We have a saying in the world of data analysis - "garbage in, garbage out." In other words, if you're not putting good quality data into your reports, then the reports themselves can't be too reliable.
In this section, we will discuss 5 Steps for Effective Analytics & Data Preparation.
Data preparation is the process of transforming raw data into a format that can be consumed by an analytics engine.
Data preparation is often the most time-consuming step in the analytics pipeline, but it doesn't need to be. Data cleaning and formatting can be automated with machine learning, and advanced algorithms are making it easier for analysts to get insights out of raw data.
Data transformation is the process of transforming data from one format to another, and it could be useful for a number of reasons.
Transforming data may be necessary to extract meaning and understanding from the data because the formats are not easily readable or understandable. Data can also be transformed to make sure that it is compatible with other systems or applications, making it easier to use and share. Finally, transformations could be used as an intermediate step before loading data into a database for storage.
How well-designed transformations will depend on the format of the input and output, as well as on how much processing is required during transformation.
Data Analysis: Key steps for data analysis
1. Annotate and code your data
2. Explore and summarize your data
3. Create graphs and charts to visualize the data
4. Formulate and test hypotheses
5. Draw conclusions
Data Visualization: What is Data Visualization
Data visualization is the process of representing data in a graphical way. Data visualization can be used for many purposes such as outcome assessment, visual communication, and exploration. It has been seen to be more intuitive than other forms of data representation because it conveys both statistical information and its context.
One of the common applications for Data Visualization is in interactive dashboards where users can explore trends or patterns in different datasets at their own pace. These are particularly useful when there are huge amounts of data that need to be analyzed for insights. It is always more effective for decision makers rather looking at list of numbers than graphical meaningful and beautiful dashboard with lucrative charts and graphs.
Data Communication: Effective data communication
In this section, we will be discussing the importance of data communication. We will look at the significance of data communication in a variety of different scenarios.
We communicate with data every day, but it's not always clear what that means or how to go about it. Data is any kind of information that can be processed by a computer. It can take many different formats and be used for a number of applications, from the old-fashioned bar graph showing quarterly profits to a complex digital map showing traffic congestion to an online application where users can see real-time information on whether their local post office is open or closed. Ultimate goal of data communication is to convert data into information and accessible that when and where needed can be found and accessible that business owners, leaders, employees and decision makers can depend on it and take decision based on that.
How to Create a Beautiful Visual Story from Your Business Intelligence Data
Let's focus on how to create a beautiful visual story from your business intelligence data in core three steps.
The first thing to do is find out what kind of story you want to tell. The second thing is to figure out the layout and style of your visual story. The third thing would be gather all the data and information you need and then start analyzing and exploring it. After that, you can start visualizing and creating graphics for your visuals story. Lastly, you can share your visual story with others!
ARS Analytics is a Data Analytics consulting company, we work with business data and convert data to meaningful and beautiful.