BI Personalities in Modern BI: Producers and Consumers
Watch this video to learn about the personalities or personas that make up the analytics ecosystem.
There are a lot of personas involved in analytics, from people who facilitate data collection and its movement through the data supply chain to system administrators to analysts to users who make decisions based on data insights. And, broadly speaking, these individuals fall into two categories: analytics producers or consumers. They are present for any analytics application. This is especially in analytics that are not automated through machine learning or bots -- human interactive analytics that organizations use to conduct business.
Transcript
I'm Anurag Tandon, VP of Product Management at Zoomdata. I've spent about 18 years in the BI and analytics space. I’m here to talk about BI personalities in the modern BI age.
So, most conversation in the big data space right now focuses around the data, the architecture, how data is moved through the organization's pipeline, how it is stored, the different tooling that is in place, all the different tech that surrounds it.
I'm here to talk and share some thoughts more about the personalities that are using BI in the modern BI and big data analytics space.
Modern BI Personas
So, there are a lot of personalities that are involved, and if you look at the analytics system end to end, it's the people who are collecting, facilitating the data collection, the management, the moving through the organization pipeline and the people who are system administrators who are managing the systems to the people who are actually extracting insights from the business data and then sharing.
But, I'll assert here that there are two major, two broad persona groups that are very important for us to consider and look at and continue to make sure that we provide value to them, which are essentially the analytic producers, the people who are extracting insights from the data, and the analytic consumers who are using those insights to power decision making and making those decisions on the front line.
Producers and Consumers for Every Analytic Application
So, these producers and consumers are present in any analytic application. So, for example, you think about AI who's just made a major comeback in the recent years, and it's pretty much due to the abundance of data that is available for these applications and the maturity of the machine learning algorithms and the data science principles that have been used in the past few years to really provide smart analytics to us, in all of our applications - so you can think of Type Ahead in IOS apps, or you can think of Google Translate, and you could think of how Google Search itself has evolved over a number of years.
Smart Algorithms
And so, if you think about these applications, there are the producers of analytics in these applications. So, for example, the data scientists, who are providing algorithms, smart algorithms, they could be considered as producers. The algorithms themselves could be considered as producers of these analytics.
Automated Bots
And then there are--there might be, you know, automated bots that are actually using those algorithms and applying it to automated tasks that are considered the consumers in this equation. Or, it could be individuals like you and I when we're using our apps on our phones taking advantage of the insights that those analytics are powering for that application.
Analytics with Human Interaction
So, that is sort of an example of the producers and the consumers side of things for automated type of analytics. So, that's sort of one track of analytic types of applications. I'm gonna focus more on the other track, which is more the mainstream track for analytics, which is around human interactive type of analysis and not necessarily analysis that is done through machine learning and through more advanced data science.
