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Over the last year, I have been very fortunate to meet more than a thousand Salesforce Einstein Analytics customers who have attended Analytics Customer Campfires around the world. Listening to customers’ use cases and empowering our Analytics Trailblazers while sharing the product team excitement and commitment has truly resonated with the customers — true to the Salesforce statement that we are but one big Ohana after all!
I kick off each Day 1 of these campfires focusing on empowering attendees with the knowledge of the Analytics landscape beyond the day to day tasks and requirements — what is needed for a successful start to their Analytics journey to drive usage and adoption. This conversation has morphed into 5 tips that should be pinned down as a daily reminder on what analytics trailblazers need to incorporate or touch upon, and it recently occurred to me it’s a simple acronym: CASES!
Yes, simply CASES for tips on Collaboration, Actions, Self-service, Embedded analytics, and Smart design.
Collaboration has become an integral part of analytics deployment the same way it has been for the rest of the business system and workflow processes. There are stand-alone analytics vendors out there trying to shift product and engineering resources to get on the collaboration check point list, but to have it right there in the business user’s day-to-day native Salesforce chatter is simply huge (just think of how fast and connected posting an insight on Chatter is to collaborating on a business issus, followup or decision making).
Einstein Analytics leverages Salesforce native action framework to take an action right HERE, right NOW on the analytics insight. This brings the term “Actionable Insights” to a whole new level. For Salesforce users, this is exemplified in the usability feel of analytics being an Application rather than a Dashboard - something the Analytics Industry has been pushing for a lot lately (see tip # 4)
Seriously, remember the old days when you were about to leave work or run to that after work social event just to get a last minute requirement to change the group on a chart? Or add a filter? Or even simply reverse the sorting? Well no more of that with exploration enabled on the widgets (ex. charts) which allows the end user to explore analytics (group by, filter, sort, measure, chart mode, etc ...) all the way down to the lowest grain in the dataset. Stuck on the road with an iPhone? No problem - explore the same!
As an icebreaker, I give attendees a tip to remember the next time they are interviewing for an analytics job. When the interviewer asks “how do you plan on building dashboards?” they should reply back with “You mean building apps — because dashboards are so 2000.” This will definitely get you the job!
When your analytics is:
...then better decisions and higher adoption will follow naturally. This is another example where many analytics vendors are scrambling to increase the extensibility of their product as “Embedded Analytics” and “Building Apps not Dashboards” is starting to gain momentum on Main Street (for Einstein Analytics users, this is so 2015!)
This might be the hardest of the tips to implement as it does require some investment in design layer skill sets — along with some complementing data layer skill sets — but the main idea is to keep it simple, informative and interactive. Users don’t want to be bombarded by colors, different chart shapes nor the same monotonous ones. Uncluttered design means every title, every txt or info display should serve a purpose of informing rather than just repeating the obvious. Additionally, there are some features in Einstein Analytics that work beautifully for a smart design layout (like one selection rendering multiple dynamic selections across different components).
One jumpstart on this tip is to check out the Einstein Analytics Sales and Services apps for starters; these apps have a lot of UI best-practice design tips with expert UX team approaches implemented and they can serve as dashboard templates to reverse engineer or build upon. Even the work at the data layer is good to analyze (some truly powerful data flow implementation is done there) to simplify design layer requirements.
These tips, CASES, should be put in focus, and it’s the job of our customers’ Analytics Trailblazers to use, implement and educate their end users on them to drive success, adoption and recognition for the work put out by the trailblazers themselves.
I hope CASES will be helpful for those who work in Analytics, and I want to thank our customers, and each and every Analytics Customer Campfire attendee for allowing us do what we enjoy and enjoy what we do, as we continue on towards another year of working together as one big Ohana family on the road to success and more Analytics endeavors!
Ziad Fayad is Senior SA and Evangelist, Analytics Cloud.
