As a long-time marketing leader, I remember needing to pull at least three people to help me connect my CRM data from Salesforce, then add in my sales leads data from Marketo and ad click-through data from Google Adwords and finally stitch it all together manually using hairy Excel files. Back then, I needed answers for my executive team faster than I could deliver them.
To speed up the insights we desperately needed from our data, I downloaded a free trial to a BI visualization tool. But, I still needed to pull in a colleague from the IT department and then brought in a services consultant to get my five data sources into one holistic view. With that cumbersome process and ten thousand dollars later, it still took three and a half weeks to complete. The end results were on par with a first-time archer’s sloppy performance at summer camp, when I needed the speed and prowess of Robin Hood hitting the bullseye and splitting his arrows over and over again.
The tools and resources I had to make sense of data were lackluster and the resulting insights lacked the depth we needed to make our best decisions. If I was underwhelmed, I could only imagine how my executive team was feeling. I know I’m not alone when I say that speedy analytics with depth is what so many senior business executives have lacked for a long time now. Why? Because delivering them requires finding more qualified people, more time, and accessing more data sources to get to the necessary “depth” quickly enough to make an impact. Business people are not data scientists nor ever plan to become one. We want answers fast, we want more information not filtered information, and we are clear on how we want information presented.
“We want answers fast, we want more information not filtered information, and we are clear on how we want information presented.”
There are now so many sources to pull information from; vast troves of digital data, internal data platforms, cloud applications, rich external data and the never ending flood of spreadsheets. To make correct and credible decisions at the speed of business necessitates accessing this wide variety of siloed data sources. So how do we do it?
Hello AI-Powered Analytics
Fast forward to 2018. I’ve turned to AI-driven analytics solutions. But I use ones with easy to use engaging user interfaces. With the right AI-powered analytics solution that is actually powered by machine algorithms, ML and AI, you can literally connect to four, five, or 10 sources of data, and get “connected insights” instantly, using just one person, or at most two. And that one person can be anyone; they do not have to be an expert data analyst. I wear many hats as a Silicon Valley marketing leader, but there are simply not enough hours in the day to do everything. Sometimes the role of “data technician” falls by the wayside. If I ever depend on a second person, it’s because that colleague knows the credentials to a data source that I need to connect to, but that’s really all I ask them for and then I’m off flying on my own.
Recently, I wanted to connect two CRM systems that we use with Google Analytics, a new contact names database call Discovery Org, another contacts source we were retiring, and three hairy Excel spreadsheets. I did it all in literally 14 minutes and it was so easy to accomplish.
AI-Powered Analytics for the Average Joe
Here’s how it went. Get the source credentials, point-and-click to connect all of them and let the machine-driven analytics solution discover all the data that I cared about. Then, all I had to do was a simple point-and-click blending across my five data sources. Then, instantly an engaging data storytelling interface appeared flush with insights that would have probably taken me days to uncover otherwise. I understand data, but I don’t write SQL. I don’t do scripting and I simply don’t have time to learn data modeling. But, I accomplished what I needed to do in 14 minutes.
The data story was narrated and beautiful. When I clicked within the interface, it automatically led me to all the “what” and “why” possibilities and showed me information that I would have never thought of on my own. The auto-discovery capabilities in the data story visualizations showed me a comprehensive set of revelations and descriptions that used to require weeks of many experts manipulating data to uncover. It was like basking in a Napa Valley sun of information, knowing I could navigate to answer any question on my own.
We are all in roles in business with burning questions everyday. We don’t have time to wait to get someone else to answer those questions, nor do we care to stand in line (the long IT queue) after the first swath of insights to get the next set of burning questions answered. We don’t just care to know the “what,” like what happened to the batch of sales leads, or what the ad spend is for last week. We want to know when, why and how the business was impacted. We want our data to tell a story in a way our peers from all sides of the business can understand. And, we want the story to have “depth” and resonance of a symphony orchestra led by a master conductor to deliver both clarity and inspiration from the insights.
“We want to know when, why and how the business was impacted. We want our data to tell a story in a way our peers from all sides of the business can understand.”
The great news is that modern analytic tools have come a long way. We have moved away from relying on experts, and reached a point of one person being able to do a whole lot of meaningful analytics, fully self-service.For business today, deeper discovery and speed to insights matters and answering your own burning questions is paramount. You can now create your own numbers-based bullseye data insights like a master archer within minutes.
Recently, world’s leading research firm, Gartner Research cited that modern machine intelligence analytics and BI platforms will deliver “twice the business value” over traditional BI and “Self-service and “AI-powered data analytics solutions will make up 80 percent of all enterprise reporting and analytics by 2020.”
“AI-powered data analytics solutions will make up 80 percent of all enterprise reporting and analytics by 2020.”
From my own experience, I’d say Gartner is dead-on right.