In today’s highly technological world, big data holds a lot of importance. Apart from being enormous when it comes to Volume, high Velocity, a big Variety, Veracity, and/or Value are characteristic. Together they form the five V’s of big data. Companies and businesses don’t just use this data for their day-to-day processes but also to gather insights and to make better future decisions. Two crucial concepts for making the most out of your data are data management and visualization.
Data management refers to the collection and organization of data and then using it efficiently and in a secure way. Data management’s primary goal is helping companies and people with the usage of data while ensuring proper usage whether it be for analytics, making future plans, or other applications.
Some examples of key strategies that are indispensable for data management include proper access management (who can create, access and/or change which data), data modeling, and data governance to ensure data consistency and quality.
Once proper data management is in place we can focus more on improving the way we present the data so it’s easier to extract meaning and value. This brings us to the topic of data visualisations which basically means changing the information present in the data to a visual format.
When we talk about data visualisation graphs first come to mind, but other graphical representations of data like maps and some flowcharts belong here. Visualised data can be understood by humans so much more efficiently than its raw counterpart as it summarizes the massive data chunks so that we can efficiently identify problems, find hidden patterns, and discover new opportunities.
Data management and visualization aren’t limited to the business floor: People keep track of their finances and get their health related parameters from their smart watch presented in a visually pleasing way.
These personal applications are however not as challenging as data management and visualization with big data. First and foremost we need the people with the proper skills to manage and visually represent the data.
Moreover, you would also have to involve other departments in the process, especially the IT department, because working with such vast chunks of data will challenge your computer hardware and the right force is required to operate all of it too. In the short term this will often be perceived more as a cost than a gain.
Lastly, it is also essential that the data is accurate, and for that you would need a lot of quality assurance. Keep in mind that the quality of your data always determines the quality of any subsequent analysis on or visualization of this data.
Data management and visualization have so much importance due to several reasons. To begin with, people can make faster and better decisions and quickly derive insights. Moreover, they also get a good understanding of what their next move should be.
Similarly, through data visualization, organization, and management, data sharing also becomes very easy. You can easily spread the data around so other people working with you can also benefit from it. It also further decreases your chances of making mistakes since you get to see what you’ve done in the past and its effects.
Data management and visualization is also one of the most critical steps in the business intelligence process as it takes the raw information and gives it a specific shape and model that people without the same skill can easily understand.
With the increasing need for data visualization, full-fledged software that takes raw data and converts it into visually understandable forms becomes more prominent. This in turn however increases the importance of data management even more.
The insights we’re looking for can be related to sales, marketing efforts, or even simple customer satisfaction and dissatisfaction. Whatever the insights may be needed for, the need for data visualization will arise. Stakeholders and business owners also make a lot of data visualization use while presenting statistics about their sales, businesses, or if they’re offering a particular idea.
Data management and visualization can and is also needed in cost-benefit analysis. Since this is mostly financial, visualizing those numbers can help the non-finance people understand the data presented in the cost-benefit analysis.
What’s great is that it can be used for any type of industry or project, which shows how limitless the bounds of data visualization and management actually are.
This section will look at different ways of how data management and visualization can be and is used. Traditionally Microsoft Excel has been popular for creating visualization in the business world but it does come with its limitations especially with big data. While hard-core data scientists will go for either R or Python to create their visualizations there are many much more accessible ways available beyond Excel, like for example Power BI, Qlik, or Tableau.
Some common uses of visualization are:
When doing process analysis the source data is a concatenation of processes that are common to your business. Using process mining the underlying sequences between these processes can be discovered. By visualizing this result it becomes clear where the prescribed rules are being followed versus when subsequent processes are out of line.
Market Research & Analysis:
This is another great example and use of data management and visualization. Business owners need insights and data about their customers, their behaviors, demographics, and all that research can be shown through data visualization.
Calculating Value & Risks:
If you look at a plain piece of information, you will hardly be able to pinpoint any value or risks mentioned in it. But if you visualize that data even by only color-coding, the values differ from the risks; the readers will immediately know what the data is representing or visualizing.
Now we will list down some of the examples as to how data management and visualization actually presents the things mentioned above:
Scatter plots, also known as scatter diagrams, are used to present a relationship between two continuous variables.
This is one of the most basic data visualization examples where lines are used to display the changes in a variable over time.
Bar charts, as the name suggests, make use of bars to represent data. The bars represent different values on a discrete variable which allows you to see their occurrence and to compare their relative presence.
If one of your variables is a geographical region it is tempting to visualize it on a map instead of the more traditional bar chart. While visually much more appealing it will not always increase the readability, so take this into account when considering using maps.
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