It’s clear that there has been a lot of recent market movement toward self-service business intelligence (SSBI) in the many vendor offerings that are out there. The Data Science community is also becoming more concerned that business users may not understand or misinterpret the data that is available, which could lead to incorrect results. (data science in Malaysia)
Experienced data scientists are very good at analysing, comparing, drilling down into, and viewing data in a way that allows them to get useful market intelligence from the data. In business, people who don’t have a lot of experience with data can make mistakes and not see important things.
Thus, even though self-service business intelligence platforms are available now, skilled data technologists may still be need to help business users get strategic intelligence and then show it to them in a way that is easy to understand. Self-service BI is more than a set of tools that are easy to find and use. It also includes a lot more than that.
Data for Analytics (data science in Malaysia)
In the post An Introduction to Self-Service Business Intelligence, the author talks about how an average person can “filter, group, or segment” data for Analytics without having to know how to use business intelligence systems, which was the case with traditional BI. SSBI doesn’t work like that: “One size does not fit all.” Before designing a self-service business intelligence platform, the IT team should pay close attention to the needs and expectations of the users in question. Self-service BI systems must be flexible and scalable so that they can be use by a wide range of people, even though only a few people are super-users.
He says in Why We Must Rethink Self-Service BI that the danger is making complex tools available to people who don’t know how to use them. Even though it’s not good to rely too much on data technology teams in the long run, there must be enough caution to make sure that some key technical people lead the business users at all levels in how they use their data and what tools they use to get the best results.
It’s not good for people to be left alone with sensitive data or technical tools without having been properly trained. DATAVERSITYarticle ®’s “Self-Service Business Intelligence” is a lot of text. Is it right for everyone? tries to figure out how to move from a traditional BI landscape to self-service BI.
Self-Service BI and Self-Service Analytics (data science in Malaysia)
Self-Service BI and Self-Service Analytics may not be able to replace Data Scientists, but they may help them.
In self-service BI, business users can use a guided analytics platform to find their own solutions without the help of a data technologist. However, the Forbes post “Why Self-Service Analytics Won’t Replace Data Analytics Professionals, May Help Them” shows that this goal is still a long way off, with technologies like OLAP and data discovery only meeting some of the needs of users. User: I want complete freedom from technical experts, but I’m not sure I’m ready for all of the new self-service BI technologies and tools that are out there yet!
In the case of data discovery, users have often been drawn to dazzling graphics, but they may have forgotten about the basics of getting meaningful insights from data patterns. Modern self-service BI tools are easy to find, but most business users prefer to stay with one business application instead of switching to a separate self-service analytics platform all the time.
Users want to be able to quickly get and combine data from different sources, then explore and query the data to get quick business insights that can help them make quick decisions. In some ways, new technologies like big data and Hadoop have made this possible. Interpreting and combing complex data to find useful insights is still a long way off.
How to Make Business Intelligence Self-Service
New technology problems keep coming up, and they require a thorough understanding of the entire Data Management ecosystem, which includes big data, Hadoop, data discovery, data visualisation, and other technologies.
Hidden costs of Self-Service BI initiatives raises an important operational issue for those who work with Self-Service BI projects. This is what the article says. As different data analysts work on their own projects at different times, there is a chance that SSBI’s overhead costs will go up. The simultaneous running of the same datasets with the same results will also use up system resources. On the other hand, analytics that are done for a specific purpose or for a small group of people can be hard to read and cost more. When one person or group makes data models, and another person or group makes reports, this can happen a lot. As a result, it might be better to make clear assumptions about data preparation, data modelling, and reports at the start of the project.
There are problems with self-service BI, like data security and data governance.
Consider hiring data security and Data Governance experts to make sure that a new self-service BI implementation goes through pre- and post-implementation checks for security, as well as Data Governance experts to make sure that the data is safe.
There are many reasons why data security on self-service BI platforms is important, including: Digital privacy measures to keep data from being see by people who don’t belong to the company, the prevention of internal misuse, and the avoidance of human errors when people use the data. You can find out more about how to make sure that Self-Service BI is properly govern in an article called Getting the Most Out of Your Self-Service Analytics. The article has a checklist for Data Governance.
There are still some self-service BI platforms that don’t have the right data security and Data Governance controls in place. The data preparation tools in good self-service BI platforms store, manage, and provide access to the source data, prepared data, and data models in a way that doesn’t stifle the ability to do self-service analytics.