Explaining Data Visualizations aand Data Analysis to Business Owners

The purpose of enterprise database management is to analyze and visualize data for strategic business decision-making. The focus of these data visualization projects focuses on two major purposes: explanation and inspiration.  Data visualizations and data analysis to business owners serve as a channel to shed light on really complex problems.

Describing Data Visualizations and Data Analysis to Business Owners

Let us further explore the major data visualization categories in terms of their intended and subsidiary purposes.

1. Inspirational – As the name suggests, the major goal of this analysis mode is to inspire people. But this cannot be just done at a superficial level but to engage intelligent people into a deeper level of thinking and sensing the inner beauty. Easy visualization of data has an incredible power to attract people and draw abstract concepts more tangibly.

2. Explanatory – The major goal of explanatory data visualization is to use the graphics as a mode to explain complex ideas, processes, and phenomena. This is another great area where the graphical representation of data comes in handy. Humans are visual creatures, and a picture can replace a thousand words. As we can see, data journalism had flourished over the years and helped explain the most complex things with the help of interpreted data.

3. Analytical – The major objective of analytical data visualization is to extract the information from the given data to answer the questions and understand the patterns. The previous type of explanatory visualization is also to help people understand some patterns from the data. The major difference in terms of visualization is that the author already knows the basic idea to be visualized with the help of data. However, in the analysis model, visualization is basically to understand what the data implies from scratch.

There are various words used to imply these visualization aspects. To be more specific, these parts of data science are called “Exploratory Data Analysis,” a term in practice for a decade now. For all database-related activities, you can get the assistance of expert providers like RemoteDBA.

Why data analysis?

As per the expert view, data analysis is a human-technological activity that can help people to solve many real-world problems through a scientific approach. Data analysis is very important in business to help the key decision-makers improve their knowledge about the complex business and market phenomenon and solve the most critical problems. As good, you understand the problem, the higher the chances to find an apt solution for the same.

As of late, there are plenty of important and interesting problems out there that we can try to understand better with data analysis. For example, data analysis is now used very efficiently to detect medical malpractices. It helps the practitioners make sense of millions of medical reports and reviews and medicolegal specialists to find any suspicious activities in medical practices.

So, if you have a closer look into it, data analysis is mostly oriented towards understanding something, like a problem. So, we can postulate as the major objective of data analysis is to understand something using related data. In turn, data and data-based models act as the objective description of some reality we want to unveil. Humans make a mental model of reality and then use the related data to understand the problem better.

How data analysis work?

The model of interactive data analysis functions in a loop-like fashion. The user may start with a loosely defined goal and then translate it into a question or question. In the next phase, the analyst tries to organize the data related to it and analyze it to understand some insights, leading to the answer to these questions. In the process, there may be the generation of new questions and repeatedly starting to find answers for the same as a loop. The different stage of interactive data analysis are:

• Defining the problem at hand

As we have seen above, every data analysis project has a problem statement. Be specific about what problem you are solving. What is the ultimate goal? How can it increase your understanding of the data?

• Generate more questions

A problem sometimes is too high-level to translate exactly into some analysis action. So, a problem must be first translated implicitly or explicitly to a sequence of questions for data analysis.

• Acquiring, transforming, and processing data

Some projects have some basic data available, whereas some other projects may require more data generation. In any case, all such projects may need the data analysts and scientists to be familiar with the content and meaning of the data and perform various transformations like slicing and dicing the data to familiarize themselves with it.

• Creating data models

All projects may not require this step, but many of them do. Here, experts use proven statistical models and machine learning approaches, which is useful to answer the questions by building an appropriate model. When most of the data models are aimed at prediction, some of them can be used for generating hypotheses. Some of the most common data modeling methods are clustering, simple regressions, dimensionality reduction, and different (natural language processing methods.

• Visualization of data

At this stage, data can be interpreted for an easy understanding of others. You may think of some fancy charts and graphs while thinking of this, but simple representations can be just lists and tables. This results from data transformation, which turned into something the layman eye can understand and digest.

• Interpretation of results

Once the analysis results are there in a visual format, you can interpret it to someone based on the context in which you have done the analysis. This is the most critical step, which further leads to this crucial step as the data-drive decision is mostly made out of this interpretation of results. However, you should also note that such interpretation may also be heavily influenced by pre-existing knowledge.

Finally, as a byproduct of data analysis, there may be a generation of more questions and inferences. All these analysis steps may lead to the creation of new knowledge and some additional hypothesis. This makes data analysis a very interesting and challenging activity. It brings out answers and more refined and better questions to always roll on in a loop.

In summary, the core purpose of data visualization is to help business owners identify trends. This helps in better and informed decision-making.