While there is no denying the benefits of data analytics, one point of concern I hear from many representatives is that they lack the skill set and technical knowledge to make full use of the data analytics platform.
Furthermore, there is another concern to be had. How can employees without technical skills, take full advantage of these analytics platforms? They would be dependent on a separate team of technical people, which can lead to problems in terms of communication and collaboration.
These are perfectly valid concerns, and it is why self-service data analytics tools can become useful.
Self-service data analytics tools are designed to take the complexity out of data analysis and reporting to make it a more accessible process. These tools accomplish this through a visual, interactive dashboard (among other methods) so that people who may not have a background in programming and coding can still conduct data analysis and read any reports generated from these tools.
Self-service data analytics tools offer a lot of benefits that are sure to help businesses.
Self-service data analytics empowers non-technical personnel to analyse and interpret data as they see fit. Instead of having to rely on programmers to do the work, non-technical personnel can dive into the data and analyse it as they see fit. This means they can ask questions and interpret data using their unique perspective on the subject matter. This is sure to enrich the data analysis process, which will benefit the organisation in the long-run.
When organisations use self-service data analytics tools, there is less back-and-forth between different departments. For example, with conventional IT systems or analytics platforms, most departments rely on their IT sector to collect and analyse data. However, this often means a lot of back-and-forth between different departments, which could hinder productivity. For example, if the IT department does not have access to all the data, it makes data analysis somewhat difficult. This means organisations have to invest a lot of time and money coalescing data from different sources to ensure data analysis is complete.
However, by implementing self-service data analytics tools, organisations can make collaboration more efficient, simply because different departments are not as dependent on one team for their analytics needs. This makes it easier to create research reports in quick time and reduce the back-and-forth between departments.
Self-service data analytics tools come with different features that make data analysis much easier, but also more effective. Most organisations have their data scattered across different data sources, which often leads to an incomplete data analysis, or atleast, not one that reaches its full potential. However, self-service data analytics tools come with several features, like visualisation and interactive dashboards, that make the process much easier. With these methods, data analysis is much easier.
Visualisation makes it much easier to draw connections between two seemingly unrelated variables. Furthermore, with interactive dashboards, it becomes much easier to dive into individual KPIs and breakdown the metrics in more detail. Furthermore, these tools can help refine the reporting process to help generate more detailed and better-structured reports.
When only a handful of professionals can access and analyse data, it remains in a silo. However, with self-service data analytics tools, anyone can access the data, essentially democratising it within the organisation, making it easier for anyone to contribute to its sources, enriching the data, and making it easier to keep it up-to-date.
Self-service data analytics tools make data analysis much easier to conduct. By democratising the data analysis process, organisations have the potential to transform their data analysis procedures by making them more timely and efficient. Furthermore, it can reduce the time taken for employees to adjust to the platforms, which makes it much easier to incorporate analytics meaningfully into the data analysis process. With these tools, organisations will have a much easier time installing and using data analytics to generate data in meaningful ways