The importance of data mining in today’s digital world is expanding rapidly due to the massive data explosion that is taking place everywhere. Data and data mining are pervasive; their uses and effects may be seen all over. To clean up and make sense of the raw data for better decision-making, data miners use a variety of innovative data mining tools, methods, and other data mining techniques including regression (predictive engine), clustering analysis, anomaly/outlier identification, classification analysis, and association rule learning.
What is Data Mining? – How it works & its benefits
Data mining, in its simplest form, is the process of cleaning unstructured raw data to find trends, track patterns, and anomalies within large or small datasets to transform them into useful information to gain data-driven insights, make smart decisions, and predict outcomes for organizations. Data mining may assist firms in gaining business knowledge, boosting operational efficiency and productivity while cutting down expenses by utilizing various statistical approaches and algorithms.
The advantages of data mining are enormous, and its applications can be felt across industries. With the fine-tuning of complex large and small data sets, data mining assists in the discovery of anomalies (defects), functions as a prediction engine (forecasting outcomes), and makes recommendations (recommendation engine).
The popular applications of Data Mining that are disrupting major industries include
- Telecom – Every minute, telecom businesses around the world generate enormous, unimaginable volumes of calls and data. They eventually are the first to accept and embrace data mining for improved functionality and efficiency. For example, call drops and network failures are major concerns for telecom operators. However, with AI, anomaly detection algorithms, and prediction engines in place, they are now able to detect, predict, and avoid network failures even before they happen, ensuring uninterrupted service for their users. This data can be quite useful for monitoring user call behavior and boosting marketing forecasting initiatives. The prediction engine provides information about telecom consumers, including – when a user is likely to switch to another telecom provider from the current one, which consumer loyalty programs are the most effective at attracting users (enhances customer loyalty programs), and which users are most likely to fall victim to cloning fraud – helps with improved customer retention management and fraud detection.
Data mining has been powering global telecom operators to function and scale up better by optimizing their network infrastructures and improving various other functions such as management, marketing, and customer service. It helps in efficient marketing/customer profiling, reducing calling fees, identifying fraud detection, reducing customer churn, and improving customer loyalty programs.
- Banking – Banking institutions gather massive amounts of customer data – data related to credit and debit cards, online transactions, payment details, and other customer information. Data mining when powered with advanced analytics, AI, and ML help in tracking customer behavior, their habits, and their online interaction activity with banking apps and sites. They can segment, profile the customers, propose to and recommend the right product or service, and even upsell/cross-sell through the power of data mining’s predictive and recommender engines and advanced analytics.
Data mining facilitates tracking the user’s mode of banking transactions – online, net banking, debit card, or credit card and identifies fraud detection, risk assessment, and captures customer feedback.
- E-commerce/Retail – Data mining along with ML, real-time analytics, and predictive engines provide E-tailers with useful insights into a customer’s journey. The possibilities here are immense – the cleansed data helps retail stores in tracking and selecting the best aisles for product display to appease customers, track incoming foot traffic, select appropriate best retail locations, observe purchasing behavior, recommend related products, cross-sell/upsell products, and assess customer lifetime value. E-commerce companies are now tracking and analyzing social media information for effective feedback to improve their services and provide better user experiences to their customers. They know what a user is searching for, on which social channels the user hangs out quite regularly, how and when to engage with the user and propose their product, etc. – they have the complete information.
- Finance & Insurance – Most financial companies rely on data mining to succeed and thrive. Here, data mining and ML can identify dissimilar patterns from identical data set groups to identify where the excessive money has been spent (to reduce operational costs), increase revenue, and predict financial markets, growth, and trends (via predictive engines) to target and propose the right product to the right customer. Big financial corporations invest heavily in gathering the best business intelligence software to gain deeper insights and competitive intelligence.
Similarly, the insurance industry is investing heavily in data mining, ML, and BI systems to identify and manage risks and detect fraud. For instance, let us imagine, an insurance company’s client base if one of the company’s clients has a gap in insurance coverage, the analytics system will immediately send a notification to the company’s sales team, which eventually helps them bring added value to their client.
- Healthcare & Medicine – Predictive engines effectuate prescribing the right medicines and further track, measure and evaluate the treatment progress. Similarly, it can track unusual dissimilar patterns, incoherent prescriptions, and fraudulent practices that might happen in medical claims. Further, it enables the assessment of patient medical record information and displays potential treatment options backed by insights and serves as evidence to a doctor for better treatment of the patient. Data mining can help in diagnosis accuracy via image analytics and computer vision, increase patient experience, patient safety, and hospital management improvement.
In genomics’ sequencing study and analysis –data mining can help track dissimilar patterns in the genome, powered by analytics information it can help bring the developed drug faster to the market.
- Supply chain – Globally, supply chain companies are powering and optimizing their supply chain processes across all touch points including -warehouses, transport systems, invoicing processing systems, and logistics by leveraging data mining capabilities. Primarily helping them in balancing the demand-supply equation, supply chains are increasingly using data to improvise and modernize their distribution channels. Data mining combined with AI, ML, geotagging, sensors, RFID, geofencing, and advanced analytics is taking the supply chain ecosystem including cold storage systems to the next level. It opens new possibilities and improves operational efficiency via delivering goods on time, tracking the condition of products, finding the best-optimized routes, ensuring driver/product safety in transit, and improving the lead time.
Data mining, big data analytics along with AI, ML, RPA, edge analytics, and blockchain will continue to disrupt organizations the world over, impacting and improving our lives in a big way. Organizations globally are realizing the rising importance of data and are trying to adopt data culture into their organizational fabric. While data becomes the basis, data mining provides the context. Businesses today, irrespective of the vertical can improve their business efficiency, and increase customer engagement and sales by taking advantage of insights provided by data and data mining applications.
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