Business Intelligence and Analytics

Business Intelligence (BI) and analytics tools make it easier to see and analyze business data in order to make more informed decisions. Databases are intended to contain valuable information that enables you to make effective business decisions. But, if the data are hard to use or if information is hidden in spreadsheets, then all of the effort in creating a database application may be wasted.

Sometimes, all that management needs is a dashboard “red light/green light” displays or gauges that summarize the information gathered across a database. In other cases, data visualization tools, such as maps, bubble charts, and other tools with drill down and filtering capabilities provide insights where simple reporting does not.

Inputsoft experts can demonstrate the various BI tools that can work with your database and help you to decide what is needed.

Dashboards
Dashboards allows at-a-glance visualization of key performance indicators. They are designed to be highly intuitive, and provide the minimum information necessary to spur management inquiry or action. Dashboards can include simple controls to filter data, typically by geography, product or time, with a drill-down capability.


Pivot Tables
Pivot tables allow you see the big picture contained in spreadsheet-style reports. Pivot-table tools can automatically sort, count, and total the data stored in one table or spreadsheet and create a second table (called a “pivot table”) displaying the summarized data.

For example, assume that we have a spreadsheet contained a list of retail sales with a column for the date sold, another column for the store that sold it, and a third column with the sale amount. A pivot table can easily create a matrix that shows total sales by store and date.

Data Cubes – Pivot Tables on Steroids
In the pivot table example above, a matrix was created that showed total sales by store and date. Usually, there are more ways to summarize data. It might be interesting to see information by product, sales person, product type, color, discount, region, month, and so on. It would be possible to create dozens of pivot tables. But, it would be more effective to create a data cube.

In the pivot table example above, a matrix was created that showed total sales by store and date. Usually, there are more ways to summarize data. It might be interesting to see information by product, sales person, product type, color, discount, region, month, and so on. It would be possible to create dozens of pivot tables. But, it would be more effective to create a data cube.

Data Mining
Data mining is the automated process of searching of large amounts of data to find patterns and previously unrecognized relationships. The data records may be clustered into groups of similar records or a model may be created. For example, data mining could find that when people in a supermarket purchase hamburger meat, they are more likely to also purchase buns.

Data Visualization
Data visualization combines the concepts of dashboards and data cubes for very large amounts of information. Geometric shapes and maps are used to display information. Attributes such as proximity, size and color express relationships between the geometric shapes. Filters and drill down capabilities allow the data to be explored.

The goal is to make it easier for humans to grasp the big picture. Outliers and trends can be spotted quicker and more easily, which makes for more effective decisions. Often, information that was hidden by looking at detail becomes uncovered with data visualization.

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