Since the term ‘Business Intelligence’ was coined in the late 80s, organisations worldwide have been leveraging BI to make sense of the huge amounts of data that they have. Through a set of analyses, BI enabled businesses to derive value and insight from data to make the best, informed decisions.
However, traditionally, harnessing this insight has complex, using technologies and tools that were developed for the technical experts in mind – those with extensive knowledge in both SQL programming as well as data analysis. This meant that they were only able to utilise the advanced data analysis software, while the majority of normal users were unable to execute BI tasks on their own. Moreover, formulating and delivering reports to decision-makers was a time-consuming process, held back by data which were held in distinct silos.
Today’s BI and analytics tools are an amalgamation of different functionalities including data access, query, analysis, reporting and presentation. The fact that these functionalities have become much simpler to use as well as accessible – able to be consumed on desktops, mobile devices, the web or even on cloud platforms. As improved techniques and technologies emerge, today’s solutions can also be used by a much wider range of users, and not just the data scientists and analysts.
However, we can see that the whole analytics experience with BI is now moving to the next step of its evolution; automation, brought on by the rise of AI technology. When it comes to discovering insights, AI has definitely changed how users interact with their data and workflows. The incorporation of machine learning has been especially important in that it allows BI systems to continuously learn from data, identify patterns and present predictions, actionable insights and other information with minimal human intervention.
Most importantly, machines are able to achieve all this at a fraction of the time.
As such, it isn’t surprising when TDWI Research found that only a small number of global organisations (14%) surveyed did not plan to adopt AI technologies to augment their BI, analytics, data management, or data integration implementations. The vast majority either already have an overall strategy and are following it, or are planning to make use of AI technology to a smaller degree – applying AI to individual departments or where they see fit.
As to how they’re using (or planning to use) AI/ML, the top three use cases are “to automate discovery of actionable data insights”, “to improve the accuracy and quality of information through smart automation”, and “to augment user decision making by giving recommendations”. Ultimately, it is about empowering users to be more productive, making processes faster and results more accurate.
The “BI and Analytics in the Age of AI and Big Data” report by TDWI Research explores the business impacts of new technologies such as AI, the challenges faced by companies adopting these technologies and offers recommendations and strategies for success. The report also takes a look at solutions offered by leaders and innovators in BI, analytics, and data management, such as Hitachi Vantara, to give a sense of where the industry as a whole is headed.
To download the report, click here.