Data automation programmatically updates data on a portal instead of manually. For your open data program to be sustainable long term, automating the process of uploading data is important. When data is updated manually, it risks being delayed since it is another task added to an individual’s workload.
Automation consists of three elements which are extract, transform, and load. Extracting is a process where data is extracted from one or several sources. Then, that data is transformed into the structure that’s necessary and can also include changing abbreviations to full names. Finally, the data is loaded into the open data portal. Each step is necessary for data uploads to successfully be fully automated.
Without needing human attention, data automation completes this process using infrastructure, software, intelligent processes, and artificial intelligence. Data automation can save money and time and increase business efficiency. Errors are also reduced because automation ensures that data is loaded correctly. When a business has an automated data analytic process, this allows them to focus on the analysis part rather than the preparation of data.
Having a data automation plan is critical for your company. To assist in engaging in the right people at the right time in your company, a strategy in place ahead of time is key. Without a strategy in place, your company could be wasting time and resources by straying off the path it should be on. Revenue loss is also likely and could cost you extra money. Your business goals and data automation plan should both be in line with each other.
The first step to developing a strategy for data automation is problem identification. You need to determine what areas within your company can benefit most from automation. Then, data needs classified. The first stage of data automation is sorting through data and putting it into categories based on accessibility and important. Operation prioritization is essential in developing a data automation strategy. The tasks that take a longer amount of time to be completed by staff, are the ones that will make a bigger impact if automated. The next stage determines what transformation are necessary to convert data into its target size. Abbreviations may need converted into full text names or database data may need converted to a CSV file. Then, its time to execute and test data automation processes. Publish the dataset to the open data portal and verify that it was loaded successfully without any issues. Make sure to schedule regular updates for your dataset. You should use an ETL product with task scheduling and workflow automation to ensure that the complete process is carried out without needing manual assistance.
