In the face of the new global economy, businesses must use every tool at their disposal to gain competitive advantage. As the internet and other technological advances have increased the amount of data, companies have taken advantage of techniques for acquiring and analyzing the information. Third party data providers have helped companies mine data for important marketing data sets.
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Business intelligence software providers have created the tools for sifting through the mountains of data available to modern companies. The core of these tools lies with the charting and graphing tools that take advantage of natural human cognitive processes to analyze large data sets quickly. Here are seven steps to take to use visualizations to their greatest advantage.
- Create data hierarchies – data from data warehouses or from data mining activities will generally by organized in a hierarchy. One example is customer data. Often, the data point will be identified with a unique key such as the customer name or social security. Then the associated parameters are attached to that unique key, including address, age, buying habits, etc. Subordinate data can also be hierarchical. For instance, buying frequency could include buying frequency for food and buying frequency for entertainment electronics. Analysts should massage the hierarchies to order the data in an appropriate way for their own analysis.
- Match the visualization to the data – choosing the chart type for the type of data and the type of analysis in a simple, clear fashion is key to data exploration. Business intelligence software can make recommendations. Some general strategies are to use columns or bar charts to perform data set comparisons, line or area charts to identify data trends and cycles over time, and pie charts or waterfall charts to determine how aggregate data breaks down by component. There are many more options available for all sorts of applications.
- Analyze the data distribution – histograms and similar tools can help determine the distribution of data. This will help determine if the data follows a regular Gaussian, or if it is skewed, polar, or tail-heavy. This will also help determine outliers.
- Identify relationships between the data sets – visualizations of scatter plots between two data sets can readily uncover relationships. Clusters of data points can identify various classifications. If the points form a general line or a curve, then regression may be used to plot the line or curve, which can be used to fill in points using interpolation. Regression will also be able to predict how well the line or curve fits the points. Databases with more than two data sets can be analyzed two sets at a time in this fashion.
- Use extrapolation to forecast future data action – regression predictions can produce formulas that predict points that lay outside the sample boundaries. For instance, if a manager plots paint drying times against temperatures between 60 degrees and 70 degrees, they can then use extrapolation can make an estimate of drying time at 75 degrees, and tell how likely the actual value will match the predicted value.
- Create reports and dashboards – as an analyst goes through this process, he or she will determine which data is important and which is not. This helps the analyst determine which parameters are indicators for key business processes, and create a dashboard to monitor these indicators in real time. Analysts can create reports that detail the important data relationships in a simple and convincing form to present to higher management.
- Publish visualization results and conclusions – business intelligence software can automate the task of publishing these results in a form suitable for the internet, or for any type of mobile device.
Although data exploration on large databases can seem daunting, business intelligence software can help analysts use visualizations to identify key data points and relationships. In this way, they can help their company with information to improve their business decisions.