I’m not going to talk about columnar databases, compression, horizontal partitioning, SAP Hana, or real-time vs. pre-aggregated summarization in this post on in-memory analytics. I’m going to talk about the other kind of in-memory analytics. The kind that can make or break your career.
What do you mean, the other kind of in-memory analytics? Quite simply, the kind you keep in your head (i.e., in human memory). Or, better put, the kind you should be expected to keep in your head and be able to recite on demand in any business meeting.
I remember when I worked at Salesforce, I covered for my boss a few times at the executive staff meeting when he was traveling or such. He told me: “Marc expects everyone to know the numbers, so before you go in there, make sure you know them.” And I did. On the few times I attended in his place, I made a cheat sheet and studied it for an hour to ensure that I knew every possible number that could reasonably be asked. I’d sit in the meeting, saying little, and listening to discuss not directly related to our area. Then, boom, out of left field, Marc asked: “what is the Service Cloud pipeline coverage ratio for this quarter in Europe?”
“3.4,” I replied succinctly. If I hadn’t have known the number I’m sure it would been an exercise in plucking the wings off a butterfly. But I did, so the conversation quickly shifted to another topic, and I lived to fight another day.
Frankly, I was happy to work in an organization where executives were expected to know — in their heads, in an instant — the values of the key metrics that drive their business. I weak organizations you constantly hear “can I get back to you on that” or “I’m going to need to look that one up.”
If you want to run a business, or a piece of one, and you want to be a credible leader — especially in a metrics-driven organization — you need to have “in-memory” the key metrics that your higher-ups and peers would expect you to know.
This is as true of CEO pitching a venture capitalist and being asked about CAC ratios and churn rates as it is of a marketing VP being asked about keywords, costs, and conversions in an online advertising program. Or a sales manager being asked about their forecast.
In fact, as I’ve told my sales directors a time or two: “I should be able to wake you up at 3:00 AM and ask your forecast, upside, and pipeline and you should be able to answer, right then, instantly.”
That’s an in-memory metric. No “let me check on that.” No “I’ll get back to you.” No “I don’t know, let me ask my ops guy,” which always makes me think: who runs the department, you or the ops guy — and if you need to ask the ops guy all the numbers maybe he/she should be running the department and not you?
I have bolded the word “expect” four times above because this issue is indeed about expectations and expectations are not a precise science. So, how can you figure out the expectations for which analytics you should hold in-memory?
- Look at your department’s strategic goals and determine which metrics best measure progress on them.
- Ask peers inside the company what key metrics they keep in-memory and design your set by analogy.
- Ask peers who perform the same job at different companies what key metrics they track.
- When in doubt, ask the boss or the higher-ups what metrics they expect you to know.
Finally, I should note that I’m not a big believer in the whole “cheat sheet” approach I described above. Because that was a special situation (covering for the boss), I think the cheat sheet was smart, but the real way to burn these metrics into your memory is to track them every week at your staff meeting, watching how they change week by week and constantly comparing them to prior periods and to a plan/model if you have one.
The point here is not “fake it until you make it” by running your business in a non-metrics-focused way and memorizing figures before a big meeting, but instead to burn the metrics review into your own weekly team meeting and then, naturally, over time you will know these metrics so instinctively that someone can wake you up at 3:00 AM and you can recite them.
That’s the other kind of in-memory analytics. And, much as I love technology, the more important kind for your career.
(Cross-posted @ Kellblog)