What is Data Mining?

Data mining is the process of discovering patterns and mining key information from large data sets through the use of machine learning algorithms. Gartner defines it as the “process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories; [it] employs pattern recognition technologies, as well as statistical and mathematical techniques.”

Another term often correlated with data mining is “knowledge discovery in data (KDD).” It is the process of identifying patterns and valuable information in massive datasets, enabling organizations to make better business decisions.

Learn More: What is Process Mining?

Data Mining Process

The end-to-end process of data mining can be broken down into these steps:

  1. Defining the business problem: Data scientists and business stakeholders identify the key business challenges. This is crucial for informing the criteria for a specific project. If the organization works with consultants and external analysts, it will require additional information to understand and evaluate the business problem effectively.
  2. Data preparation: This phase involves a number of processes to prepare data for mining. It begins with data exploration, pre-processing and data cleansing to correct errors and other data quality challenges.
  3. Mining patterns: Based on the problem, the team collects relevant datasets and mines patterns such as correlations or association rules to understand the problem at hand. Machine learning algorithms are applied to discover similarities or patterns.
  4. Analysis and visualization: Data visualization and data storytelling is a key part of the process of evaluating and understanding the problem. After the data analysis is carried out, the team builds dashboards with BI tools to further evaluate and interpret the results.

Based on the findings of data mining, the teams implement strategies and actions to achieve the intended business goals.

Learn More: What is Process Automation?

Uses of Data Mining

Data mining is leveraged to improve processes with a data-driven approach, drive business value and enhance growth. Some of the use cases are:

  1.  Target and acquire new customers: Through data mining, teams can gain a granular understanding of customer preferences and behavior, thereby enabling them to develop more focused strategies. Additionally, sales teams tap into insights from data mining to increase lead conversions and introduce new products and services to customers.
  2. Manage supply chains: Organizations can accurately identify industry trends and predict product demand, allowing them to effectively manage their product inventories and supplies. Data mining is also used by supply chain managers to improve storage, delivery, as well as other logistics operations. Similarly, in large industrial operations, data mining insights from sensors in manufacturing equipment and machinery facilitate predictive maintenance.
  3. Find and eliminate process inefficiencies: Data mining in an organization reveals how exactly processes can be improved. For instance, it shows process gaps, inefficiencies, and the need for compliance with baselined SOPs. Through this data, teams can find and eliminate inefficiencies.

Learn More: What is Process Intelligence?

Getting Started with Data Mining

Looking to deploy data mining for end-to-end process discovery, task mining, and process automation, then kickstart your journey with ScoutTM platform. Here’s why: over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record. This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises.

Traditional solutions for identifying and building automation pipelines have significant privacy, accuracy, and scalability issues and fail to capture work done outside the systems of record, such as ERPs and CRMs.

Through Soroco ScoutTM, powered by the world’s first work graph platform, organizations can unlock this data source to discover processes where inefficiencies occur and are prime candidates for intelligent process automation.

Request a demo of Soroco ScoutTM today!    

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