As the involvement of data has increased with the passage of time, businesses these days have to rely on a variety of tools to make sure that they make the right decisions. The greater amount of data that they have at their disposal, as well as some other considerations, goes a long way toward making sure that they not only have a distinct advantage in terms of information but can make an informed decision based on that data (Lee et al. 2014). The role of analytics, for instance, has become really important these days (Lee et al. 2014). As businesses have moved toward advanced analytics, it is important to make sure that businesses can manipulate data to their advantage so that they can find the right sort of business opportunities (Lee et al. 2014). This act is called self-service analytics, whereby organizations are trying to mould the data as per their needs and wants in order to have greater clarity in terms of how business decision-making needs to be carried out (Venkatesan et al. 2015).
Data Proliferation And Self Service Analytics
With the increase in data proliferation, the role of Self Service Analytics has become really important these days (Lee et al. 2015). Businesses these days have to make sure that they draw the analytics down and make sure that all the business users have the right sort of data at their disposal to ensure that they make the right decisions (Li et al. 2015). The major challenge in this regard is how business support is going to be carried out in terms of self-service analytics (Venkatesan et al. 2015) while making sure that the safety and integrity of the people are being taken care of. The key purpose of Self Service Analytics is to make sure that businesses are empowered to make the right sort of decisions and can work with the relevant data (Schlesinger and Rahman, 2016). Not only that, but the idea is also to make sure that massive data proliferation can be used in a manner that allows businesses to analyze the data on their own effectively without resorting to much help from the IT or Business Intelligence Team (Lee et al. 2015).
Rise Of Self Service Analytics
Self-service analytics is a much more refined but, at the same time, simpler form of business intelligence (Lee et al., 2015). The idea is to make sure that business users are empowered in a manner that allows them to access the relevant data and perform the right sort of queries (Schlesinger and Rahman, 2016). Not only that, but report generation, as well as other aspects, is also important in terms of the way easy-to-use Self Service Analytics works (Li et al. 2015). The entire process and the way it is designed are carried out in a manner that is really simple and is scaled down to a great extent (Lee et al. 2015). The key need is to make sure that the business team and the users are able to execute and perform the day-to-day analytics tasks on their own (Lee et al. 2015).
Management Of Information Overload And Self Service Analytics
One of the problems that modern organizations face is how they cope with information overload (Alpar and Schulz, 2016). Organizations these days need to be more efficient and agile when it comes to looking at new data sources, but at the same time, it is imperative that this information serves the business requirements. Self-service analytics is one of the ways through which it can be made sure that only the right sort of information is available to the end user. The key challenge, though, is to make sure that data management is carried out during the course of Self Service Analytics being carried out by the employees (Alpar and Schulz, 2016).
What Self Service Analytics does is introduce more powerful and self-serving BI platforms while making sure that the BI tools are working in the appropriate manner (Nutman et al. 2015). Not only that, it also allows the expansion of modern BI tools in such a way that each of the individual business units is going to work efficiently, to say the least (Alpar and Schulz, 2016). The other important consideration is how strict governance is going to be carried out in terms of data quality and consistency (Li et al. 2015). What Self Service Analytics does is define clear roles and responsibilities through which users across the board in the organization can make informed decisions about the direction that the business needs to take (Alpar and Schulz, 2016).
Expertise Needed For The Implementation Of Self Service Analytics
One good thing that Self Service Analytics is going to bring about is that it will make sure that the implementation of self-service analytics allows the core analytics team to concentrate on better tasks (Nutman et al. 2015). Most of the time, what really happens is that the BI team is supposed to make sure that they are providing reports and templates to the end users, which is not something that they are supposed to do (Li et al. 2015). Part of the problem is the fact that, at times, general business users do not have much insight into the analytics and business intelligence that is going to work out. With the adoption of Self Service Analytics, it would be ensured that the generic business user is in a position to get all the relevant reports on a self-generated basis and does not need any sort of assistance from the BI team (Alpar and Schulz, 2016).
Increased Synergy In The Team
With the introduction of Self Service Analytics, the users and the core data team are going to work in a manner that would eventually increase the level of synergy that exists among the team (Acito and Khatri, 2014). They can sit together and make sure that they are opting for better results all the time (Acito and Khatri, 2014). What can happen is that the business users can help themselves with self-service. Not only that, the core data science team can also take input from the Self Service Analytics team to make sure that they can go beyond conventional analytics and create something that is going to provide long-term value. That does not mean that Self Service Analytics does not have its risks (Acito and Khatri, 2014).
Risks And Pitfalls Of Self Service Analytics
There are considerable risks that can be seen when working with Self Service Analytics. Most of the time, these risks are based on how business intelligence works out in the long run and some of the considerations and expectations of business users in this regard.
- There is always a need for proper training for all the users who are looking after Self Service Analytics, and at times, when the business users do not have proper training, they are not able to use the tools in the right manner (Li et al. 2015).
- The overall acumen, education, and system expertise of the user also have to be taken into account, and thus, the organization has to make sure what some of the protocols and rights are that can be given to the users (Nutman et al. 2015).
- There is also a risk in Self Service Analytics in terms of the consistency of the data, and thus, organizations have to make sure that they have accurate data at their disposal so that the purpose of Self Service Analytics can be served (Acito and Khatri, 2014).
Conclusion
Self-service analytics has gone a long way toward making sure that power is transferred from the BI users and analytical staff to the end business user (Marjanovic et al., 2018). What they can do is use the additional information that they have at their disposal to make sure that they make informed business decisions so that unexplored market niches and areas can be looked after (Martinez and Pulier, 2016). If Self Service Analytics is used in the right manner, it will go a long way toward making sure that long-term value is added to the business (Acito and Khatri, 2014). There are some potential risks in terms of the inconsistency of data and training, but it is up to the organization to manage them (Acito and Khatri, 2014).
Works Cited
Acito, Frank, and Vijay Khatri. “Business analytics: Why now and what next?.” (2014): 565-570.
Alpar, Paul, and Michael Schulz. “Self-service business intelligence.” Business & Information Systems Engineering58.2 (2016): 151-155.
Lee, Jay, et al. “Industrial big data analytics and cyber-physical systems for future maintenance & service innovation.” Procedia CIRP 38 (2015): 3-7.
Lee, Jay, Hung-An Kao, and Shanhu Yang. “Service innovation and smart analytics for industry 4.0 and big data environment.” Procedia Cirp 16 (2014): 3-8.
Li, Zhenlong, et al. “Enabling big geoscience data analytics with a cloud-based, MapReduce-enabled and service-oriented workflow framework.” PloS one 10.3 (2015): e0116781.
Marjanovic, Olivera, Barbara Dinter, and Thilini R Ariyachandra. “Introduction to the Minitrack on Organizational Issues of Business Intelligence, Business Analytics and Big Data.” Proceedings of the 51st Hawaii International Conference on System Sciences. 2018.
Martinez, Frank, and Eric Pulier. “System and method for a cloud computing abstraction with self-service portal for publishing resources.” U.S. Patent No. 9,489,647. 8 Nov. 2016.
Nutman, Thomas, et al. “Leveraging an Open Source Data Warehousing and Analytics Tool to Promote Longitudinal Research, Improve Knowledge Transfer and Avoid Redundancy Across Research Studies.” AMIA. 2015.
Schlesinger, Peggy A., and Nayem Rahman. “Self-service business intelligence resulting in disruptive technology.” Journal of Computer Information Systems 56.1 (2016): 11-21.
Venkatesan, Rajkumar, et al. “Consumer brand marketing through full-and self-service channels in an emerging economy.” Journal of Retailing 91.4 (2015): 644-659.
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