5 Data Mining Steps
Data Mining is a process that requires many steps. While many companies have data, not all of them are usable or necessary for the particular project objective involved. Here’s are the 5 Steps to Data Mining that you should know about:
Project Goal Setting
For anything to succeed, it has to have a plan. Goal setting is the foundation of every successful data mining project. Through aligning on their project objectives and timelines, business and data mining teams can have a smoother working relationship throughout the experience.
Goal setting allows teams to assign roles and make a clear plan to move forward. Expectation management is key to avoiding issues throughout the data mining process.
Data Gathering & Preparation
For every good kind of data, there is a mountain of bad data. From incomplete, fraudulent to out of date, bad data is everywhere. When not cleaned, it can ruin any campaign. The data gathering and preparation stage is all about making sure that the data is usable.
For larger, more established clients, there must be mitigation of security risk. Trust is a necessary element when dealing with sensitive information. Data processing often uses modern database management systems (DBMS) to improve data mining speed. It is also a primary precaution when dealing with data that is confidential to an organization.
Read about our Bad Data Series, what to avoid and how to provide clean data for your CRM
Data Modeling
With the use of mathematical models and various data visualization tools, there are meaningful patterns discovered in the data. Through conceptual representations of how data objects and rules go hand in hand, they form a Database.
A Database can be conceptual, physical, or logical, depending on the Data Model applied. With the right structure, it can help define relational tables, keys, and procedures. For Data Modeling to work, it needs to have quality data, security procedures, consistent semantics, default values, and naming conventions. There are two types of Data Modeling Techniques: Entity-Relationship (E-R) Model & Unified Modeling Language (UML).
Data Analysis
After the modeled data is analyzed, it is then extracted, transformed, and visualized. Data analysis helps bring together useful information to give insights or test hypotheses.
With a combination of business intelligence and analytics models, Data Analysis orders raw data in a way that is relevant to the project goals. Armed with visual representations and insight on previously unrefined data, it is then ready for deployment towards relevant business units.
Deployment
In the last stage of Data Mining, relevant partners test the hypothesis. There are four different types of model deployment: data science tools, programming language, database, and SQL script or predictive model markup language.
Mined data provides a single source of truth that can guide business decisions moving forward.With coordination between data scientists, IT teams, software developments, and business professionals work together to integrate the new models with the existing production system of an organization. Companies such as Hey DAN are experience and well organized in handling professional data mining.
