Forget about perfection
Your data (information) is a set of free-flowing, dynamic instructions about how business (or whatever) is understood. It will never be perfect. In fact, you don’t want it to be; if it’s perfect (which, again, is impossible) there’s no room for improvement or self-reflection. What’s more, it will lack creative impulse: you don’t think freely if something is ‘perfect’ and done.
Adapt with data
Data is fluid
The goal should be to bring the certification/governance process *to* the data. If you must wait, collect, meet, agree and on and on, there is a critical piece missing: data should be certified from what it produces (or its many derivations). How does this work? How can you certify a csv file? Simple: Alert, Integrate and Monitor your Analytics Infrastructure.
Essentially, if nothing is created from the data, why would there be a need to certify it? Once something is created, then you relentlessly certify, in flight, what is being produced. It’s a sort of fact-checking, data-alerting mechanism that’s completely possible with the right framework. Which leads to the next point…
Collect data about patterns of usage
If you’re not analyzing usage patterns, you’re missing valuable data. With all analytics, there are reasons for why (1) a specific set of data is selected and (2) what the user is attempting to do with the data. You can easily keep this metadata in AWS S3 (with a good lifecycle policy) or store for potential later use somewhere else. The point is that if you aren’t understanding *why* then you are only seeing one side of the coin.
Keep everything and then figure out what to do with it and then how to certify it.
Leverage the cloud
Don’t be constrained or afraid to combine pieces of cloud technologies to serve the Analytics structure.
Become durable / resilient
Just in case
Even though there are very high monthly uptime percentages, just be prepared for something to break. If you do that, you’ll have even *more* creative freedom (crazy, huh?).
Choose to: (1) scale laterally or (2) scale vertically
This is all about re-framing the question around Projects vs Sites.
Why you have Sites over Projects or Projects over Sites? And that can’t be the only choice, right? (Hint: it’s not the only choice)
I’ve seen benefits to both but the extra work involved with Sites make scaling laterally (Sites) much more difficult than vertically (Projects), not to mention the challenges of stepping into the compliance realm.
Remove all the pieces from your base install that can be done elsewhere (eg: collect the ‘garbage’ but store on AWS S3 with a good lifecycle policy). That way, your Analytics infra is light and fast.
I challenge you to think *bigger* with Tableau. How can you provide more fluid access to insight than anything else?
I’m calling it: 2017 will not confuse Analytics with Reporting
We’ve got too much technology, tooling, and components to mix the 2 realms (hint: they’ve never been related…the ‘self-service’ myth hasn’t really separated them quite yet ).
Analytics has depth and is fluid. Reporting is rigid and superficial.
Here is a small example of what I mean. Your #Fitbit is more than a report. Think about that and shake your, er, data-maker 🙂
Look for more on this and other tech bits this year.
Happy New Year!