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
Self-service analytics is going to change the entire analytics industry. Even though this version of analytics is not as powerful as other analytical platforms, self-service is going to transform the industry with the value it brings to organisations. Self-service in analytics is possible because of the rise and convergence of several technologies, like AI, big data and data visualisation. I believe that this form of analytics is going to have a transformative effect on the industry, so I am going to take the time to explain why I think this.
Before going into its transformative impact, I need to explain what is meant by self-service analytics. It refers to data analytics platforms that are accessible and usable by business-minded people or those who don’t have a background in data analytics. Such an analytics platform is not as advanced as other platforms, but the benefit is that it can be used by non-technical people to perform basic functions. Basic functions like generating reports, performing queries and accessing relevant data. With this form of analytics, users can perform day-to-day operations without having to consult an analytics team.
There are several benefits to using self-service analytics. The first is better productivity because organisations can get tasks done by themselves because they do not have to contact their data analytics team for the smallest task. With basic functions covered by other professionals, the data analysts’ time is free to do more advanced work, making better use of their time. Furthermore, self-service analytics democratises data analytics, making it more accessible to people who don’t have a background in data analytics.
There are several benefits to using self-service analytics, but what impact will it have on the analytics industry, as a whole? For starters, it opens the door to more analytics platforms. Self-service analytics is built for different end-users who want to complete different functions. These functions include, but are not limited to workflow integration and operations reporting. It opens the door to a wide variety of data analytics platforms with different functions and capabilities, enriching the industry with different product types.
The biggest impact of self-service analytics will be felt in big data. With end-users able to process data on a basic level, there is going to be an explosion of valuable data produced by organisations. Organisations and their data analytics consultants need to set strict standards on how data is accessed, processed and protected. Although, if organisations want to encourage a culture of data exploration, they should adjust their data governance standards to be lighter and flexible.
Finally, self-service analytics completely changes the relationship between data analytics professionals and their clients. With organisations being able to perform analytics services, they will no longer call on analytics organisations for basic services. This means analytics teams have the option to offer higher-value services to their clients. The 2018 State of Embedded Analytics Report reported that over 49% of organisations saw a reduction in the number of ad-hoc requests from clients once self-service analytics was implemented.
It’s easy to think that self-service analytics is going to replace data analysts, at least in theory. However, reality tells a different story. The fact of the matter is, people with a non-technical background don’t have the time (and in some cases, the inclination) to become familiar with more advanced data analytics platforms and even learn other techniques, like data mining. Hence, while self-service data analytics is perfect for executing basic functions, it is no replacement for data analysts because they are not suited for more advanced functions. At least for now 😉
Self-service analytics are a preview of what we can expect for the future of analytics, i.e. making analytics more accessible. Previously, data analytics was restricted to a handful of experts, and only to large corporations who had the scale and scope to invest in analytics. Cloud computing and AI combine to open analytics platforms to parties that never had access to it before. It is an exciting time for data analytics because its benefits will be accessible to organisations of all sizes. Some analytics organisations are adjusting to this reality either by providing self-service analytics or on-demand analytics made possible through cloud computing.