Mark Flaherty: So, the big question is how do you really align those very often different and opposite requirements. Well, it's not all bad news, and there are lots of next-generation BI technologies in addition to some of the best practices that can help. There are definitely these next-generation technologies that can indeed bring business and IT closer together and can align them.
The two key ones that we are spending time talking about today are the next-generation technologies that make business intelligence and analytics environment much more agile and that do indeed enable end-user self-service. And among some of these specific technologies, I can name lots of them, but very relevant to today’s discussion are databases that are built from the ground up for analytics, not for transaction processing.
When we look at traditional databases, they were all invented thirty or forty years ago for transaction processing, and sometime optimizing them for analytics is almost like trying to have a square peg into a round hole. So we definitely need analytical platforms that are designed from the ground up for business intelligence, not for transaction processing.
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Businesses also spend a lot of time modeling data, and that’s precisely what IT does very well. They collect requirements and they transform those requirements into data models. But there is one problem with that. It takes too long, and by the time you are done, the requirements have changed. And my point is that if you didn’t foresee some of the requirements, and you didn’t model them, well then, guess what, you can't really analyze that condition. So we definitely need environments that are driven by the data content itself, not by the data models.
I can keep going on and on, but another key feature for agility and self-service is a point-and-click user interface. For many years, vendors have been promoting products that have point-and-click and drag-and-drop, especially for those of us who are old-timers who are used to green mainframe screens and even working with punch cards, obviously graphical user interfaces with point-and-click, drag-and-drop interfaces are much more user friendly. But to non-technical end-users, you really have to design the application smartly to make them really user friendly.
Convergence of BI with AI and ML
The future of BI software is poised for even greater innovation and transformation. One key trend on the horizon is the convergence of BI with artificial intelligence (AI) and machine learning (ML) technologies. By integrating AI and ML capabilities into BI platforms, organizations can automate data analysis, uncover deeper insights, and drive more informed decision-making in real-time. This fusion of BI with AI and ML holds immense potential to revolutionize how businesses leverage data to gain competitive advantages and adapt to rapidly changing market dynamics.
Another prominent trend shaping the future of BI software is the rise of augmented analytics. Augmented analytics refers to the use of AI and ML algorithms to automate data preparation, analysis, and insights generation, enabling business users to explore data and discover actionable insights with minimal manual effort. By leveraging natural language processing (NLP) and natural language generation (NLG) capabilities, augmented analytics platforms empower users to interact with data in a more intuitive and conversational manner, democratizing access to analytics across the organization. As organizations seek to become more data-driven and agile, augmented analytics will play a pivotal role in enabling business users to make faster, more informed decisions based on data-driven insights.
Convergence of BI with AI and ML
The future of BI software is characterized by the increasing emphasis on data governance, privacy, and security. With the growing importance of data privacy regulations such as GDPR and CCPA, organizations are under greater pressure to ensure the responsible use and protection of sensitive data. In response, BI vendors are enhancing their platforms with robust data governance and security features, such as data lineage tracking, access controls, and encryption capabilities, to safeguard data integrity and compliance. As data continues to proliferate and become more complex, organizations will prioritize BI solutions that not only deliver powerful analytics capabilities but also adhere to stringent data governance standards and security best practices, thereby instilling trust and confidence in their data-driven decision-making processes.