Data Mining Windows Desktop Application
Once you have information in a database you may perform a function called “data mining” to analyze stored information and derive new information. Data mining, also known as knowledge discovery in data (KDD) takes larger sets of raw data, and detects patterns that can be extracted into new datasets. Data mining has its foundation in a number of data analysis domains including machine learning and statistics. Datasets derived from data mining can be critical in business intelligence gathering, where a solid understanding of information is necessary for good decision making.
As an individual user you might find the process of data mining to be quite daunting. Typically, data mining is used by larger organizations like banks, to understand their market risks, deduct fraud and better understand their competitive landscape. However, individuals can also benefit from data mining. For example, as a small business owner you might detect that certain products get used up faster during the holidays. This simple observation is a form of data mining, and could be used to help you better manage your inventory throughout the year.
Our desktop Tracker Ten database can be used to perform simple data mining tasks through the use of integrated reporting and chart-based data visualization.
Why is Data Mining Important for a Small Business Owner?
Any business can use data mining to turn raw data into useful insights. Whenever you deduct a pattern in your data, this information can help you make better decisions. For example, if you are using our Tracker Ten for Equipment software, you may find that service costs for a piece of equipment always increase after the 5-year mark, after you generate a report that shows you maintenance cost by year. This information is a form of data mining, and it could help you to decide when you should replace your heavy equipment.
Alternatively, you might be using our Tracker Ten for Customers software to keep track of your customer service purchases. After generating reports, you might find that your customers always order a set of services together. This may lead you to offer new service bundles, that help you to increase your business.
As you can see, even if you are a small business, data mining techniques can help you make better decisions and help you to provide better services to your current and future customers.
Types of Data Mining
There are 5 main types of data mining: classification, anomaly detection, regression analysis, association and clustering. Don’t let these terms scare you! They all refer to simple concepts as described below.
The first type of data mining technique is classification. Classification is used to assign your data to common categories. For example, if you are tracking books, using our Tracker Ten for Libraries program, a simple classification might be between fiction and non fiction. Then if you study book circulation history, you might find that non fiction books are much more popular then fiction books, and you’ll know to increase your inventory of non fiction books. All of our Tracker Ten products support nested classifications of records.
The second type of data mining is anomaly detection. Using this mechanism, you can find data points that are unusually high or unusually low. An example at a larger organization might be bank fraud detection. If an account all of sudden has a number of high value purchases, this would be anomalous and worthy of further investigation. For a more specialized example, if you are using our Tracker Ten for Equipment program, you might find that a particular equipment manufacturer has outlier service costs – i.e., much higher then equipment from other manufacturers. In this case you will know to avoid equipment purchases from this manufacturer in the future.
The third type of data mining is regression analysis. This simply means that one field in your database seems to have a relationship with another field in your database. A large-scale example might be the inflation rate in your country being related to the interest rate. With lower interest rates, you might find higher inflation rates and vice versa. An example that might apply to your business is more expensive products may have less service costs. This observation may lead you to realize that purchasing more expensive products can pay off in the long run, as the overall cost of the product through its lifecycle is less, even if the initial purchase price is higher.
The fourth type of data mining is association. This type of minding detects when values in one set of fields always seem to correspond to values in another set of fields. For example, if you are using our Tracker Ten for Medical Equipment software you might find that reliable X-Ray equipment always has certain features. Accordingly, in the future when you are looking for new X-Ray equipment you will know which features you should look for.
The final type of data mining is clustering. Clustering is simply the process of grouping similar items together. If you are using our “Tracker Ten for People” program to track volunteers, you might cluster people in the same age group together to determine what activities they are best suited for. Or if you are using our “Tracker Ten for Customers” program you might find that certain customers are more likely to buy certain products. For example, new mom and dads might purchase diapers. Therefore, you may want to send new parents offers for diapers, increase your sales.
While all of these data mining types might be described using complex technical terms, you can see that they apply to everyday situations that might be facing your business or organization. Effective use of the derived data can save you time and money, and help you to increase end user and customer satisfaction.
Ensuring Data Mining Effectiveness
To ensure that your data mining efforts pay dividends, you should make sure that your data is “clean”. This simply means that your data has been validated and is free from errors and duplicates. If your database does have a lot of errors, the insights derived from the data will be suspect.
You also want to make sure that your data mining techniques work in the real world. Before you rely on a data mining technique, make sure that the results you are seeing actually match what’s really happening. For example, you might notice an uptick in sales on a particular day. But that doesn’t mean that sales will always increase on that day, if there was another cause for the surge like a marketing campaign.
Data Mining and Visualization
It’s not always easy to see patterns and detect issues in rows of raw data. Therefore, it is often useful to take the data and convert it into visual representations. By looking at numbers on a graph, you can much more easily see patterns that may require further analysis. All of our Tracker Ten products feature charting tools that can show you numeric data in graphs.