For example, airlines are known for keeping close track of and frequently monitoring their prices. Airlines that can quickly identify flights or routes that need to have a price adjustment can have the advantage of being ahead of their competitors resulting in more sales. Similarly, some airlines have implemented processes where travelers can monitor where their luggage is on their phones, giving customers peace of mind and higher satisfaction. Each of these examples requires data automation.
Without data automation, companies may have to rely on employees manually going to their data source, downloading a file, performing necessary transformations, uploading it to a BI tool, visualizing the data, and then sending the visual to whoever needs it. Not only is this more time-consuming, but it is also delivering older data. Data becomes stale the minute it leaves the source system, causing your business to have inaccurate reporting.
Having an effective BI tool can allow a business to automate each step in their data reporting process, from data integration all the way to exporting and sharing the data.
For every credit card transaction and every online order submitted, data is being generated and stored. By utilizing a BI tool, a company can connect to their data source and automate the process of extracting the data from it.
Once connected, data can be refreshed in minutes rather than hours. Since there are many different sources to gather data from, it is important to ensure that the BI tool used can effectively establish a connection between itself and the data source. Often companies will need to gather data from multiple sources.
An important thing to consider is to know how frequently your data will need to be updated and at what time it will need to be available. Some datasets may only need to be updated weekly a couple of hours before a meeting. While other datasets may need to be updated as frequently as every few minutes.
Once you have established an effective connection, you will likely need to make changes or transformations to the data. These could include changes such as joining or connecting the data to other data that is on hand or is also being brought in, removing unneeded columns, aggregating the data, creating calculations, or any of a handful of other transformations.
Once set up, a BI tool is able to take in data and make any of these changes without the need for an employee to manually do that each time new data is requested.
Another important aspect of this step is to ensure that the BI tool being used has the power to process all the rows coming in. For small- or medium-sized organizations, this will likely rarely be an issue. However, for large organizations, this could be an issue that will require an in-depth look and research into the best BI tool for them.
The most visual aspect of this process is refreshing the dashboard or visualization. Having these are very beneficial to businesses as they provide quick, summarized information about company metrics.
Once data is in the BI tool, updating the visualization is likely the easiest part of the process as most BI tools will know to do this automatically.
Automating data dashboards
BI tools can also automate the creation of data dashboards through pre-built tools. Often referred to as quickstart dashboards, these processes connect to data from a source system, transform it, and display it to an end-user with a few short clicks.
A great example of this is a website analytics dashboard. Using a common tool such as Google or Adobe analytics, an end-user can create a fully functioning dashboard in minutes using an automated quickstart template. Metrics such as number of visitors, bounce rates, and average page view are displayed automatically, allowing end-users to extract quick insights from the dashboard.
Exporting and sharing data
The last part of the data automation process is exporting and sharing the data. The data is nearly useless unless it makes its way to the person who will use it to make decisions or benefit from the information.
An effective BI tool can, once finished updating the visualizations, send the visuals to whoever has requested it. Many times, this can include emails, texts, or other forms of updates.
Another way to take advantage of automated data is using the BI tool to schedule alerts. These alerts can be scheduled to be sent on a regular basis or can be configured to send only if the data triggers an alert (for example a metric falls below a specified threshold).
Often, those with the data will want to make it accessible to anyone through embedding the visual on a website. This can also be configured through the use of BI tools so that visitors to the website can monitor and view the most recent data.
Applying data automation within your own organization
BI offers powerful capabilities when it comes to data automation. Using a BI tool, your employees can be more efficient in their job responsibilities, allowing them to focus their time and effort on tasks that drive value to the business.
Data automation can be an important investment for your organization, regardless of its size or type. Having a BI tool and trained data professionals can accelerate every single department within your organization—whether that’s sales, marketing, customer support, or finance.
Having effective data automation is an important aspect for many companies and vital for others. It can provide more accurate answers, increased adaptability, higher trust in the data, and increased interaction with it.
Any company that uses data to make decisions can benefit from data automation in its reporting.
Check out some related resources:
Creating apps with no coding in 5 simple steps
Building Data Integrations on a Modern BI Platform
Modern BI for All Field Guide: Data Agility
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