And I think that’s the mindset that a lot of us have had over the last 10, even like 20 years. I mean a
lot of these ad hoc query and OLAP tools go back to the early ‘90s, and this notion of ad hoc query and
OLAP is the sweet spot for BI. I mean I know I kind of grew up with that mindset that this is the sweet spot.
If we can only deliver ad-hoc query and OLAP, we will be able to avoid the problems at the extremes.
And the epiphany I had was that ad hoc query and OLAP is not the sweet spot. If we focus on ad hoc query and
OLAP, we overshoot our information consumers. No matter how easy we make it to build their own reports, they
don’t want to do it themselves. They want someone else to build reports for them, and really interactive
reports is the sweet spot for them.
Reports that were built where they can use prompts or parameters and drill in a very easy and guided way to
information they want to see, but it’s very much an information consumption model. They are not creating
their own reports. Along the same lines, if we are overshooting our information consumers, we’re
undershooting our power users.
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So, basically our application, as well as the Qliks and Spotfires and Tableaus of the world are hAnd we have
been giving our power users ad hoc query tools and OLAP cubes for 10 to 20 years now, which essentially is
putting them in a straitjacket where we are basically saying hey you can drill up and down these dimensional
hierarchies that are predefined, but you can’t go anywhere else. And that’s just too constraining
with these folks.
They want to play and explore, they want to blend different data sets together. Once they’ve mashed up
those data sets, they want to drill into those details of that data with unfettered drilling, which is why
they live out in that far right world, the dark side of data dumps in Excel and Access because that’s
where they have the freedom to do this.
And I think we need to recognize that there is a sweet spot for the power user that’s somewhere between
ad hoc query and OLAP and these data dumps in Excel and Access, and that’s creating these data discovery
tools where you do have the ability to blend data together from different data sources easily, you do have the
ability to drill into the details of the data without being bound by a dimensional, hierarchical drill path.
And you can do a lot of the calculations and customize fields and calculations that you want to build in
this data discovery world for power users to play in. I think the idea here is that we are blending this data
together, doing lot of this analysis and creating interesting insights that then feed the reports and
dashboards we give to our information consumers.
I want you to think about that as the process here, that the power users become the report builders with the
help and guidance of IT where necessary, and they build dashboards and reports that then get promoted to
information consumers. And we are going to talk a lot about that in some subsequent slides.
But the big epiphany here I think is that if you are just continually dishing out ad-hoc query and OLAP to
users and are frustrated that it’s not getting the adoption that you want, I think we need to recognize
that maybe we are digging in the wrong spot here, and maybe this is not the sweet spot, and we need to align
the right capabilities and tools for various constituencies.
Information Consumers And Power Users
And by the way, there are more than just two constituencies here. I think we are going to break down
information consumers and power users into more granular categories, but just for simplification sake, as a
bare minimum, identifying these two very distinct categories is key. One is an information consumer who just
wants to look at reports and dashboards that have been built for them, and the other is a power user who wants
to not only consume but create new analytical views on their own.
The difference between information consumers and power users lies primarily in their interaction with data, their level of technical expertise, and their roles within an organization, particularly in the context of data analytics, business intelligence (BI), or software tools.
1. Information Consumers
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Purpose: Information consumers typically use data for basic decision-making or for gaining insights that directly apply to their daily roles. They often rely on pre-made reports or dashboards created by others, and their primary need is to interpret or consume the information, not manipulate or generate new data sets.
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Technical Expertise: They have a low to moderate level of technical skill. Information consumers do not need in-depth technical knowledge or advanced analytical skills. They are familiar with using tools like Excel, reports, dashboards, or other user-friendly BI interfaces.
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Data Interaction: They interact with pre-built data visualizations, KPIs, or dashboards that have been configured by someone else. Their engagement is more about reviewing and interpreting the information presented rather than building the reports or performing complex analyses.
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Example: Executives or managers who review high-level business performance reports or dashboards to make strategic decisions.
2. Power Users
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Purpose: Power users are data-driven professionals who actively work to create, manipulate, and analyze data. They might work directly with raw data, perform ad hoc analyses, or build dashboards and reports that are used by others within the organization. Power users are integral in customizing data insights to meet specific needs.
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Technical Expertise: They possess high technical skills and have a deep understanding of the tools they use, whether it's a BI tool, advanced Excel, SQL, or even coding languages like Python or R. They are comfortable navigating through complex data structures and often engage in data modeling, querying, or data transformation.
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Data Interaction: Power users not only consume information but also act as creators and transformers of that data. They have the ability to customize, filter, and manipulate the data to get the exact insights they need. They may also be responsible for building tools, reports, and dashboards for information consumers to use.
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Example: Data analysts, business analysts, or data scientists who build complex queries, design interactive dashboards, or perform in-depth data analysis.
Key Differences
- Role: Information consumers use data for decision-making, while power users enable others by preparing and analyzing data.
- Complexity of Tools: Information consumers typically use simple interfaces, while power users engage with more complex tools and workflows.
- Data Access: Information consumers access filtered or summarized data, while power users work with raw data and can dig into details.
- Customization: Power users create and customize reports and dashboards; information consumers rely on these outputs.