The speed at which data comes into organizations is fast and furious. End users of business applications must be able to leverage this data to extract insights and make data-driven decisions. How can software teams help users achieve this?
We recently hosted a webinar with Dr. Kirk Borne, Principal Data Scientist at Booz Allen Hamilton, to learn how embedded analytics is boosting data-driven applications.
Logi Analytics: How should companies approach embedding analytics?
Dr. Kirk Borne: When you’re faced with all that data, you might conclude that you need to take a data-first strategy in your business. I would like to suggest that that’s not the right approach, and in fact, the three natural reactions to this myth are what I call the “three F’s”: fear, fragility, and friction.
The first one: How do we get people over the hurdle of this fearful thing like data? The second one, fragility, is pertaining to the talent. If all the work is being done by professional data scientists, and there’s huge demand for data scientists in the world, those data scientists may go elsewhere. What happens if our experts leave? The third natural reaction is friction: How do we actually enable this stuff when there’s so many different tools, techniques, technologies, and even talents? Resistance comes from trying to pull all these different pieces together, and the biggest part of the friction challenge is the context switching.
How do we avoid the “three F’s?”
Instead of having these tools being at the forefront, embed those technologies within the existing business tools. Then people are not necessarily seeing those technologies that might scare them, but instead they’re doing things in their familiar environments, but with greater power. Embed these technologies within existing tools, and that makes it easier to drive that value from the data.
And by doing this—embedding and enabling people who might not otherwise be inclined to go learn the coding—you actually reduce the fragility problem. You created this culture where everyone can use the data, access data, and make decisions from data.
Finally, this data-first strategy is not the right approach because data is the input. The fundamental output of all this stuff is the analytics—the outcomes. Focus on those outputs, the mission, the business outcomes, and that will lead to value creation. This allows us to avoid those three F’s, with this analytics-first strategy.
How does an organization take the analytics-first posture?
Another way of describing that is “analytics by design.” How do we set ourselves up to be thinking this way? Analytics first means focus on the products, the outcomes, the mission. So, really the mission is the gold standard. The mission of engineering is that it’s designing a system such that the ultimate victory is that you win the mission.
Sometimes companies have to invest a significant R&D fund, and so they’re actually reducing their profit for the year, and maybe the shareholders don’t like that, but that investment will ultimately turn into a better win in the future. And the good thing about that is that it really helps focus the messaging and the culture to be really, truly aligned with what matters the most, which are the business outcomes and business mission.
Every organization has tons of data. That doesn’t make you distinctive in the marketplace. So, the goal is to deliver the innovation and value from the products of our data activities. And these successes, returns on innovation, and value creation are going to inspire more people across the organization to start thinking about data and digital assets in an analytical way. This will inspire that cultural change that helps larger implementations to come in the future.
How can we help data move “at the speed of data”?
I’ve heard organizations say in conferences for many years, “Our data analytics has to move at the speed of business.” And I say, “That’s just wrong because analytics and data are moving much faster than your business, so you’re going to have to slow down your data.” So, what you really need to do is help your business move at the speed of data.
It’s not the mission to produce more data. We have to move our business at the speed of all the incoming data, because the data flows through organizations fast and furious. Our business analytics users want to leverage the data, and leverage the tools, and they want to do this in a way that reduces the fear, fragility, and friction.
In this case, this is exactly where embedded analytics comes in handy. If a user has to go in and out of multiple different tools, it becomes almost impossible to be productive, efficient, and effective in generating value for their organization. So, embedded analytics boosts both the agility and the ability of organizations to extract that big value from big data.