Sep 18, 2026 · 30 min · 8 segments
In this episode, co-hosts Emma McGrattan and Ole Olesen-Bagneux sit down with Ben Mathew, General Manager of Business Intelligence at Actian and former GM of Jaspersoft, to explore how business…
Ben MathewGuest
Ole Olesen-BagneuxHost
Emma McGrattanHostOkay, that is obviously an interesting story, moving countries like that, and I guess there's a lot of different experiences associated to that, and I wish we had more time diving into that, Ben.
But I will jump into some of the other questions, because obviously, as you mentioned in the introduction, You have quite a history with business intelligence with BI.
So I want to ask you, what is the reason business intelligence emerged in the first place? Like, why do enterprises need business intelligence?
So let me start with the formal definition, and then maybe we can move on to more of a little bit of a working definition.
Business intelligence is the set of processes and tools that takes raw operational data, transactions, logs, sensor feeds, customer activity, and turns it into something that a person can look at and act on.
So it's collected, integrated, presented in a form that a human can actually use in the moment they're making a decision.
Now, over time, the term was used to describe mostly the last mile of these processes, which is how data was finally presented.
Now, the most commonly used categories of BI and analytics are broken into four types.
Descriptive is mostly in the realm of BI, right, as we know it, that last mile of the formal definition I mentioned.
And then predictive and prescriptive fall squarely in the analytics category with the latter parts encompassing data science as well.
So what does this mean for enterprises? I like to think about it in three stages of what they go through in their data journey.
So hindsight is what happened, and this is where BI started, right? So static reports and dashboards telling you last month's numbers, last year's performance.
And this is where you have a little bit of BI and a little bit of analytics drilling into the drivers, segments, and root causes instead of just the headline number.
This is where most mature BI deployments sit today, right? Not just the backward looking.
And foresight, of course, is what's likely to happen, right? And what to do about it.
Okay, that is obviously an interesting story, moving countries like that, and I guess there's a lot of different experiences associated to that, and I wish we had more time diving into that, Ben.
But I will jump into some of the other questions, because obviously, as you mentioned in the introduction, You have quite a history with business intelligence with BI.
So I want to ask you, what is the reason business intelligence emerged in the first place? Like, why do enterprises need business intelligence?
So let me start with the formal definition, and then maybe we can move on to more of a little bit of a working definition.
Business intelligence is the set of processes and tools that takes raw operational data, transactions, logs, sensor feeds, customer activity, and turns it into something that a person can look at and act on.
So it's collected, integrated, presented in a form that a human can actually use in the moment they're making a decision.
Now, over time, the term was used to describe mostly the last mile of these processes, which is how data was finally presented.
Now, the most commonly used categories of BI and analytics are broken into four types.
Descriptive is mostly in the realm of BI, right, as we know it, that last mile of the formal definition I mentioned.
And then predictive and prescriptive fall squarely in the analytics category with the latter parts encompassing data science as well.
So what does this mean for enterprises? I like to think about it in three stages of what they go through in their data journey.
So hindsight is what happened, and this is where BI started, right? So static reports and dashboards telling you last month's numbers, last year's performance.
And this is where you have a little bit of BI and a little bit of analytics drilling into the drivers, segments, and root causes instead of just the headline number.
This is where most mature BI deployments sit today, right? Not just the backward looking.
And foresight, of course, is what's likely to happen, right? And what to do about it.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
