Home » Understanding Data Management And Data Mining

Understanding Data Management And Data Mining

In the world of business, it is easy to believe that data mining has some magic power to transform the way that a company runs its business. How can that be so?

The truth is that not all companies use the right technology or business strategy to achieve success. When you hear the term “data management,” you might think that everything you do is stored in a database.

But, all that file is just data that you have already stored on the hard drive of your computer. Software analyzes and processes data sets to improve the way you can interact with customers. With several tools at your disposal, you can analyze and understand why some salespeople are struggling, and if you need to make changes.

What is Data Mining?

Data mining is when companies use non-sensical data, process it, and convert it into useful information. Businesses can use new and quality data to understand customer behaviors, market their products effectively, and so much more.

Data mining can’t happen without the effective collection of data, proper data warehousing, and processing of the data.

How Data Mining Works?

As mentioned above, in data mining you analyze large sets of random data and look for meaningful and valuable patterns of information from it.

Data mining is used in a lot of ways. The most common applications of data mining today include:

  • Database marketing
  • Credit risk management
  • Fraud detection
  • Spam email filtering and more.

Data Mining processes can be broken down into five steps. The first part of the process is for organizations to collect data from a number of sources. Then they store the data in the data warehouse. Next up, they store and manage data, and they can use this data either on the cloud or in their local data warehouse.

In the next step, teams of business analysts, managers, and IT professionals access the collected data and come up with how to organize this data. Then, the software sorts the data based on the user’s results. In the end, the sorted data is then shared with teams that can make use of this quality data to enhance business processes.

Techniques of Data Mining

Data mining software relies on various algorithms and techniques to convert large data sets into final data that businesses can use to grow themselves. There are multiple data mining techniques, including:

1. Association Rules

This data mining technique is also referred to as Market Basket analysis. It searches for relationships between variables. This relationship in itself creates additional value within the data set as it aims to link other pieces of data.

The best example of this is that software will search a company’s sales history to see which products are most commonly purchased together. This way, companies can plan, promote, and forecast accordingly.

2. Classification

Classification data mining uses predefined classes to sort objects into. The purpose of this classification is to describe the characteristics of items. It also represents what the data sets have in common with one another. This data mining technique allows for better categorization of data. Then you can summarize the data with the features they have or based on the product lines.

3. Clustering

Data clustering is pretty similar to data classification. But in this data mining technique, the software sorts products together based on what makes them different than other items. In classification, items may be sorted as shampoo, conditioner, soap, and toothpaste, while clustering may sort items as “hair care” and “dental health.”

4. Decision Trees

Decision trees are used to classify and predict any outcome based on the list of decisions. A decision tree is used to ask for input on a series of cascading questions that sort datasets based on the responses received.

5. K-Nearest Neighbor (KNN)

KNN is an algorithm that classifies data based on its proximity to the other data. The core of KNN is rooted in assuming that data points are close to each other or they’re more similar to each other than other bits of data.

6. Neural Networks

This type of machine learning processes data using nodes. These nodes are made up of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected.

7. Predictive

Predictive analysis of data mining tries to leverage historical information to build graphical or mathematical models to try and predict future outcomes. The goal of this data mining process is to support an unknown figure in the future based on current data.

What is Data Management?

Data management is actually about integrating all the different sources of information into one system. It could include many various tools, like spreadsheets, Google, or Microsoft Active Directory. Some businesses are only able to manage their database using Oracle, but most companies find that using a comprehensive data management solution is the best choice. Of course, using an ERP system will give you the power to see and work with all the essential files in a single view.

Today, companies and institutions use files to understand the trends, their sources, and their implications. Data is collected for analysis, making it possible to keep track of critical data that can be used to make crucial decisions.

These days, companies often have large numbers of systems running on different platforms. One may run on a Linux distribution, another on Windows, and another on an ERP system. What if you want to change something in your existing software?

Types of Data Management

Managing your data is a tough job, and it impacts every single part of your business. Data management may include daily tasks, creating policies, maintaining processes, and more. So, regardless of what you’re researching, there will be many types of data management that you will come across.

  • Data integration
  • Data modeling
  • Data storage
  • Data catalogs
  • Data processing
  • Data governance
  • Data lifecycle management
  • Data Pipelines
  • Data Security
  • Data Architecture

Future of Data Management

Data management will be the tool to make that change. You can explore all the possibilities by using one of the many data management tools that are available. You can manage data with spreadsheet programs and advanced query packages. These tools will allow you to merge data from different sources and then show which ones are relevant to your organization.

If you have trouble interpreting the information, you can use tools like Oracle DB, which has built-in techniques to make this work.

Data management is also key to the data mining process. With this data, you can examine whether a new product or service can change the way the company does business.

Data management will help you spot trends that can be used to improve the way you manage your business. So, if you want to improve the quality of your staff and improve the way you interact with your customers, these tools are a must.

Of course, the more data you have, the more critical it is to be able to manage it well. Many companies are not good enough to have the right amount of data. So, with professional data management software, you can quickly get the right tools for your business.

Back to top