To stay competitive in an ever-changing technological environment, companies have to use the available data as a tool for predicting what may occur in the future. Thus, predictive analytics plays an essential role in capturing useful information and employ it for modeling customer behaviors, patterns of sales, and other future trends that may affect performance. Data mining is deeply integrated into the processes of predictive analytics and implies the turning of raw data into useful information.
Through the use of software that can process large volumes of data, organizations can get a greater understanding of customers and more effective marketing strategies. Thus, exploring available software packages that enable data mining and predictive analytics can help in developing a perspective on how the existing processes can be improved to meet customer expectations.
Background on RapidMiner
RapidMiner is a software company that provides a package that can be used to facilitate the processes within data and text mining as well as predictive analytics. The basic principle of the software’s operation is associated with the inserting of raw data, which ranges from text to databases, which are later analyzed on a broad scale automatically and intelligently (Stangl and Pesonen, 2018). The applications embedded into the software are extensive and can be used for different purposes. Although, the key focus is placed on machine learning and data mining procedures.
The company was initially a start-up developed by Ingo Mierswa, Simon Fischer, and Ralf Klinkenberg. With its headquarters in Boston, MA, RapidMiner reports having an estimated annual revenue of around $19 million (About RapidMiner, no date). The popularity of the software services offered by the organization has been attributed to the high capacity of employing the Graphical User Interface for designing the analytical process, with the involvement of several operators (About RapidMiner, no date).
These operators represent single tasks in the analytical process, in which the input is produced through an operator’s input (About RapidMiner, no date). According to the purpose statement available on the official website, RapidMiner intends to “bring artificial intelligence to the enterprise through an open and extensive data science platform” (About RapidMiner, no date).
Created specifically to aid analytics teams at companies, RapidMiner facilitates the unification of an entire data science lifecycle from data preparation to machine learning and the deployment of predictive models. As mentioned in the information about the company, more than 6,000 professionals working in the sphere of analytics use the products developed by RapidMiner in order to reduce risks and costs as well as to drive revenue (About RapidMiner, no date).
Organizations that use RapidMiner can engage in the loading and transformation of data, such as the extract, transform, load (ETL) (Sherman, 2015). Moreover, it is possible to facilitate data pre-processing and visualization, implement statistical modeling, data evaluation, and deployment.
Companies use the software for predictive analytics and other objectives based on their needs and capabilities. For example, the company offers such options as both cloud-based and on-premise servers, managed cloud offerings, real-time data scoring, and others. Written in the Java programming language, RapidMiner applications can be applied in different organizational contexts. The extensive list of clients, which includes such companies as Lufthansa, Pepsi, BMW, PayPal, Samsung, Intel, and many others, points to the fact that the organization has a positive reputation
As data mining is among the key specializations of RapidMiner, it is essential to understand the concept in greater detail. Data mining implies the transfer of raw data into useful information. Through using software to identify patterns in large data volumes, organizations can acquire more information about their customers and thus can develop effective marketing strategies, decrease costs, and increase their revenue. Successful data mining, therefore, depends on whether organizations are effective in collecting data, its warehousing, and computer processing.
The overall process of data mining is divided into five distinct stages. The first stage is concerned with organizations collecting information and transferring it into their data storage warehouses. The second stage is data management, and this can be done either through cloud-based or in-house servers. Specialists working in the sphere of analysis, management, and information technology then assess the collected data for determining the ways in which it should be organized. The fourth step is concerned with data sorting based on the results attained as a result of data organization and assessment. Finally, the data is presented in easy-to-share formats, for example, graphs or tables, for the enhanced visualization of information.
Text mining, or text analytics, refers to an artificial intelligence technology (AIT) that employs natural language processing (NLP) for transforming the unstructured text in documents and databases into structured and normalized data that can be used for analysis (What is text mining, text analytics and natural language processing?, no date).
Besides, text mining can facilitate machine learning algorithms, which are essential for helping organizations make predictions for sales, possible changes in the financial environment, or adjust processes based on customer reviews. Text mining is thus heavily applied in knowledge-driven organizations that require the collection of large volumes of new information to answer specific research questions. The process of text mining will help to identify facts, assertions, and relationships that would have otherwise remained unnoticed in the large volumes of data.
Predictive analytics is a branch of data analytics that is targeted at making predictions about the further outcomes on the basis of historical data as well as analytical techniques such as machine learning and statistical modeling. Science used in predictive analytics can help generate future insights with a desired degree of precision (Edwards, 2019). When organizations use predictive analytics, they can employ both past and future data for predicting trends reliably.
Businesses that engage in predictive analytics usually look for ways to save costs and earn profits. For example, retailers use the models for forecasting the requirements of their inventories, managing transportation schedules, and configuring the layouts at stores in order to increase sales. Service providers, such as airlines, also use predictive analytics for setting prices on tickets that would be representative of the previous travel trends. Furthermore, businesses operating in the sphere of hospitality, such as cafes, restaurants, and hotels, use the technology to predict the estimated number of customers on any given day for maximizing revenue and occupancy rates.
Characteristics and Features of RapidMiner
When purchasing the services of RapidMiner, data analysts can build new data mining processes and set predictive analytics. The company’s server lets analysts run processes on enterprise hardware from any device, without imposing and limitations. The server is used for scheduling as well as getting real-time results as well as integrates with other sources of data, letting to add personalized algorithms for comprehensive data mining. Another important characteristic of RapidMiner is interactive dashboards, which are provided on the service’s shared repositories that give access to data, its monitoring, sharing, and task assignment.
RapidMiner has a multitude of features that companies can use to their advantage. For example, the customizable reporting feature offers the capacity for users to create reports based on specific needs, such as dimensions, metrics, and the methods in which they are displayed. The real-time analytics featured provided by RapidMiner offers analysis as soon as new data becomes available (RapidMiner studio feature list, no date).
This makes it possible for users to get immediate insights and make conclusions very quickly after the data enters the system. Data visualization, which is another feature of RapidMiner, implies the graphical representation of available information and data. By using the feature, customers can gain advantage from having access to visualized data, which provides a clear and accessible way to view and understand patterns, outliers, and trends that play essential roles in the data processing.
It is also important to mention that RapidMiner uses one platform that unifies the whole lifecycle of data science, ranging from predictive model operations to machine learning preparation (One platform. Does everything, no date). In RapidMiner Studio, customers can use a visual workflow designer that the whole analytics team can use. Within the RapidMiner Server, users can share and re-integrate predictive models, automate relevant processes, as well as deploy models into production. RapidMiner Radoop helps to eliminate the complexity of data science processes within the Spark and Hadoop frameworks.
Comparison of RapidMiner to Competition
Because of the increased interest of multiple companies in big data tools and their utilization in decision-making, there is a multitude of firms specializing in the services that RapidMiner providers. According to various comparison tools available online, SAS Advanced Analytics is the principal competitor of RapidMiner because of the similarity in the services provided as well as the costs that customers pay. SAS Advanced Analytics specializes in multiple solutions for businesses to facilitate web, social media, and marketing analytics that are useful for effective decision-making. Based on the comparison provided by the Q2 website, RapidMiner exceeds SAS Analytics pricing because the company offers a free trial while the latter does not.
In terms of the ease of use, RapidMiner received a ranking of 8.9 out of 10, while SAS was ranked 7.6 (Compare RapidMiner vs SAS Advanced Analytics, no date). The ease of setup and admin was also superior in RapidMiner, which received 9.1 and 8.5 out of 10, respectively (Compare RapidMiner vs SAS Advanced Analytics, no date). SAS Analytics received 7.8 for both admin and set up categories, which suggested that customers find RapidMiner’s software more user-friendly.
When it comes to the quality of customer service, the rating for RapidMiner was higher by 0.2 points compared with SAS, with companies receiving 8.5 and 8.3 ratings, respectively (Compare RapidMiner vs SAS Advanced Analytics, no date). As for the features, RapidMiner exceeded its competitor in such categories as scripting, data mining, algorithms, data interaction and visualization, decision-making, and data unification.
However, it is notable that SAS Analytics got a better score in the data analysis category (9.3 compared to RapidMiner’s 8.9) (Compare RapidMiner vs SAS Advanced Analytics, no date). Overall, the analysis of customer ratings for RapidMiner and its vital competitor SAS Analytics showed that the company of interest had received better ratings based on customer satisfaction surveys.
Market Statistics and Industry Usage
Big data technologies, which include both data and text mining, are continuing their growth worldwide, with more and more companies using the tools to stay competitive and earn revenue. In 2018, the market size for global big data technologies was valued at $36.8 billion and was forecasted to reach $104.3 by 2026 (Fortune Business Insights, 2020) (see Figure 1). The steady rise in the market size is attributed to the fact that big data technologies such as data mining can be used for making decisions within strategic business initiatives. Furthermore, the deployment of cloud-based services, which RapidMiner also provides, ensures that data mining and other big data tools are used by millions of organizations worldwide.
The market size for predictive analytics utilization across companies has been increasing each year globally. In 2016, the market of predictive analytics was $3.49 billion, according to the report published by Statista (2020). In 2017, the market increased to $4.2 billion, and in 2018, it reached $5.1 billion (Statista, 2020). By the end of 2019, the revenue from predictive analytics was $6.2 billion (Statista, 2020). As estimated by Statista (2020) forecasters, by 2022, the size of the market is expected to reach $10.95 billion. This points to the fact that more and more companies use the services of predictive analytics firms in a large variety of areas, ranging from formulating medical diagnoses to detecting fraudulent activity. Market statistics for the increase in revenues for the service are below (see Figure 2).
When it comes to exploring the use of big data tools, including data mining and predictive analytics, companies operating in multiple spheres, such as telecom, retail, advertising, and healthcare, have paid for the service. For example, Vodafone implemented data mining tools for improving business processes by utilizing the services of Celonis, a company specializing in process mining software. By diving deep into available data and drawing useful inferences for making processes related to information use as transparent as possible (Jones, 2017). Shortly after introducing process mining, Vodafone improved its critical back-end processes as reported by the company’s experts.
Another example of successful data mining use is associated with e-commerce and Amazon. The company used data mining to deliver products to its customers. This is possible through keeping a note of clients’ previous orders and their website activity, which ranges from adding products to their carts to viewing product pages. Data mining allowed Amazon to create an algorithm that manages these activities and give insights on them to administrators. Pre-stored data also facilitates the delivery of orders without having customers to enter their complete address every time they make a purchase (Zatari, 2015). Furthermore, apart from providing support for delivery processes, data mining also reduced the costs of supplying and distributing products.
RapidMiner is a reputable service provider specializing in data and text mining, as well as predictive analytics. Used by hundreds of thousands of analytics professionals worldwide, the company helps companies to increase their revenues, cut costs, and avoid business risks. The comprehensive overview of the company showed that the demand for big data tools is growing with each year, which means that the services of RapidMiner would increase in popularity.
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Edwards, J. (2019) What is predictive analytics? Transforming data into future insights. Web.
Fortune Business Insights. (2018) Big data technology market size, share & industry analysis, by offering (solution, services), by deployment (on-premise, cloud, hybrid), by application (customer analytics, operational analytics, fraud detection and compliance, enterprise data warehouse optimization, others), by end use industry (BFSI, retail, manufacturing, IT and telecom, government, healthcare, utility, others) and regional forecast, 2019-2026. Web.
Jones, S. (2015) Vodafone: making data speak. Web.
One platform. Does everything (no date). Web.
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Sherman, R. (2015) Business intelligence guidebook: from data integration to analytics. Waltham, MA, Elsevier.
Stangl, B. and Pesonen, J. (2018) Information and communication technologies in tourism 2018. Guildfold, UK, Springer.
Zatari, T. (2015) ‘Data mining by Amazon’, International Journal of Scientific & Engineering Research, 6(6), pp. 867-868.