Data analytics is an expansive term that encompasses a number of disciplines, but generally data analysts serve the valuable role of evaluating and interpreting raw data to help provide meaningful insights for organizations and their customers. If you’re interested, a related field called data science can take data interpretation to new levels by designing custom and advanced processes and techniques.
For the purposes of this post, however, we’ll be taking a brief look at data mining, data manipulation, and data modeling. At their core, each one of these related tasks is used in conjunction to help tell stories. But what makes data mining, data manipulation, and data modeling different? It’s an important question.
Knowing the difference can help you better focus your efforts. Especially if you’re learning a relevant programming language like Python at Devmountain and developing critical thinking skills to manage your emerging tool set and communicate your data analysis and findings to your team or client. (Read also: 3 Data Analytics Skills You Can Use in Your Career.)
Here are the basics of what you need to know to better understand these three parts of data analytics.
What Is Data Mining?
Data mining is the process of looking for patterns in datasets to predict what one or multiple outcomes might be. As an analyst, if you can find an anomaly in a known pattern, then you can potentially figure out what caused the pattern to break. In a business, this information can be useful for predicting disruptions and changes in sales and product processes among others.
Partial or fully automated software or scripts can be used to find previously unknown patterns. Since data mining can be used for vast quantities of data, and machines can be trained to look for patterns without fatiguing, it makes sense for the mining process to be run by high-performance tools. Once the data or patterns have been mined, then an analyst can interpret the results.
What Is Data Manipulation?
Data manipulation is exactly what it sounds like. An analyst gets a database and then runs a program or uses a data manipulation language to modify it. Automatically adding, deleting, and otherwise modifying data is not only useful but necessary when dealing with large databases.
An analyst can use data manipulation to remove unwanted or irrelevant data from a database before, during, or after modeling or mining. When a development cycle calls for continued maintenance, data manipulation can be helpful in making sure up-to-date information is available in the database used by the software application and therefore end user.
What Is Data Modeling?
Data modeling is about organization. When you create a data model, you take different sets of data and organize them. By doing this, you show how data relates to each other. This is a useful skill for a data analyst to know because you need to be able to clearly show what’s happening, otherwise computers and people won’t know how to read the data. If data is unorganized (or without a model), then it can be hard to transfer and understand.
There are different ways to model data depending on what your goals are. At a high level, these include conceptual, logical, and physical instances. Conceptual is where you can start when organizing data and this level can help show what the scope of the data is. On the logical level, you describe the structure of the data, which may overlap with the conceptual instance. The physical instance allows you to detail how the data is stored, such as in partitions.