It would be challenging to list all of the ways in which data can be used. Whether it’s a business gaining valuable information about their operations or sports teams analyzing their statistics, data is what powers the world.
Data analysts work tirelessly to extract crucial insights and provide them to their clients. It’s a top priority for those working with large data sets to find the meaning in between the numbers. Sometimes, it isn’t easy to know what to do with old data sets that already exist.
Knowing when to restructure or recycle data presents limitations to the average data analyst. However, using old data to deliver new insights or solutions to problems pleases clients and leaves them feeling satisfied. A business can perform better, and it improves the skills of the analyst working with the data.
Understanding the difference between restructuring and recycling data allows analysts to make better-educated decisions. So, what is the difference between the two?
Restructuring Data
To restructure data for a company means to breathe new life into existing data. Using a tool like IBM’s Restructure Data Wizard allows analysts to draw new conclusions from a data set that has been used already.
Identification values in a data set are essentially correlations that can be put into a new table to review the variables that impact data. When someone restructures their data, they can provide new data that is narrowed in scope and can be used to tackle specific problems.
Recycling Data
Also referred to as reusable data, recycled data is similar to restructured data, except it’s unnecessary to follow through with the restructuring process. This can come in handy when companies do not have access to real-time data or are waiting for new data to be gathered.
When departments want to use data to aid them in achieving their goals, reusing existing data can make a world of difference. It’s no secret that working with data has its limitations. The collection and enrichment of data can be a tedious process, and without easy access, it makes an analyst’s job that much more challenging. Rather than collect new data, it’s a more attractive option to recycle data that’s already been collected.
When to Restructure or Recycle Data
Working with data requires a certain level of expertise and patience. Analysts must know whether to restructure or recycle data to achieve the greatest output. Here is how to decide between the two strategies:
When to Restructure
Here are some instances where it’s helpful to restructure data:
- To input data into other automatic applications or programs.
- Save time in the data reporting process.
- When trying to achieve a deeper analysis of data.
It’s vital to review the outcomes analysts are searching for. Sometimes, working with data presents challenges that only restructuring can help analysts overcome.
It’s also important to consider how the data will change in the format while restructuring. When thinking about the entire process holistically, it’s easier for analysts to know when restructuring their data is a worthwhile endeavor.
When to Recycle
It’s essential to know the uses for recycling data, as well. Below are three benefits of recycling them:
- Engage in partnerships with other organizations that can benefit from existing data.
- Improve internal collaboration between a company and its departments.
- Use recycling as an alternative to gathering live data.
Not all companies have access to data storage and live data just yet. Recycling older data sets with different variables can prove to be helpful for these types of organizations.
Make Data-Driven Decisions
Using data to make decisions is crucial in today’s world. Data analysts spend their time reviewing and analyzing data in data lakes and data oceans. Big data will continue to help business owners seize new opportunities and implement change within their organizations.
Without data, it would be impossible to gain a deeper understanding of the world of business. No industry would succeed without the use of data. Get the most out of data by considering a strategy of restructuring or recycling existing data.