What is data optimisation?

Data optimisation is the practice of fine-tuning a company’s data strategy in order to improve the speed of data handling. By extracting and interpreting your data, you can gain valuable insights and make informed choices for your business.

 

What can data optimisation do?

1. Standardise data: Standardising your data helps prevent errors when interpreting it.
2. Fix faulty data: Even the best analytic tools will provide erroneous results if your data is inaccurate. This leads to incorrect conclusions and reduces your ROI.
3. Eliminate irrelevant data: Irrelevant data lowers the rate of processing. By removing it, you are able to analyse your data at much higher speeds.
4. Removes lags: Latency slows down data processing by reducing the rate at which results are returned. Optimised data is far less prone to lags.
5. Gives you real-time data: You can gain insights in real time, in order to make the best decisions for your business.

 

What are the benefits of data optimisation?

• Low-risk decision making: Data optimisation gives businesses quick access to error-free data.
• Improved reputation: By relying solely on automation for your data, you open your business to the risk of errors. Data optimisation lowers the risk of mistakes and inconsistencies.
• Increased ROI of your infrastructure: Data optimisation gives you insights into the performance of your server, storage, network, and software. This makes it easier to make informed decisions and use these products efficiently.
• Improved speeds: Data optimisation reduces lags, ensuring you can get the data insights you need in real time.

 

How does data optimisation work?

Data optimisation uses machine learning to improve the speed and accuracy of data handling. This allows companies to view data trends and reports in real time.

 

Does my business need data optimisation?

These days, every company interacts with huge amounts of data. This volume of data is impossible to manage efficiently without some degree of data optimisation.

Machine learning can convert huge amounts of data into trends, insights, and reports. These can be studied to make high-quality decisions. When your company is able to detect trends as they occur, you get a larger and more accurate perspective.

 

What does standardising data mean?

For example, if you have a database of names, John Smith might be recorded as J. Smith, Mr Smith, John S., or Mr John Smith. This inconsistency can cause skewed analytics, among other issues. To avoid errors and false results, your data should be standardised.

 

What are the types of data errors?

Common data errors include:

• Repeated entries
• Inconsistent formats
• Insufficient information
• False data

Data optimisation can find and fix data errors to ensure your analysis is accurate and efficient.