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Making Data Analytics Meaningful with Business Intelligence

business intelligence

You’ve got all the mechanisms and software to capture, store and analyze data about your organizational operations, your performance and so much more – but now what?

This is the common point of realization for many managers where, because they think they have all the data they need, that it automatically tells them what they want to know. Capturing data is one part of the equation, which can easily be done with the appropriate platform, yet giving that gathered data meaning is the other part of the equation.

Defining business intelligence

Business intelligence is one of those buzzwords that we often hear around in association with information technology, integrations, software solutions, and many more; but what does it mean? Business intelligence (or BI for short) describes the applications, infrastructure and tools, and best practices that enables access to and the analysis of information to optimize and improve decision-making and performance.

To be able to utilize insightful data, BI dashboards and tools make it easier to capture this data and enable analytics tools. The best part of having a dashboard system to work with your data is the ability to visually see it represented in a multitude of graphs in varying styles. Having a visual representation of numeric data not only makes it more pleasing to the eye, but also makes it more interactive and easier to understand. With more advanced BI interfaces available on the market at this time, greater functionality enables you to see the different layers of a group of data and ‘drill down’ further into it and breakdown the numeric values.

Benefits of BI solutions

Continually progressing BI solutions offering these drill-down features and greater functionality, allow to make sense and assign meaning to the data captured. This is the superpower analytics hold for organizations, and is crucial to decision-making that can actually make a difference in business operations and influence change management. However, the key to making data meaningful is to apply context to the elements you’re capturing. Context is what turns data into information.
Here are some points to help guide this realization:

  • If more data is needed to explain the context of a measurement, more information is better.
  • If unstructured data is needed to augment the context of a measurement, unstructured data is filling a gap.
  • If real-time data is needed to differentiate the context of a measurement, real-time data is required.

Also, it’s important to remember that to get the most out of the data you captured, it should be mapped to your unique process which aligns to your strategy; and not the other way around. Once you’ve first defined your strategy, then your process paths, your data will follow and have a lot more context and meaning to it than you’ve previously seen.

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