Data Mining Made Easy with InetSoft

Cutting costs and increasing revenue is an established goal for all businesses. In order to accomplish this, data must be collected and then analyzed for any readjustments that must be made.That's where data mining comes in handy. So the question is what is data mining and how can it help?

Data mining is the process of finding patterns, trends, and relationships by interpreting raw data into meaningful information. This information can then be used effectively to make key decisions and/or predictions on future sales, inventory levels, customer response, etc.

'Mining' data requires sifting through a diverse field of relational databases for market research, report creation, and report analysis. This in turn can be used to sort and summarize any relations that are derived.

Included in InetSoft's award-winning dashboard solutions and reporting tools are our unique data mining tools which help users effectively analyze data sets. One key feature is our 'brushing' tool, a unique data exploration tool employed only by InetSoft, which allows users to select specific data and drill down by allowing them to have a closer and precise analysis on the chosen data set.

#1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index Read More

Visualize Free

InetSoft offers this web-based application for users seeking to test out the interface and features of a visual analysis tool. While the full potential of the software is not accessible, it does allow users to upload, create an interactive dashboard, and analyze using own data set and dashboard features.

Running a trial and evaluating Visualize Free will help users digest how easy it is to use our dashboard and reporting software.

More importantly, users will get an idea of how powerful our 'brushing', along with other tools, are when data mining. It's the perfect tool when working with multi-dimensional data to spot any significant patterns.

Why hesitate? Go and try Visualize Free, a free visual analysis tool, to explore and present data that standard office software cannot handle.

Read what InetSoft customers and partners have said about their selection of Style Scope for their solution for dashboard reporting.

Good Strategies for Data Mining

  • Make sure all data sets that are being used are compatible, easy to use, and organized. Consistency and organization help create an effective and efficient process throughout.

  • Key attributes in the data help ease the process. Look over entire data set and make sure to record any relevant attributes that haven't been included. Missing key components limits the accuracy of resulting predictions.

  • Unnecessary data and/or errors in the data can exist. Perform a thorough audit of the data to omit any fruitless information.

  • Quality over quantity. Having quality data is more beneficial than having a large set of data. Large amounts of data can elongate the process as a whole.

  • Understanding the data. Make sure you or someone looking at the data is an expert. A thorough understanding of the data helps identify relevant attributes, patterns, and relationships.

  • Plan, organize, and follow through. Because data mining can become a complicated process, it is crucial to plan beforehand, stay organized throughout, and execute the plan.

  • Mining in an actual field requires miners to have a road map that helps define and guide them through any critical issues and makes sure all important points are addressed. Without a map laid out beforehand, miners are bound to get lost in the digging process.

Best Practices When Data Mining

  • Don't be lazy. Take time to learn and comprehend the data instead of solely relying on a piece of software to do all the work.

  • Know what you're looking for and trying to figure out. Having a goal will allow you to focus on that goal until the end. Applying a software to find irrelevant data would be considered a waste of time

  • Learn to familiarize yourself with effective processes that incorporate similar data through any data mining process. This way allows for easier implementation.

  • Constantly re-evaluate existing processes and models.