The BI Technology Enterprises Rely on for Business Agility
This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Business Intelligence Agility" The speaker is Mark Flaherty, CMO at InetSoft.
So, let’s look at the business intelligence and other technology that enterprises rely on for business agility. This is possibly the most important of the slides so if you walk away from this presentation with anything, then it’s the message of this particular slide. What you see here on the vertical axis is the response time of applications. And that’s actually a logarithmic scale so every marker on that scale is actually ten times slower than the one below it.
Right at the bottom we have the range of real time response, and the we have less than one tenth of a second, and above that where people interact with a computer you have about from two or three seconds down to one tenth of a second. Above that are transactional systems response ranges. You get out to about fifteen seconds. Above that you get what are really batch processes, and then you go out into minutes, and the next level is another kind of batch level that goes to hours and after that to days.
Now, what we’ve drawn on this graph is a number of curves that represent years in history, starting with 1960. You see the curve gradually move in a kind of southeast direction across this graph. Gradually taking in more and more applications in terms of what computing was able to do and the direction of those arrows that are heading south east indicate an area of as yet unrealized applications. Gradually you see this curve eating up all of the potential things to do in the business sense also in a scientific sense I suppose.
| #1 Ranking: Read how InetSoft was rated #1 for user adoption in G2's user survey-based index | Read More |
Movement of Applications
And then these white arrows that are kind of curving downwards in a southwest direction, they represent the movement of applications because the response time changes and they move down from slow batch to medium batch to fast batch to transactional to interactive into real time. And this movement is happening all the time and this marginal technology which is fundamentally Moore’s Law which is being illustrated here.
It just keeps on going and every year that goes by, certainly ever five or six years you hear of something almost completely new for instance complex event processing, streaming, the analysis of streamed data, that didn’t exist before about 2003, it suddenly comes up, and what you’re actually seeing with that is you’re seeing business intelligence reporting begin to become near real time.
This is the march of technology, and being able to negotiate the march of technology is what successful business is about. Really it’s one of the primary factors in successful business, it is probably better to say. Maybe three or four years ago we never thought of BI as anything other than a set of applications with a bit of infrastructure sitting over a data warehouse.
There may have been sophisticated BI applications, but we didn’t really think of there being an overarching architecture to them. But now what’s happening in the world of BI, we’re seeing more and more of vendors that are now moving towards becoming a platform for BI, rather than just delivering individual applications. This is an example of the kind of strategic technology change that happens when you get this regular unavoidable change in technology.
To summarize what we’ve talked about so far, there are four levels to change, global, sector based, business processes, and technology. Global changes are beyond your control. There’s nothing you can do about them, and you probably didn’t cause them. Sector factors depend upon corporate excellence. Business process factors depend upon agility, and technology is in permanent flux and very difficult to master.
 |
Learn how InetSoft's data intelligence technology is central to delivering efficient business intelligence. |
Now I will build and elaborate a little bit more on this focus around change, this idea of how process and the technology in support of process evolve. We’ll talk about a roadmap of how you need to categorize and deal within the current environment of change. What’s also very interesting in this environment, especially as we’ve gone through the last couple of years, really starting last year and moving to this year, is the shift from cost containment and efficiencies to top line revenue growth.
Over the last year, the Business Intelligence (BI) landscape has undergone significant transformation driven by technological advances, changing workforce expectations, and evolving data challenges. BI professionals now face a variety of new demands and opportunities that affect everything from tool selection to data strategy implementation. Below are some of the most impactful changes reshaping the field.
1. Generative AI and Natural Language Queries
The biggest game-changer has been the rapid integration of generative AI and natural language processing (NLP) into BI tools. Vendors have begun embedding AI copilots that allow users to ask complex questions in plain English and receive visualizations or data narratives instantly. This shift is making analytics more accessible to non-technical users, reducing the burden on data teams while also forcing BI professionals to learn how to fine-tune AI models and manage prompt engineering in enterprise contexts.
2. Convergence of BI and Data Science Workflows
There is growing convergence between traditional BI workflows and more advanced data science practices. Many BI platforms now offer built-in machine learning capabilities or integrate directly with notebooks and predictive engines, enabling analysts to deploy models alongside dashboards. BI professionals are being pushed to adopt new skill sets in statistical modeling, feature engineering, and version-controlled experimentation to remain competitive.
3. Embedded and Headless BI on the Rise
The demand for embedded analytics and headless BI is accelerating as organizations look to deliver seamless data experiences within SaaS applications and customer-facing portals. BI teams now need to work closely with developers to implement flexible APIs, SDKs, and design systems that support white-labeling and user personalization. This trend requires BI professionals to understand front-end frameworks, RESTful API integration, and security models for multi-tenant environments.
4. Cloud Cost Optimization and Data Fabric Architectures
As cloud data warehousing costs continue to rise, organizations are reevaluating how they store, query, and transfer data. The modern data stack is evolving into a more cost-efficient data fabric that spans multiple sources without centralizing everything in a single warehouse. BI teams are being tasked with optimizing queries, reducing data movement, and leveraging in-place analytics technologies to reduce spend while maintaining performance.
5. Growing Emphasis on Data Governance and AI Ethics
With stricter data privacy regulations and the growing role of AI in decision-making, data governance has become a central concern. BI professionals are increasingly responsible for enforcing data quality standards, managing data lineage, and ensuring compliance with evolving frameworks like GDPR, CCPA, and AI transparency mandates. This shift is pushing BI toward a more cross-functional role that blends analytics with legal, ethical, and operational oversight.
As BI professionals navigate these challenges, staying ahead means not only mastering new tools, but also adapting to broader shifts in organizational culture, user expectations, and the data ecosystem. The coming year will likely see further acceleration in these areas, making it crucial for analytics leaders to remain agile and forward-thinking.
| Previous: How to Achieve Greater Business Intelligence Agility |