Data Fusion

Whenever we want to understand something, we gather information about it from as many different sources as we can. This is necessary not only in order to simply have more information, but also to have a way to look at something from multiple perspectives. The fusion of these multiple perspectives is what allows us to have a deeper and better understanding of the object of our interest. This is the underlying concept behind data fusion, which is a crucial component of the data wrangling process. As such, fusion ranks among the key procedures in data analytics applied for business benefits.

What does it mean to fuse data?

As is often the case with data management and analytics procedures, there is more than one definition of data fusion available. Since data usage and technology is in constant development, it is natural that the science describing it is also never in a standstill and keeps producing various definitions.

However, these definitions that may be more or less applicable in particular situations have overlapping key features and concepts. They all recognize data fusion as the process of integrating data from multiple sources to produce a coherent view of the object that it describes which in turn allows to improve our knowledge of it.

Whether the object in question is physical, like a forest or a tropical storm as watched through multiple satellites, or conceptual, like customer behavior, fusing data on it from the different databases creates a more well-rounded informational product.

How does one go about fusing data?

What goes for definitions, applies even more for the ways to conduct data fusion. There are multiple techniques for this procedure which are all bound to change as the technology develops. Additionally, specific situations and goals may require putting a spin on any of the fusion models in order to get the best results.

However, when trying to understand the fusion process, one can start with the classical model that describes what needs to be done when fusing data. This model includes the following steps of fusion.

  • Level 0: Data assessment, also known in more technical terms as source pre-processing.
  • Level 1: Object assessment.
  • Level 2: Situation assessment.
  • Level 3: Impact assessment, which is more clearly stated as threat refinement.
  • Level 4: Resource management, or process refinement.
  • Level 5: User refinement, also known as cognitive refinement.
  • Level 6: Mission management.

These steps provide the theoretical framework by which one can understand what needs to be done when fusing data. Thus, we start with the preassessment of the data itself and the establishment of goals and end with an evaluation of the results and of the goals themselves in light of one another.

This model is still considered foundational for data fusion although liable to criticism and refinements. More importantly, it is suitable as a quick blueprint before going into this procedure for our particular business purposes.

When and why to do it?

As important as the various technical aspects of the fusion process are the reasons to do it when business data is in question. To see why we need data fusion in order to extract the full potential from our data, let us look over some examples of its beneficial use cases.

  • Increased data quality. When the data is fused together from multiple sources, it is also refined, as errors in one source are removed by contrasting it with the information in other sources. Furthermore, all the inconsistencies can thus be removed, avoiding conflicting pieces of information when company users are accessing the database.
  • Unified understanding of customer behavior. Fusing together different data points describing customer behavior allows us to understand it better. Companies track various aspects of customer behavior from website activity to purchase history and might store this information in multiple storage units. Fusing them together allows viewing customers from multiple angles at once, thus enabling well-rounded insights into factors affecting and motivating this behavior.
  • Better predicting capabilities. Having such a unified view of the various business-relevant phenomena increases the probability of making the correct predictions about the future. Thus, enabled to plan better for what lies ahead, businesses come well-prepared for it and avoid prolonged periods of turmoil.
  • Seeing the patterns. When comparing the information about the different aspects of a particular object of interest, for example, product reception in social media, analysts are able to recognize the key variables that tend to repeat under certain conditions. This helps figure out the causes of the particular outcome and what variables can be altered for desirable results in the future.

These are just a few of the benefits of the data fusion process, showcasing its importance for business analytics and the advancement of the company goals. The bottom line is that data fusion is crucial for a firm to aim at full utilization of its informational assets.


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