sources from where the data is generated. In such cases, the adoption of data mining solution has become essential for governing the data and its sources, and access and identify the patterns in the data. Services form an integral part of the data mining tools. The data mining services segment includes managed services, consulting and implementation, and other services. The other services include training and education, and support and maintenance. Managed services are valued based on their usage and provided by third-party vendors. Professional services are obtained directly from the parent company without any mediator involved. Professional services could be provided in the form of consulting and implementation, support and maintenance, and training and education and support and maintenance.
Best Data Mining Software, By Service
The data mining services segment includes managed services, consulting and implementation, and other services.
Managed services is an umbrella term for the services provided by third parties, and include monitoring and maintenance of computers, networks, and software. It has become an absolute necessity for companies to concentrate on their core business operations to sustain in the highly competitive global market. Managed services assist organizations in optimizing their data mining practices cost-effectively. The managed service providers also ensure the smooth functioning of business operations.
Consulting and Implementation
Consulting services provide various solutions and services to organizations. Depending on the requirements of organizations, different enterprises require different consultation services. These services provide knowledge to organizations about the implementation of software and services. The consulting services help enterprises make better and informed business decisions. These services aid in designing business-specific solutions for deploying testing services. They also help end-users in integrating data mining solutions into their IT environment. This helps the end-users adopt new data mining solutions faster, along with an end-to-end consultative approach for infrastructure modernization. Consulting services are crucial for all organizations, largely for the non-IT companies, owing to their lack of understanding of data mining tools.
The other services include training and education, and support and maintenance. Training and education services are given as per the requirements of companies. Training services deliver communal and onsite courses as well as personalized courses, depending on organizational requirements. These training services can be provided onsite and remotely. These services can also provide various educational programs to mitigate the knowledge gaps for the employees. The services simplify various complex tasks, such as the deployment of data mining solutions. Subsequently, the training services also work as an implementation guide for the IT workforce of organizations.
Support and maintenance services are crucial, as they directly deal with customer issues, which in turn, impact the customer satisfaction quotient. Every solution vendor has a dedicated support team that supports the needs of customers. They can also be offered in the form of onsite help. Furthermore, support and maintenance services are important for the appropriate configuration, enhancement, and extension of the life cycle of the deployed data mining solution. Additionally, support and maintenance services focus on the seamless maintenance and transformation of business-critical applications and help meet the ever-changing demands of customers.
Best Data Mining Software, By Business Function
With advancements in technologies, enormous amounts of data are being generated at a rapid rate. Much of the generated data can provide actionable insights into the global business ecosystem, which can be instrumental in improving the sales, redesigning marketing strategies, and comprehending the changing perceptions and behaviours of the target customers. Data scientists are working hard to uncover the hidden patterns and trends in the generated data with the help of data mining solutions. With these solutions, the data scientists are also trying to analyze the data to gain significant insights about customers’ behaviour and buying patterns. Integrating best data mining solutions would prove to be helpful for data-driven professionals and data scientists who wish to unlock consumer insights by uncovering the trends and patterns of customers’ buying behaviours, which would further help in enhancing organizations’ operational performance. The data mining software market by business function has been categorized into marketing, finance, supply chain and logistics, and operations.
Data mining is at the core of every department. It facilitates decision-making and generates actionable insights from the data gathered from various sources. For instance, Tesla analyzed more than 100 million miles of driving data for building self-driving cars. Another instance to quote is the data analysis facilitated by Netflix to understand which kind of TV shows to produce. Marketers have been utilizing historical data to make strategic decisions. The need for actionable insights from data has augmented the demand for data mining solutions and data scientists in organizations worldwide.
Moreover, data mining is used for various marketing functions, such as predicting a customer’s lifetime value, sharing the wallet estimation, upselling, discount targeting, target marketing, customer segmentation based on demographics, and identifying customer attrition/turnover. Organizations rely on advanced analytics to determine the reason for the rising customer turnover. They also come up with strategies to address this issue and diminish the churn. Data mining solutions enable marketing professionals to analyze patterns from extensive customer data available throughout the organization. It helps marketing professionals combine this information with external observations and interactions over channels, and determine and forecast if there could be a customer churn shortly.
Many corporate activities rely on the implementation of the finance industry, as it centres around creating profit/loss calculations, income statements, and annual report evaluation. Bad data quality can be damaging to the success of the organisation. The decision-making capabilities of financial leaders, financial controllers, or CFOs could be seriously hindered by data quality problems or outdated technologies, stopping them from providing effective management reports. In order to define risk factors, optimise market practises, spend judiciously, assess performance, define target consumers and forecast future events, the finance business application actively uses the power of analytics. It is therefore imperative for this sector to ensure a high standard of results. High-quality data empowers companies with detailed insights into budgeting, planning, estimating, and related tasks, and it is also expected that the financial industry will lead in terms of utilising data mining instruments.
Supply Chain And Logistics
Accurate forecasting of demand for products and services, essential modification of services, and the emergence of new business models are examples of exploiting, untouched sources of data. Logistics controls every aspect of Supply Chain Management (SCM), starting from order picking from the stock, managing the stock to maintain an adequate level, proper packaging for excellent delivery, to routing to avoid chokepoints by tracking the package and ensuring the delivery is done on time, for which various parameters such as traffic optimizations and network planning are considered. Ever since logistics operations have been computerized, enormous amounts of data are being generated. Consequently, it has become a significant challenge for organizations to avoid human errors on the input side of the business processes.
Supply chain and logistics are responsible for the optimization of business processes, satisfying customers, expanding globally, and building a strong delivery and transport network to strengthen the global/local presence of an organization. Data scientists apply advanced mathematics and statistics to address numerous business queries that deliver insights into the management processes that are required to maximize the return on assets and investments. In logistics, network planning is essential, and the topology and volume of a distribution network are adopted as per expected demand. The results from this stage of planning usually drive investments and payback cycles. The external economic data related to specific industries and the regional growth predictions are also considered to generate the exact prediction of transportation volume demand. Data mining solutions help in sorting data to support efficient network planning and network optimization by analyzing the historical data of transportation routes.
Data mining solutions enable enterprises to capture operational data from various sources, such as smart devices, IoT-enabled devices, networks, and edge devices and process the data to generate timely insights. These solutions also enable organizations to analyze the data effectively for uncovering the anomalies, performance bottlenecks, and security threats. This enables enterprises to improve their operations and streamline their business processes for providing quality services. Data mining solutions can also be embedded in workflows to optimize the business processes.
Data mining solutions enable enterprises to increase their operational efficiency by helping them discover data from various sources and finding patterns and relationships among the data. This is achieved by analyzing the network data, which gives deeper insights into the system, thereby enabling enterprises to reduce their maintenance costs, eliminate unnecessary truck rolls, and automate the process of creating/updating network tickets based on advanced KPI monitoring, baselining, and anomaly detection. Service providers can better manage the performance of networks and mitigate security threats.
Best Data Mining Software, By Industry Vertical
Data mining tools are likely to gain significant traction in the age of big data. Enterprises would seek to harness significant value from structured and unstructured data. Real-time data analytics, combined with predictive and prescriptive modelling, can help improve customer service and marketing campaigns and enhance loyalty programs. Various organizations across different industry verticals have been interested in gaining valuable and actionable insights from the data generated from different sources. This has resulted in the increased demand for best data mining solutions among enterprises across industry verticals. Moreover, to automate the entire delivery pipeline in software development and testing, organizations are said to be implementing data mining services. On the basis of industry verticals, the data mining software market has been segmented into retail, BFSI, healthcare and life sciences, manufacturing, telecom, government and defense, IT, energy and utilities, others (media and entertainment, and education).
In today’s connected world, online users are growing continuously. Retailers across the globe have significant opportunities to leverage the data about their customers and design business strategies accordingly. Retailers are eager to grab this opportunity to gain a competitive advantage in the market. Retailers such as Amazon, Nordstrom, Warby Parker, Rebecca Minkoff, and IKEA are constantly innovating their products by analyzing their customers’ experience with the help of data mining tools. These tools enable these retailers to distinguish themselves from the other players in the retail industry vertical. Apart from improving the customer experience through personalized shopping experiences, data mining is also helping retailers in predicting trends and styles, forecasting demands and managing the inventories effectively, identifying potential customers, optimizing the pricing structure, creating effective customer loyalty programs, and merchandising.
Banking, Financial Services, and Insurance
This vertical deals with huge amounts of customer data and their respective transactions from different sources. This data is stored across different departments and applications in silos. It is estimated that a typical bank has over 600 million data elements, which are worth USD 1.2 billion. The diversity and dimensions of financial data are exploding at a faster rate, due to the ever-increasing advancements in internet connectivity, empowering social media activities and smartphone communications, and gathering real-time market inputs and transactional details. This has leveraged the capturing and sorting of diverse data for future analysis to build predictive models. Moreover, the need to gather customer insights from channels, such as Point of Sale (PoS) and Automated Teller Machines (ATMs), has additionally enhanced the adoption of the best data mining software in the BFSI industry vertical.
Healthcare and Life Sciences
The healthcare and life sciences industry vertical generates large volumes of data related to patients, clinical trials, claims, hospital systems, finances, pharmacies, and wearable devices. With a massive increase in the data generated, the need for data mining tools emerges more strongly. Best Data mining software play an important role in the healthcare and life sciences industry vertical, beginning with the recording of patients’ information until the data related to discharging patients. This life cycle includes information related to patient treatment, payment, improvements, research, and government reporting. Healthcare organizations and institutions use this information to improve their operational efficiency, increase safety, enhance the quality of patient care, and reduce costs. Healthcare companies use data mining tools to identify and observe high-risk patients and chronic diseases and design the right interventions needed for them. Healthcare facilities and groups leverage data mining toolsto provide better services to their patients. These tools help healthcare organizations in providing information that would help staff recognize patient preferences, resulting in improved patient satisfaction.
Telecom and IT
The telecom and IT industry vertical is one of the most competitive and has been adopting solutions that can integrate with the current assets of enterprises, and at the same time, reduce the risks from various threats, such as cyber-attacks. The telecom service providers generate massive amounts of data in the form of customer information, subscriber calling, messaging service information, financial and billing information, and various related information, which are present across different departments. The telecom industry vertical therefore faces challenges in improving the response time, analyzing the traffic, and studying products’ affinity to create a happy client base. The data mining tools and services provide several benefits to telecom organizations. The benefits include higher customer satisfaction due to enhanced customer experience, assistance in establishing smarter networks, and helping generate new revenue sources.
In today’s digital world, the growing competition is the key factor that is influencing enterprises to stay abreast of innovative technologies. There are huge chunks of data generated in the telecom and IT industry vertical, and the data includes log data and usage history data. To manage the enormous volumes of data generated, it has become very important for the telecom and IT industry vertical to deploy best data mining software. The high demand for real-time analysis and correlation of data from multiple sources is expected to drive the adoption of the best data mining software in the telecom and IT industry vertical.
Government and Defense
Data is a critical asset for the government and defense agencies to ensure public safety and security, develop the infrastructure, and offer better public services. This industry vertical is likely to witness a significant challenge in the digital information arena. This has resulted in the need for developing better capabilities to manage and understand the data generated from smart connected devices, wearables, social networks, and IoT devices. Governments require an effective and efficient tool to extract the data from discrete sources and gain in-depth insights into the geography or terrain, weather conditions, route conditions, and the location of nearby critical facilities for on-field operations. With the right information at the right time, the government and defense organizations can plan and execute better strategies and successful operations.
Energy And Utilities
The energy and utility industry vertical is observing a major transformation, due to the advent of technologies such as smart meters, smart sensors, and IoT devices. With these developments, there is unprecedented growth in the amount of data being generated, creating a demand for technologies that can efficiently capture this data and consolidate them for further analysis. Energy and utility companies capture data from various locations and sources, such as oil rigs, generation plants, utility grids, gas plants, and sensors in a distributed environment. Energy and utility enterprises use this data to extract critical insights into business processes for optimizing the assets, reducing the downtimes, bridging the real-time demand and supply gaps, and identifying the consumption patterns, which are the key parameters for the business growth.
The manufacturing industry vertical has undergone substantial changes in recent times, due to the emergence of industry 4.0 regulations and smart manufacturing initiatives. The manufacturing industry vertical includes some of the most mission-critical and data-intensive production processes that deploy heavy-duty data analytics and data management systems. Data is generated from various sources, such as from within a factory, along with the analogue data, images, raw sensor data, and the information churned out from applications that are used inside the factory. The other sources of data generation include the Enterprise Resource Planning (ERP) systems, manufacturing execution systems, time and attendance logs, supplier information, and various process automation and control systems. With the right data mining strategy in place, manufacturers can understand their plant’s performance across multiple metrics, better forecast the product demand and production, and provide quality services and support to their customers.
The other industry verticals include media and entertainment, and education. The media and entertainment companies face real-time challenges, such as content optimization, website management, and adding live feeds during the management, sharing, and distribution of content. A reliable data mining tool is required to help the media and entertainment companies in effectively accessing data from external as well as internal sources. An effective data mining tool can aid with various analytics requirements, such as motion picture and content distribution analytics, production cost analytics, financial analytics and forecasting for content distribution, and royalty payment analytics for digitalized companies.
The use of data mining solution in the education industry vertical is new in the market. The major factors that are expected to be driving the growth of data mining solution and services in the education industry vertical include the demand for understanding historical data for designing real-time recommendations based on a student’s learning ability. The concept of smart schools is also emerging in developed regions, where data mining tools are playing a vital role in formulating learning plans.
Best Data Mining Software, By Deployment Type
The data mining solution market by deployment type has been segmented into on-premises and cloud. Companies choose their deployment type depending on their requirements and size. They have been focusing more on the cloud deployment type in recent times, as it empowers them with several benefits, such as increased reliability, scalability, interoperability, and cost-effectiveness. Due to the easy deployment and growing awareness of flexibility among the vendors, the cloud deployment type is expected to grow at a higher CAGR.
On-premises data mining solutions provide support, enabling significant enhancements for data validation, versatile standardization, and robust matching, as these are done with the help of local monitoring. Large enterprises adopt on-premises data mining software and services, as these empower the enterprises in limiting their dependence on third-party resources and having full control over the tools by building their own infrastructure. The on-premises deployment type involves competent management by the IT teams of organizations, which have large capitals to expand the IT infrastructure. However, the high cost attached to on-premises solutions and the expenses involved in the expansion of IT resources is expected to limit the adoption of on-premises data mining solutions among the SMEs. Moreover, the on-premises deployment type offers several benefits, such as centralized control for better management of resources, without compromising on security. Hence, many enterprises continue to invest in the on-premises deployment type and also develop new cloud business models for deploying the data mining solutions.
With advancements in the cloud technology in recent years, organizations are looking forward to shifting to on-cloud data mining solutions for relishing the benefits of scalability, streamlined IT, easy deployment, and reduced expenditure. Cloud-based data mining solutions offer organizations subscription-based pricing. With third-party service providers managing the deployed data mining solutions, enterprises can focus on their core functions, such as data processing, instead of having to deal with additional workloads, such as integration, installation, configuration, optimization, maintenance, and support for IT infrastructure.
Cloud-based data mining solutions provide enterprises’ data scientist teams flexibility in terms of the data storage capacity of the system. In a cloud-based data mining solution, data storage is virtually limitless and can be scaled up with the increase in data volume, with no additional effort in modifying the existing applications. The cloud deployment type allows enterprises to increase their operational efficiency while reducing the ownership cost.
A cloud data mining solution can scale up, and the existing data models and algorithms can easily run with the increasing amount of data. In a cloud-based environment, data scientists can collaborate faster, without having to copy the data to their remote workstations before analysis. With enterprises aspiring to be more insight-driven, they prefer to focus on their core business functions. Therefore, cloud-based data mining solutions are expected to be rapidly adopted among organizations for instant access to data and analysis in several use cases.
Best Data Mining Software, By Organization Size
This section specifies the market segmentation based on the use of data mining solution and services across organizations of different size. The organization size has been segmented on the basis of the number of employees in an organization. On the basis of organization size, the data mining software market has been segmented into SMEs and large enterprises. For any large or SME, access to critical business information, optimizing resources, and integrated reporting are of the utmost importance when it comes to managing the performance of business operations. The data mining solutions and services are said to be gaining traction among the SMEs and large enterprises across all major industry verticals. Enterprises across various industry verticals are at the forefront of adopting data mining tools, owing to the increasing amount of data volumes. With the expansion of enterprises, the data keeps growing, and these enterprises need data quality mining to gain actionable insights from the data for better decision-making.
Organizations with more than 1,000 employees are categorized under the large enterprises' segment. These enterprises leave no stone unturned in competing with today’s cut-throat competitive market. For instance, the manufacturing industry vertical generates abundant amounts of data throughout its operating cycle. However, the generated data is unstructured and consists of inconsistencies, which leads to future complexities. Thus, the need for data mining is at its peak in the manufacturing companies. In large enterprises, actionable insights from the data generated play a major role in evaluating the overall performance of an organization.
Small And Medium-Sized Enterprises
Organizations with employees between 1 and 1,000 are categorized under the SMEs segment. Cost-effectiveness is an important need for the SMEs, as they always face constraints in marketing themselves and gaining visibility due to limited budgets. SMEs face the need for integrating their data systems to keep their business processes running smoothly and cost-effectively. The integration of both cloud as well as on-premises data is vital for the SMEs to gain operational efficiency. Data mining tools can be extremely helpful in evaluating data and achieving business objectives. With the help of advanced analytics and good quality of data, SMEs can take proper actions. High-quality data also helps organizations in improving the understanding of customers’ demands and formulating business strategies accordingly.
For SMEs, it is crucial to find data mining software and solutions that are highly cost-effective and deliver high performance. The SMEs segment is expected to have a huge potential to flourish in the data mining software market during the forecast period.
The greatest strength of Octoparse is the easy interface that it has. The first of a kind data mining platform where you do not need to know any codes for data extraction. Even a beginner can use Octoparse to the best of its ability. Just copy and paste the URL and run the extract data options, hat is all one has to do.
XLMiner software is a type of analytic solver that integrates a large number of productive or prescriptive analysis. Its services also include forecasting, optimizing, data mining, and simulation of various data problems in Excel. The best advantage of the software is that almost every business, irrespective of its size, can use this software for its analytic project requirement. XLMiner pricing is also affordable.