However, your competitors see similar data and trends, so without the ability to analyze data quickly, that opportunity may vanish just as fast.

Unfortunately, traditional business intelligence solutions do not generally enable such speed.

Think about the typical process for someone looking for data. A manager would ask the IT or data team to create a dashboard to track and monitors KPIs. There’s a waiting period, depending on how many projects the IT team has. When the dashboard is complete, in many cases, the manager sees they forgot to request certain data points, so it gets sent back for more precise creation. Then, there’s another waiting period.

In some cases, this can happen two or three times. Along the way, everybody gets frustrated and in the end, the dashboard may function properly but not provide the real data the manager needs. So, even after a week or two, the dashboard created doesn’t produce the expected insights or show correlations that impact decisions. In the meantime, it may be too late to gain an advantage.

Often, dashboards don’t foster data exploration. Even though they may provide real-time data visualizations, there is no easy way to explore. In most cases, the best insights come from manipulating the data and introducing new variables. Yet, traditional BI solutions do not enable such activity without another trip to the IT team.

There is another way.

Several key features make IDA more powerful than other BI solutions.

Maybe you find that there is a higher number of defects coming from a particular factory or with a particular part. You can then zero in on the root cause. However, IDA gives you the tools to go much deeper. For example, what happens if you shut down a particular line and increase production on another line to maintain volume? You can then judge the impact on costs, wear and tear on equipment, and how that will impact overall defects and the bottom line.

You might also find that a particular part from a certain supplier produces more defects. So, you can introduce new data on the fly, such as the cost of switching suppliers, to predict future outcomes.

In another scenario, you might find that defect rates are higher in coastal cities. For example, vehicles might rust more quickly due to constant exposure to salt air. You can see the impact of adding additional rustproofing protection at the factory and how that might change profit margins and reduce defects.

Endless what-if scenarios let you manipulate all the variables that are part of producing defects to find optimal solutions.