The last time I talked with Yellowfin CEO Glen Rabie, we dug into the pitfalls that lead to poor BI decisions - and how to avoid them (Misinterpreting data comes with a price tag - Yellowfin's CEO explains why generic BI projects are off the table).
Things change quickly. In the last piece, data storytelling seemed like an afterthought. I can't make that mistake again - not with Rabie reporting that Yellowfin Stories is "really starting to resonate." How so? As Rabie told me:
We're winning a lot of deals because of Stories lately.
But what is effective "data storytelling"? Is it just a buzzphrase that every BI vendor is cozying up against? Or is it an essential capability for business users? We'll get to that, but first, Mr. Rabie, give us your Yellowfin update. Rabie:
We've really settled in to this idea around the business user experience, as a broad way of encapsulating what's probably wrong with the BI market in general, which is: everyone's always thought and focused on the data analyst.
The BI problem we have to solve
So what's the issue with the data analyst? Well, that's the BI super-user problem. Your data analyst might love your software, but does your business user? Rabie:
The primary consumer of data in organizations is businesses, so how do they want to experience data consumption? And how do you enable the people who build those analytical products? So the data analysts, the developers, the data engineers, and everyone else in the mix - how do you help them to build these exceptional experiences?
Yellowfin has its mission:
As an organization, we're very much fixated around that idea - and solving that problem. As a result of that, it's really quite an exciting time. After all these years, I think we've really nailed the problem we're trying to solve.
The fundamental problem with this industry has been - and always has been - the low-end user adoption. As a result, people's perception of the ROI that they get from reporting and analytics, etc. That is our number one primary purpose in life: to increase business user adoption.
But data complexity keeps Rabie humble:
Lots of people come out and go, 'We've got the silver bullet, blah, blah, blah,' and there's actually no silver bullet in this industry. It doesn't exist. You have to acknowledge the complexity that exists, and then work terribly hard to simplify that.
Now we're getting to the heart of it: you're nowhere in BI without business user adoption. Because mandating tools usage doesn't work, that adoption is tied to the user experience. Complexity is enemy number one:
That's really the problem that we're solving - reducing the complexity of analytics for end users. So that it is consumable; it is easy to use.
One excuse BI vendors trot out: when data literacy goes up, adoption will go up. Rabie thinks that rationale lets tool providers off the hook. To be specific, Rabie says that rationale is "complete rubbish."
People don't use BI tools for a whole lot of reasons. And one of them - the primary one - is not because they're not data literate. They don't use them because they don't meet their needs.
How do we define an exceptional BI user experience?
This is a crucial twist. Last time, we focused on better decision-making. But without BI adoption, there is no informed decision-making. But that surfaces a new question: how do we define a good BI UX? Rabie:
My favorite one is that you don't even know you're using it. You're consuming data without thinking about it. We point to data journalism as a really good example of that, where you have rich stories that include a lot of data, but people consume both the narrative and the data at the same time, and the data supports the narrative. [Author's note: I've linked to a couple terrific examples at the end of this piece]
In case it isn't obvious, yes, Rabie is criticizing the BI dashboard - or at least exposing its limitations. Dashboards need context. Rabie explains:
The dashboard generally is the thing that is just the data, but when the data takes a little bit of a backward step, and it just plays a supporting role, that becomes something really nuanced. People consume it without even knowing about it. They don't think about it; they just see it.
One of Yellowfin's core use cases is embedded analytics. No surprise, then: Rabie sees that as great BI UX.
The other way you can consume data without really thinking about it is when it's completely embedded into a business process, or an application that you're using.
Your online banking, when you look at your credit card statement, and it tells you that you spent 30% of it in bars and restaurants. It's embedded in the context of what you're doing, and it supports what you're doing. It becomes just background, and you absorb the data.
Rabie's next BI UX criteria might be the most important of all. The data better be actionable:
The data product you're using [must] support your ability to take action. We always talk about decisions, but decisions are strategic. At some level, I want to analyze my business, or understand how my business is going - and therefore make some long-term strategic decisions. There's that kind of decision-making.
But there's a lot of operational decision-making that happens with data. If I'm managing, say, my sales organization and I want to track it, are we having a conversation daily or weekly about the deals we've got coming in? I want to be able to use the data to support that conversation, but also to immediately update my CRM tool. And so how does that happen? How do you bring those two together? By enabling actions within the data.
Data storytelling - why does it matter, and why now?
So where does that leave us with data storytelling? Or, to sharpen the question: why is Yellowfin's approach to data storytelling getting traction? Rabie says that historically, the data storyteller was the data analyst, or the BI super-user, if you will. But it's the business user who has the domain expertise to tell the data story:
The true expert in the business is actually the business user... That's what good data storytelling is about: giving the business user the voice to be able to put the context around the data, and give that story.
Why is Yellowfin Stories taking hold now? Rabie:
I think people just get it. Maybe because we are focusing on that business user a lot more, it just resonates with them. It's something that they go, 'Oh, my God, I can own that. I don't need to have the propeller head-on. I can take and run with and own this, and tell my story.'
Leading up to this piece, I picked up anecdotes of business users drawing on Yellowfin Stories to publish data-infused blogs on LinkedIn, and elsewhere. That's not something you hear about often. Rabie:
That's probably because it is the least technical part of our whole product. It's as simple as writing a blog. I think that's probably why it really resonates with the non-technical audience. Because they see it as 'That'll be me. I'm not just going to be the passive consumer, I'm actually going to be engaged and active in this product.
Look, diginomica readers know that I instinctively push back against phrases like "data storytelling" that tend to be both fuzzy and, let's face it, over-flogged by marketers. For another reality check, I'm looking forward to talking to Yellowfin customers about Stories, and hearing directly on their impact.
That said, putting intuitive tools in the hands of business users is always a worthy goal. Aside from data integrity, adoption might be the most important BI factor. I like how the storytelling model helps us move beyond the dashboards argument. Some companies swear by dashboards, others don't. Understanding how data needs context helps to explain why dashboards can fall short.
Data storytelling only works if you are prepared to alter your narrative when fresh data disrupts the story. It's easy to get fixated on a feel-good corporate narrative, example: "retail is back in growth mode again," or, my recent spleen-vent, "on-the-ground events are safe again," and then fixate on data that supports the narrative, perhaps ignoring the upheavals looming in the background. That goes back to Rabie's early point: there is no BI cure-all.
However, there are better approaches. That includes Yellowfin Signals, an automated analysis solution we discussed last time - one that continues to be a good news story for Yellowfin. As Rabie explained, this is another way Yellowfin seeks to speed up time-to-decision, while reducing the strain and dependence on data analysts: "I don't have the time to hope that my data analysts find something for me. I need to know what's happening at a really low level in my organization very quickly."
Yes, most tools in the BI market are not business-user-friendly. However, there are a few. Rabie pushed back on this point, however. He believes this is not Yellowfin's only differentiator. As he sees it, Yellowfin's entire go-to-market is different than others, with an emphasis on embedded analytics, partner-built, industry-focused BI apps built on Yellowfin's platform, and a go-to-market that includes white label OEM. I always prefer a strong, defensible niche to a generalist play, especially for upstart vendors with tech chops.
Yellowfin is not the type to take the foot of the gas. On the fall agenda: the release of Yellowfin's NLQ (Natural Language Query) solution. As expected, Yellowfin says they are going about this differently, and will have a superior approach to other NLQ BI tools. That's worth a closer look, but given the length of this article, that will have to wait. We can't wait too long, however, as Rabie says this will be in General Availability by the end of October. Watch this space...
End note - a couple of my favorite pieces of data journalism: The Stories Behind a Line, which charts the migration paths of six asylum seekers who traveled to Italy. Also, the New York Times opinion piece, Can You Live on the Minimum Wage? The built-in calculator provides the challenge to find out.