Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, health industry players, etc. are using data mining to discover relationships among everything from price optimization, promotions and demographics to how the economy, risk, competition and online presence are affecting their business models, revenues, operations and customer relationships.Businesses are now harnessing data mining and machine learning to improve everything from their sales processes to interpreting financials for investment purposes. Nowadays, data scientists have become vital to organizations all over the world as companies seek to achieve bigger goals with data science than ever before. In this article you will find key data mining use cases as well as how data mining has opened a world of possibilities for business.
Organizations have access to more data now than they have ever had before. However, making sense of the huge volumes of structured and unstructured data to implement organization-wide improvements can be extremely challenging because of the huge amount of information.
Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities.
Data mining, also called knowledge discovery in databases, in computer science, the process of discovering interesting and useful patterns and relationships in large volumes of data. The field combines tools from statistics and artificial intelligence with database management to analyze large digital collections, known as data sets. Data mining is widely used in business , science research, and government security. It is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. It is a process used by companies to turn raw data into useful information.
The data mining process breaks down into five steps:
- Organizations collect data and load it into their data warehouses
- They store and manage the data, either on in-house servers or the cloud
- Business analysts, management teams and information technology professionals access the data and determine 4. how they want to organize it
- Application software sorts the data based on the user’s results
- The end-user presents the data in an easy-to-share format, such as a graph or table.
Data mining practitioners typically achieve timely, reliable results by following a structured, repeatable process that involves these six steps:
- Business understanding Developing a thorough understanding of the project parameters, including the current business situation, the primary business objective of the project, and the criteria for success.
- Data understanding Determining the data that will be needed to solve the problem and gathering it from all available sources.
- Data preparation Preparing the data in the appropriate format to answer the business question, fixing any data quality problems such as missing or duplicate data.
- Modeling Using algorithms to identify patterns within the data.
- Evaluation Determining whether and how well the results delivered by a given model will help achieve the business goal. There is often an iterative phase to find the best algorithm in order to achieve the best result.
- Deployment Making the results of the project available to decision makers.
There are many data mining techniques organizations can use to turn raw data into actionable insights. These involve everything from cutting-edge artificial Intelligence to the basics of data preparation, which are both key for maximizing the value of data investments:
Data cleaning and preparation
Data cleaning and preparation is a vital part of the data mining process. Raw data must be cleansed and formatted to be useful in different analytic methods. Data cleaning and preparation includes different elements of data modeling, transformation, data migration, data integration, and aggregation. It’s a necessary step for understanding the basic features and attributes of data to determine its best use.
Tracking patterns is a fundamental data mining technique. It involves identifying and monitoring trends or patterns in data to make intelligent inferences about business outcomes. Once an organization identifies a trend in sales data, for example, there’s a basis for taking action to capitalize on that insight. If it’s determined that a certain product is selling more than others for a particular demographic, an organization can use this knowledge to create similar products or services, or simply better stock the original product for this demographic.
Classification data mining techniques involve analyzing the various attributes associated with different types of data. Once organizations identify the main characteristics of these data types, organizations can categorize or classify related data. Doing so is critical for identifying, for example, personally identifiable information organizations may want to protect or redact from documents.
Association is a data mining technique related to statistics. It indicates that certain data are linked to other data or data-driven events. It is similar to the notion of co-occurrence in machine learning, in which the likelihood of one data-driven event is indicated by the presence of another. This means that the analysis of data shows that there is a relationship between two data events: such as the fact that the purchase of hamburgers is frequently accompanied by that of French fries.
Outlier detection determines any anomalies in datasets. Once organizations find aberrations in their data, it becomes easier to understand why these anomalies happen and prepare for any future occurrences to best achieve business objectives. For instance, if there’s a spike in the usage of transactional systems for credit cards at a certain time of day, organizations can capitalize on this information by figuring out why it’s happening to optimize their sales during the rest of the day.
Clustering is an analytics technique that relies on visual approaches to understanding data. Clustering mechanisms use graphics to show where the distribution of data is in relation to different types of metrics. Clustering techniques also use different colors to show the distribution of data. Graph approaches are ideal for using cluster analytics. With graphs and clustering in particular, users can visually see how data is distributed to identify trends that are relevant to their business objectives.
Regression techniques are useful for identifying the nature of the relationship between variables in a dataset. Those relationships could be causal in some instances, or just simply correlate in others. Regression is a straightforward white box technique that clearly reveals how variables are related. Regression techniques are used in aspects of forecasting and data modeling
Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into the future. Thus, it gives organizations insight into what trends will happen next in their data. There are several different approaches to using predictive analytics. Some of the more advanced involve aspects of machine learning and artificial intelligence. However, predictive analytics doesn’t necessarily depend on these techniques —it can also be facilitated with more straightforward algorithms.
This data mining technique focuses on uncovering a series of events that takes place in sequence. It’s particularly useful for data mining transactional data. For instance, this technique can reveal what items of clothing customers are more likely to buy after an initial purchase of say, a pair of shoes. Understanding sequential patterns can help organizations recommend additional items to customers to spur sales.
Decision trees are a specific type of predictive model that lets organizations effectively mine data. Technically, a decision tree is part of machine learning, but it is more popularly known as a white box machine learning technique because of its extremely straightforward nature.
A decision tree enables users to clearly understand how the data inputs affect the outputs. When various decision tree models are combined they create predictive analytics models known as a random forest. Complicated random forest models are considered black box machine learning techniques, because it’s not always easy to understand their outputs based on their inputs. In most cases, however, this basic form of ensemble modeling is more accurate than using decision trees on their own.
Statistical techniques are at the core of most analytics involved in the data mining process. The different analytics models are based on statistical concepts, which output numerical values that are applicable to specific business objectives. For instance, neural networks use complex statistics based on different weights and measures to determine if a picture is a dog or a cat in image recognition systems
Data visualizations are another important element of data mining. They grant users insight into data based on sensory perceptions that people can see. Today’s data visualizations are dynamic, useful for streaming data in real-time, and characterized by different colors that reveal different trends and patterns in data. Dashboards are a powerful way to use data visualizations to uncover data mining insights. Organizations can base dashboards on different metrics and use visualizations to visually highlight patterns in data, instead of simply using numerical outputs of statistical models.
A neural network is a specific type of machine learning model that is often used with AI and deep learning. Named after the fact that they have different layers which resemble the way neurons work in the human brain, neural networks are one of the more accurate machine learning models used today.
Data warehousing is an important part of the data mining process. Traditionally, data warehousing involved storing structured data in relational database management systems so it could be analyzed for business intelligence, reporting, and basic dashboarding capabilities. Today, there are cloud data warehouses and data warehouses in semi-structured and unstructured data stores like Hadoop. While data warehouses were traditionally used for historic data, many modern approaches can provide an in-depth, real-time analysis of data.
Long-term memory processing
Long term memory processing refers to the ability to analyze data over extended periods of time. The historic data stored in data warehouses is useful for this purpose. When an organization can perform analytics on an extended period of time, it’s able to identify patterns that otherwise might be too subtle to detect. For example, by analyzing attrition over a period of several years, an organization may find subtle clues that could lead to reducing churn in finance.
Machine learning and artificial intelligence
Machine learning and artificial intelligence (AI) represent some of the most advanced developments in data mining. Advanced forms of machine learning like deep learning offer highly accurate predictions when working with data at scale. Consequently, they’re useful for processing data in AI deployments like computer vision, speech recognition, or sophisticated text analytics using Natural Language Processing. These data mining techniques are good for determining value from semi-structured and unstructured data.
Data mining allows you to:
- Sift through all the chaotic and repetitive noise in your data.
- Understand what is relevant and then make good use of that information to assess likely outcomes.
- Accelerate the pace of making informed decisions.
- Data mining helps companies to get knowledge-based information.
- It can be implemented in new systems as well as existing platforms
- Data mining helps organizations to make the profitable adjustments in operation and production.
- Facilitates automated prediction of trends and behaviors as well as automated discovery of hidden patterns.
- The data mining is a cost-effective and efficient solution compared to other statistical data applications.
- Data mining helps with the decision-making process.
- It is the speedy process which makes it easy for the users to analyze huge amount of data in less time.
The predictive capacity of data mining has changed the design of business strategies. Now, you can understand the present to anticipate the future. These are some uses cases and examples of data mining in current industry:
- Marketing. Data mining is used to explore increasingly large databases and to improve market segmentation. By analysing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behaviour in order to direct personalised loyalty campaigns. Data mining in marketing also predicts which users are likely to unsubscribe from a service, what interests them based on their searches, or what a mailing list should include to achieve a higher response rate.
- Banking. Banks use data mining to better understand market risks. It is commonly applied to credit ratings and to intelligent anti-fraud systems to analyse transactions, card transactions, purchasing patterns and customer financial data. Data mining also allows banks to learn more about our online preferences or habits to optimise the return on their marketing campaigns, study the performance of sales channels or manage regulatory compliance obligations.
- Education. Data mining benefits educators to access student data, predict achievement levels and find students or groups of students which need extra attention. For example, students who are weak in maths subject.
- E-Commerce. E-commerce websites use Data Mining to offer cross-sells and up-sells through their websites. One of the most famous names is Amazon, who use Data mining techniques to get more customers into their eCommerce store.
- Retail. Supermarkets, for example, use joint purchasing patterns to identify product associations and decide how to place them in the aisles and on the shelves. Data mining also detects which offers are most valued by customers or increase sales at the checkout queue.
- Service Providers. Service providers like mobile phone and utility industries use Data Mining to predict the reasons when a customer leaves their company. They analyze billing details, customer service interactions, complaints made to the company to assign each customer a probability score and offer incentives.
- Medicine. Data mining enables more accurate diagnostics. Having all of the patient’s information, such as medical records, physical examinations, and treatment patterns, allows more effective treatments to be prescribed. It also enables more effective, efficient and cost-effective management of health resources by identifying risks, predicting illnesses in certain segments of the population or forecasting the length of hospital admission. Detecting fraud and irregularities, and strengthening ties with patients with an enhanced knowledge of their needs are also advantages of using data mining in medicine.
- Insurance. Data mining helps insurance companies to price their products profitable and promote new offers to their new or existing customers.
- Manufacturing. With the help of Data Mining Manufacturers can predict wear and tear of production assets. They can anticipate maintenance which helps them reduce them to minimize downtime.
- Crime Investigation. Data Mining helps crime investigation agencies to deploy police workforce (where is a crime most likely to happen and when?), who to search at a border crossing etc.
- Television and radio. There are networks that apply real time data mining to measure their online television (IPTV) and radio audiences. These systems collect and analyse, on the fly, anonymous information from channel views, broadcasts and programming. Data mining allows networks to make personalised recommendations to radio listeners and TV viewers, as well as get to know their interests and activities in real time and better understand their behaviour. Networks also gain valuable knowledge for their advertisers, who use this data to target their potential customers more accurately.
- Bayer helps farmers with sustainable food production Weeds that damage crops have been a problem for farmers since farming began. A proper solution is to apply a narrow spectrum herbicide that effectively kills the exact species of weed in the field while having as few undesirable side effects as possible. But to do that, farmers first need to accurately identify the weeds in their fields. Using Talend Real-time Big Data, Bayer Digital Farming developed WEEDSCOUT, a new application farmers can download free. The app uses machine learning and artificial intelligence to match photos of weeds in a Bayer database with weed photos farmers send in. It gives the grower the opportunity to more precisely predict the impact of his or her actions such as, choice of seed variety, application rate of crop protection products, or harvest timing.
- Air France KLM caters to customer travel preferences The airline uses data mining techniques to create a 360-degree customer view by integrating data from trip searches, bookings, and flight operations with web, social media, call center, and airport lounge interactions. They use this deep customer insight to create personalized travel experiences.
- Groupon aligns marketing activities One of Groupon’s key challenges is processing the massive volume of data it uses to provide its shopping service. Every day, the company processes more than a terabyte of raw data in real time and stores this information in various database systems. Data mining allows Groupon to align marketing activities more closely with customer preferences, analyzing 1 terabyte of customer data in real time and helping the company identify trends as they emerge.
- Domino’s helps customers build the perfect pizza The largest pizza company in the world collects 85,000 structured and unstructured data sources, including point of sales systems and 26 supply chain centers, and through all its channels, including text messages, social media, and Amazon Echo. This level of insight has improved business performance while enabling one-to-one buying experiences across touchpoints.
You can use data mining to solve almost any business problem that involves data, including:
- Increasing revenue.
- Understanding customer segments and preferences.
- Acquiring new customers.
- Improving cross-selling and up-selling.
- Retaining customers and increasing loyalty.
- Increasing ROI from marketing campaigns.
- Detecting fraud.
- Identifying credit risks.
- Monitoring operational performance.
Organizations can get started with data mining by accessing the necessary tools. Because the data mining process starts right after data ingestion, it’s critical to find data preparation tools that support different data structures necessary for data mining analytics. Organizations will also want to classify data in order to explore it with the numerous techniques discussed above.
1.Oracle Data Mining
Oracle Data Mining popularly knownn as ODM is a module of the Oracle Advanced Analytics Database. This Data mining tool allows data analysts to generate detailed insights and make predictions. It helps predict customer behavior, develops customer profiles, identifies cross-selling opportunities.
Rapid Miner is one of the best predictive analysis systems, it is written in JAVA programming language. It provides an integrated environment for deep learning, text mining, machine learning & predictive analysis. It offers a range of products to build new data mining processes and predictive setup analysis.
Orange Data Mining
It is a perfect software suite for machine learning & data mining. It best aids the data visualization and is a component based software. The components of Orange are called “widgets.” These widgets range from preprocessing and data visualization to the assessment of algorithms and predictive modeling. Widgets deliver significant functionalities such as:
displaying data table and allowing to select features, data reading, training predictors and comparison of learning algorithms, data element visualization, etc.
Weka has a GUI that facilitates easy access to all its features. It is written in JAVA programming language. Weka is an open-source machine learning software with a vast collection of algorithms for data mining. It supports different data mining tasks, like preprocessing, classification, regression, clustering, and visualization, in a graphical interface that makes it easy to use. For each of these tasks, Weka provides built-in machine learning algorithms which allow you to quickly test your ideas and deploy models without writing any code.
It is the best integration platform for data analytics and reporting developed by KNIME.com AG. It operates on the concept of the modular data pipeline. KNIME constitutes of various machine learning and data mining components embedded together. It is a free, open-source platform for data mining and machine learning. Its intuitive interface allows you to create end-to-end data science workflows, from modeling to production. And different pre-built components enable fast modeling without entering a single line of code. A set of powerful extensions and integrations make KNIME a versatile and scalable platform to process complex types of data and use advanced algorithms. With KNIME, data scientists can create applications and services for analytics or business intelligence. In the financial industry, for instance, common use cases include credit scoring, fraud detection, and credit risk assessment.
Sisense is another effective Data mining tool. Sisense is extremely useful and best suited BI software when it comes to reporting purposes within the organization. It has a brilliant capability to handle and process data for the small scale/large scale organizations. It instantly analyzes and visualizes both big and disparate datasets. It is an ideal tool for creating dashboards with a wide variety of visualizations. It allows combining data from various sources to build a common repository and further, refines data to generate rich reports that get shared across departments for reporting. Sisense generates reports which are highly visual. It is specially designed for users that are non-technical. It allows drag & drop facility as well as widgets. Different widgets can be selected to generate the reports in form of pie charts, line charts, bar graphs etc. based on the purpose of an organization. Reports can be further drilled down by simply clicking to check details and comprehensive data.
Dundas is another excellent dashboard, reporting & data analytics tool. Dundas is quite reliable with its rapid integrations & quick insights. It provides unlimited data transformation patterns with attractive tables, charts & graphs. Dundas BI puts data in well-defined structures in a specific manner in order to ease the processing for the user. It constitutes of relational methods that facilitate multi-dimensional analysis and focuses on business-critical matters. As it generates reliable reports, thus it reduces cost and eliminates the requirement of other additional software.
Intetsoft is analytics dashboard and reporting tool that provides iterative development of data reports/views & generates pixel perfect reports. It allows the quick and flexible transformation of data from various sources.
Qlik is Data mining and visualization tool. It also offers dashboards and supports multiple data sources and file types.It has the following features: drag-and-drop interfaces to create flexible, interactive data visualizations, instantly respond to interactions and changes, supports multiple data sources and file types, allows easy security for data and content across all devices, allows you to share relevant analyses, including apps and stories, using a centralized hub.
MonkeyLearn is a machine learning platform that specializes in text mining. Available in a user-friendly interface, you can easily integrate MonkeyLearn with your existing tools to perform data mining in real-time. Start immediately with pre-trained text mining models like this sentiment analyzer, below, or build a customized solution to cater to more specific business needs. MonkeyLearn supports various data mining tasks, from detecting topics, sentiment, and intent, to extracting keywords and named entities.MonkeyLearn’s text mining tools are already being used to automate ticket tagging and routing in customer support, automatically detect negative feedback in social media, and deliver fine-grained insights that lead to better decision making.
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