As the involvement of the 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 data that they have at their disposal, as well as some other considerations, goes a long way towards making sure that they not only have a distinct advantage in terms of the 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 towards advanced analytics, it is about making sure that how 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 where the organizations are trying mould the data as per their needs and wants in order o have greater clarity in terms of the way business decision making is needed 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 decision (Li et al. 2015). The major challenge in this regard is how the business support is going to be carried out in terms of self-service (Venkatesan et al. 2015) analytics 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 to also make sure that how the massive data proliferation can be used in the manner that the businesses can analyze the data on their own in an effective manner without resorting to much help from the IT or Business Intelligence Team (Lee et al. 2015).
Rise Of The Self Service Analytics Analytic
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 the 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 task on their own (Lee et al. 2015).
Management Of The 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 going to be carried out during the course of the 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 out in an efficient manner, 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 it tends to define the clear roles and responsibilities through which the 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 The Self Service Analytics
One good thing that Self Service Analytics is going to bring about is that it would make sure that the implementation of the self-service analytics would be done that is going to make sure that the core analytics is going to be concentrating on the better task (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 how, at times, the general business users do not have much insight into the analytics and business intelligence that is going to work out. With the adaptation of the Self Service Analytics, it would be made sure that the generic business user is going to be in the position to ensure that they are getting all the relevant reports on a self-generated basis and they do not need any sort of assistance of the BI team (Alpar and Schulz, 2016).
Increased Synergy In The Team
With the introduction o the Self Service Analytics, the users and the core data team is going to work in the 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 the 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).
Risk And Pitfalls Of The Self Service Analytics
There are considerable risks that can be seen when working with the 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 the 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 has to be taken into account, and thus, the organization has to make sure that what are some of the protocols and rights 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 the power is transferred from the BI users and analytical staff to the end business user (Marjanovic et al., 2018). What they can do is they can use the additional information that they have at their disposal to make sure that they make informed business decisions so that the 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 the 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.
