4 Major difference between Traditional BI and Self-Service BI

Updated: Jul 15


Differences between Traditional BI and Self-Service BI

 

You may be a tiny online ecommerce business attempting to choose which things to discount. Alternatively, you may be a huge oil corporation attempting to integrate project data in order to detect any symptoms of scope creep.


In either situation, a Business Intelligence platform (BI) solution is the only item that can give the information and answers you want. The programme is useful if you want to acquire actionable insights, regardless of how large or small your company is or what industry it is in.


 

Traditional BI


Traditional business intelligence is the "old-school" method of integrating data analytics technologies. It often necessitates a complicated IT system, data warehouse capacity, and near-constant interaction from IT employees.


The key point to remember here is that "old-school" does not imply "outdated" or "inferior." It simply indicates how huge corporations have used data analytics technologies since the introduction of BI into the business sector.


Between 1970 and 1990, IBM and Siebel Solutions (bought by Oracle in 2006) created the first complete BI systems. The first versions were data warehouses, which acted as central repository for integrated data from one or more sources.


These data warehouses were technically difficult and needed a large number of IT personnel to maintain and operate. They need BI specialists to extract insights from data and develop analytical reports for various departments and business operations. In other words, owning and running BI was a pricey affair that could only be sustained by resource-rich firms.


Self-service BI


Self-service BI is a data analytics implementation strategy that allows consumers to access and use data without statistical, analytical, or data handling skills. This strategy is based on BI tools that enable people to filter, sort, analyze, and display data to extract insights without the need for developers, data analysts, or IT professionals. The concept is straightforward: provide people immediate access to insight and assist them in slicing and dicing data as needed.


Technology innovation is altering what the market expects from BI solutions. As a result, suppliers are constantly developing and enhancing their solutions in order to enhance governance and scalability capabilities. Machine learning and artificial intelligence are increasingly being used in data preparation, data modelling, and insight production processes. Instead of a "query-building" procedure, natural language programming provides users with a search-like experience. Finally, advances in cloud technology are enabling the classification and processing of data in previously unimaginable amounts.


Tableau and Power BI, who were market "challengers" in 2020, have now become "leaders" in the Analytics and BI Magic Quadrant.


a) Usability


Traditional BI solutions have supplied firms with insights, but only via the data team's hands-on labour. This consumed time, assigned tiresome jobs to data specialists, and curtailed employees' liberty.


Everyone may be their own data scientist with today's business intelligence services. Instead of waiting for a report, a user may ask ThoughtSpot a question and receive fast answers via compelling data visualizations.


b) Analytics and Multi-Cloud Support Search Ability


In many respects, the world is unpredictable, but in the realm of corporate data, collection and processing volumes will continue to climb. When it comes to connecting with different data sources, traditional business intelligence tools, often known as legacy or on-premise solutions, have generally fallen short. They were also not designed to manage enormous amounts of data, especially across different clouds.


c) Capabilities of Artificial Intelligence


Artificial intelligence bridges a large gap between traditional corporate intelligence and current self-service analytics. Traditional BI solutions used to deliver insights that were either pre-set or manually regulated by data experts. This hindered production and reduced the amount of insight a company could create.

To extract as much value (and profits) from data as possible, current business intelligence and analytics services must combine AI and machine learning.


d) Video Analytics


Video analytics transforms video into meaningful data in real time. They automatically create descriptions of what is happening in the video (metadata) and are used to recognize and track items in the video stream that may also be classified as people, automobiles, and other things. This data is utilized to make decisions, such as whether security personnel should be contacted or if a better quality recording stream should be used. Video analytics transforms ordinary IP video into commercial insight.


If you are still undecided about which BI method to choose, you can contact a Business Intelligence or Power BI service professional for a free consultation and price information tailored to your specific needs.


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