What is data organization?

Data organization is the practice of categorizing and classifying data to make it more usable. Similar to a file folder, where we keep important documents, you’ll need to arrange your data in the most logical and orderly fashion, so you — and anyone else who accesses it — can easily find what they’re looking for.

Why is data organization important?

Good data organization strategies are important because your data contains the keys to managing your company’s most valuable assets. Getting insights out of this data could help you obtain better business intelligence and play a major role in your company’s success.

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Each organization has its own particular relationship with its data, as well as specific needs for organizing it. A BI and analytics platform like Sisense can help you handle your rapidly growing piles of data. The following questions should be considered as you create your data architecture and decide how it will be analyzed:

  1. Is it scalable? You don’t want to sacrifice performance when working with larger and more complex datasets. Make sure you’ve got the power and technology to scale up on users, complex analysis, and workloads.

  2. Is it flexible and customizable?  Ideally, your choice should be tailored to your requirements. But sometimes, definitions and goals change in the middle of the project period. Your BI solution should be able to quickly meet your changing demands, without getting developers involved. You might want more granular customization, shorter time-to-insights, or have new regulations to adhere to. Your BI platform should be able to handle changes without sacrificing performance.

  3. Is your application future-proofed? The amount of data you’re managing, the number of sources it’s coming from, and the number of users that access it, will probably be ramping up dramatically in the next few years. It’s vital that your BI and analytics platform can handle a massive increase in data and usability. Sisense’s modern, scalable platform is battle-tested and agile enough to take your company’s insights into the future.

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What can you organize?

Your data is probably stored as one of the most common structure types. Tabular data are flat, rectangular files. This represents data that is currently stored in a spreadsheet. Most research data is stored in this structure.

Hierarchical files are typically xml files that are able to save data and metadata in the same file. This structure is used to avoid redundancies. Relational databases organize data in multiple tables, which can hold great quantities of data and handle complex queries.

In any good data organization strategy, understanding your data’s structure is key to unlocking its value. Data can be stories in two ways: structured or unstructured. 80 to 90 percent of the world’s data is unstructured — and that number is growing many times faster than its structured counterpart.

Data that is formatted, tagged, and organized in databases is referred to as structured. It can be easily accessed, processed, and analyzed.

Tips to ensure your data is organized in the most optimal way

  1. Establish consistent and clear naming practices. Name your files in a descriptive and clear way. If you need to rename multiple files, you can use a file renaming application to do it automatically. 
  2. Keep file titles short. Avoid symbols. If you use dates, keep a consistent format.
  3. Use consistent file version management. This means that you create a new file using an updated name, instead of saving over the old file. This is also known as “file versioning.”
  4. Create and use a data dictionary to standardize categories and provide a definition around the role of each. This will allow all your company’s stakeholders to get the most out of the datasets you’ve collected.

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