So again, the push and pull with the consumers. The pull from consumers is I want to be able to do
something and I want to be able to do it easily. The push from the vendor side of things should really
be more about let me teach you the best practices, let me teach you the right way to look at this data, the
right way to analyze your business and to be able to make different decisions. So I am all in favor of
removing manuals, but those are the manuals that teach you which button to click. I am all for textbooks that
train people on data analysis and statistics and how to run a business.
Eric Kavanagh: Yeah. No, that’s exactly right.
Tracie Kambies: I think those are all a solid foundation. But I think that that is an education
which comes not from a manual, but from sitting and learning, it's actual hard core learning of foundational
capabilities that our education system needs, and we can get in a whole discussion about that. But when
you change our education system, then you should be able to start changing our generational thinking and
building on those learning aspects. And so it is not the manual, to your point Byron, I think it is
about teaching people how to think better, how to use statistics in a different way and how to really create
those building blocks, versus having to read the manual on where to find my settings and where to go to get to
this one particular type of menu option.
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Eric Kavanagh: Yeah. And one thing I will say too, and we haven’t really talked about this
at all so maybe we can dig into it during the roundtable, but there is of course the whole data layer
underneath the surface layer, the UI, that feeds and enables the true consumerization of deep BI, of
drill-down capabilities and so on and so forth. It doesn’t just get there; you can’t just
get there by throwing a nice UI on something. You have got to have some kind of a data layer
underneath. I know obviously Tableau has got an In-Memory database underneath there now.
Kim, I know that you guys have done a lot of work around enabling people to access different data sources
and make that sort of last mile much easier and really enable companies to lean into the data, because you
have to have some kind of architecture underneath. I will throw this out to you Byron and then we will
go to the roundtable in a minute, you have to really have thought about all the complexity underneath in order
to enable the simplicity on top, right?
Kim Theis: Certainly of course. I mean some of it is IT’s doing in the old style, building
data warehouses and such. We have added a new approach that helps to complement that of data mashup, so
really pushing more self-service. And as for Tracie’s point earlier, with the do-it-yourself
attitude that people take to analyzing information, they want to be the one controlling. They
don’t want to send a request down to the modern day typing pool and then wait for the answer to come
back from IT. They want to be able to analyze the data, ask those iterative questions that Francois was
mentioning before, and for that you need the strong back-end to handle Big Data, to handle data that’s
outside of the system, to be able to mash-up information coming from various sources. It's all critical
for this whole process to work.
Justin Kern: Ownership too, though. I mean, obviously, you want your people who are interested in data
and are able to tap into different aspects, different types of reporting or remote access, you want them to be
able to do that. But IT has its governance mandates and those types of things as well. I
don’t know if Kim or someone else wants to chime in maybe on some challenges or suggestions on dealing
with the ownership side of the consumerization equation.
How Has Personalization of BI Dashboards Improved Recently?
Personalization of Business Intelligence (BI) dashboards has seen significant advancements recently, largely driven by the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These technologies enable BI platforms to automatically tailor dashboards to individual users based on their roles, preferences, and past interactions. AI algorithms analyze user behavior and data usage patterns to deliver personalized content, ensuring that users have quick access to the most relevant data and insights. This level of personalization not only enhances user experience but also boosts productivity by reducing the time spent searching for information.
Another major improvement in personalization is the incorporation of natural language processing (NLP). Modern BI tools now allow users to interact with dashboards using natural language queries, making data analysis more intuitive and accessible. Users can simply ask questions in plain English and receive answers in the form of visualizations and reports customized to their needs. This feature democratizes data access, empowering non-technical users to engage with data analytics without requiring specialized skills, thereby fostering a data-driven culture across the organization.
Personalization has also been enhanced through adaptive learning algorithms that continually refine the dashboard experience. These algorithms track user interactions and preferences over time, learning from each engagement to provide increasingly tailored insights. For example, if a user frequently analyzes sales data, the dashboard will prioritize sales metrics and related visualizations. This dynamic adaptability ensures that the most relevant data is always at the user's fingertips, facilitating quicker decision-making and more informed strategic planning.
Integration with other enterprise systems has further improved the personalization of BI dashboards. Modern BI platforms can seamlessly connect with Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and other business systems to aggregate and display data in a coherent, user-specific manner. This integration provides a holistic view of operations tailored to each user's specific context. For instance, a sales manager might see a dashboard with real-time sales performance, customer interactions, and supply chain metrics, all in one place, providing comprehensive insights that are directly relevant to their responsibilities.
Finally, mobile optimization and responsive design have made personalized BI dashboards more accessible than ever. Users can now access their customized dashboards on various devices, including smartphones and tablets, without compromising functionality or usability. This flexibility supports the modern workforce's need for mobility, enabling employees to stay connected to critical insights and make data-driven decisions on the go. Personalized alerts and notifications further enhance this capability, ensuring that users are promptly informed of any significant changes or opportunities that require their attention. These advancements collectively contribute to a more efficient, effective, and personalized BI experience.