The value of data
Data collection is something virtually every business in the world does at this point. There are numerous reasons to consider collecting more and more data as a business. For one thing, data is incredibly valuable for several reasons. A few of these include:
- It is abundant – The amount of data available worldwide is abundant and readily available. It can be generated from a nearly endless list of places. The value of each piece of individual data can be debated depending on the nature of the business collecting it and what they intend to do with it.
- It is easy to transmit – Data is not a physical asset and is easy to transmit where it needs to go. It can be moved around electronically as necessary from place to place. This means that information can be shared as needed among different parties needing to review it. You can always trust that your data will be available to call upon right when necessary.
- It is not an asset from an accounting point of view – There is no need to book it as a physical asset as one might have to with other tools they use within the business. Thus, it can be kept off the books for taxable purposes.
These reasons are just the tip of the iceberg regarding why data matters to organizations. You cannot underestimate the value of the data that you have under your belt. That is why you also need to know what data modeling is and how it might be used to your advantage.
Table of Contents
What is data modeling?
Why is data modeling used?
Which data modeling techniques should Data teams prioritize?
What is data modeling?
For a simplified definition of what data modeling is, we can turn to TechTarget.com for this information:
Data modeling is the process of creating a simplified diagram of a software system and the data elements it contains, using text and symbols to represent the data and how it flows. Data models provide a blueprint for designing a new database or reengineering a legacy application.
Data modeling helps businesses develop better plans for strategically taking on the challenges they may have to face. Any given business is better served when it has more data, not less data, which is why data modeling is critical to what every organization does.
Who is in charge of data modeling?
Data managers and analysts are in charge of data modeling. They are placed in the role of modeling the data that the company has obtained into digestible and actionable information. Those who wish to become data analysts will need to understand that data modeling is a big part of the job that they have to take on.
The best data models are those with an immersive design and those models that offer something that team members can take action on. Put another way, the models need to present information in an informative way that will allow their viewers to use them to take action. It is a great way to use the company’s available data to make strategic business plans.
Why is data modeling used?
Looking into ream after ream of hard data is a quick way to make your eyes glaze over. You won’t gain the insights that you might have hoped, and you will likely end up more confused and frustrated than you were before. Data modeling can help you solve many of those problems and make data collection and analysis much more fruitful.
Let’s take a look at a scenario in which data modeling may prove its usefulness:
Your company is trying to work out how it will approach the issue of crafting a better digital marketing program for its brand. The problem is that the company is unsure which approach will make the most sense. The data collection process begins when your company sends out a survey to existing customers to ask them what they do and do not like about your brand. Your data analysts work on the data collected from those customers. What they determine from the responses given by customers is that the most significant issue seems to be that your brand has come off as too stale for the modern economy.
The analysts use data modeling to put together a presentation that lays out the issues that the data has presented to them, and they make sure that data is presented to the appropriate parties in a way that they can understand and act upon.
In this scenario, the data modeling process has done the heavy lifting, and the company has benefited significantly from using these processes to help get itself to a better place. It may be difficult to hear the truth about the company and the brand, but it is better to recognize those challenges and move forward than not to realize that they are a real problem.
Data modeling techniques to know about
Everyone in the business world should know about a handful of data modeling techniques. Learning these basics is a big step toward setting up useful data models.
You have likely already seen this technique in action, even if you didn’t realize it at the time. It is commonly used across different organizations, and you may recognize it based on its “tree-like” structure.
This technique has fallen out of favor in many organizations because they tend to view it as too simplistic to be helpful in our complex world. That said, it may still pop up from time to time. Also, it may still prove useful in certain limited situations.
A more flexible data modeling technique is known as the network technique. Xenonstack.com describes it in the following way:
The network model provides a flexible representation of objects and relationships between these entities. It has a feature known as a schema representing the data in the form of a graph.
A node will represent an object in this model, and an edge represents the relationship between them. This means there can be multiple parent-child relationships when using this technique.
Defining data elements and relationships between the entities in a system is best done with the entity-relationship model. This model is straightforward to understand and helps visualize data much easier than it otherwise would be. Many organizations use this type of model because it helps bring data into focus in a way that it might not otherwise be. The relationship between various entities within this type of model can all be seen, and it is fairly easy to draw connections between the different elements within your model.
Don’t forget to look at the relational technique when you think about how different relationships work with each other. There are one-to-one, one-to-many, many-to-one, and many-to-many relationships. All of these can be successfully mapped out using the relational technique. Considering how these different types of relationships may make the data more or less useful in certain circumstances is imperative. This technique is particularly helpful for displaying complex data sets in a way that makes them easier to understand.
Suppose you take nothing else away from what you have learned here. In that case, you should take away the fact that data modeling is exceedingly important and that it should become part of almost everything you do within your organization.
Everyone within an organization needs the tools at their fingertips that will allow them to visualize data in the way it was meant to be seen. It is challenging to use the vast resources available to your company in terms of data effectively if you don’t spell them out in a way that everyone on the team can digest. For more information on data modeling and its usefulness in modern business practices, visit Pathstream today.
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