Speaking at a data conference is hard.
Programming data conferences is hard. It’s damn hard.
It’s hard to predict who’ll show up in your audience.
It’s hard to predict if what you’ve planned to say will align with your audience.
It’s even harder to predict if who you’ve chosen to talk will align to who is likely to show up.
Image below most certainly related.
I’ve had incredibly patient mentors when it comes to this. And I’m still optimizing. And I still find it tough.
Heuristics for speakers:
Shilling doesn’t work
You never close a sale during a presentation. Worse, you turn leads off. Putting your ad first puts the audience last.
If it’s about causing awareness of your product or chosen service, that can be achieved by producing a great story that’s likely to resonate. You can butter up the room with a great story, finish with a takeaway, and then making it really clear and clean how people can contact you.
Leaving your contact information up, and letting it linger during Q&A, is effective.
They won’t remember 80% of what you say, they’ll remember 100% of what they feel
Special attention must be taken to your top 3 takeaways. These three items get recorded in notebooks and are commonly recycled for lunch and learns back at the office. The more concise and explainable they are, the better.
More than 80% of the audience won’t remember what you said. They can’t possibly.
But 100% will remember what they feel.
If you cause them to feel inspired, that something is possible, that a new opportunity has arisen – if you’re credible while doing it – you can leave an impressions. You’ll cause greater recall of your three main bullet points.
This can be done with imagery on slides. It can be done with the intonation of your voice. It can be done with the stories you choose to tell.
Leave them with a feeling.
Assume that 15.9% have no background in the topic, 15.9% are experts, and that 68.2% know a mix of nothing and everything.
Spend no more than 15.9% of the time on landscape. If other speakers have already talked about landscapes, adjust your talk. Spend even less time on landscape. Spend more time on the meat of your talk.
Bring everybody along. Start with simple ideas and stories. Build from there.
It’s an open question on how much time do you spend with experts.
I haven’t nailed this.
This may be nailed by embedding a tertiary track into the slides. (But I don’t know.)
If you’ve figured this out, please tell me.
Heuristics for (Data Conference) Programmers:
Shilling doesn’t work
Audiences love success stories. Audiences love hearing about successes that are repeatable thanks to a specific vendor technology. Those types of stories really work.
Commercials should be restricted to 15 minutes. And make it clear that those are commercials.
Thank the sponsors for their commercials.
Lots of introverts
Many practitioners that work with data are introverted. They skew introverted. Many of them have not pursued a life of public speaking.
Most have not pursued public speaking training.
And public speaking unto itself is hard. Nobody likes to set themselves up for such massive public failure.
Unlike other types of conferences, you’ll have to work pretty hard to get the practitioners to step forward and tell their stories.
It’s worth digging.
Panels are great because they distribute risk.
Panels are bad because they can distribute too much risk.
Panels can cause interesting people to appear boring.
Panels can cause interesting people to appear far more interesting than they really are.
But they’re wickedly hard to plan for.
Do bring out the best in each panelist.
Don’t promote a monkey knife fight.
Do enable spirited debate.
Controversy is good.
If you’ve figured out how to nail this, please tell me.
Audience and Speakers
Speakers should be briefed on who the audience is designed to attract, and their general knowledge level.
Audiences are more than brand names and titles.
It’s 50/50 whether or not a speaker can or will adjust their stories based on the audience.
I haven’t nailed this. If you know how, please tell me.
Programming data conferences is damn hard.
And I salute those who speak, and those who program.