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Theme that emerged in this week’s email is … cost optimization projects get the funding; AI needs good data and we are seeing real companies implement AI in ways that is impacting their day-to-day (cost savings and otherwise).
QUOTE:
“Data leaders are tasked with adding value to the business, and avoiding unnecessary costs absolutely falls within that purview. Optimized data platforms are performant as are the teams that make it a habit to design and redesign for cost.” – Michael Segner of Monte Carlo
News Articles
Podcasts
Cool Charts
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Final Thoughts (The math of sales)
#1 – LinkedIn published How do you select data sources for AI?. July 2023.
My Take: This was one of those new collaborate articles where community members can add to the discussion. I believe the owners of robust, first party, dataset will benefit from the growth in AI. Good data will be valued at a premium. If you can deliver clean, complete, consistent data to builders of AI models, you will be well-positioned.
#2 – Michael Segner of Monte Carlo published Data Optimization Tips From 7 Experienced Data Leaders. July 2023.
My Take: Here are the seven tips:
Cut costs, not business value
There is more low hanging fruit than you think
Get granular
Incentivize those closest to the costs
Make data optimization a continuous process
Understanding dependencies
A penny saved is a penny earned
#3 – Three Data Point Thursday published Why Every Business Should Become A Data Business. August 2023.
My Take: You need to use data more effectively in your business (whatever your business happens to be) … or risk a competitor doing it first. The flywheel: 1- make product better using data, 2- get more data as more customers use your product, 3- further improve product, 4- until you take over the market.
The third of the Three Data Point Thursday is about pricing and gave me a new book to read … Information Rules (from 1998!).
BONUS: Benn Stancil’s Will We Ever Have Clean Data? July 2023. “When people talk about data quality and reliability, they often implicitly frame it as an unambiguous fight against entropy. We win if we’re persistent, prudent, disciplined, and thoughtful; we lose if we are lazy, reckless, inattentive, or foolish. But we would never lose because we chose to.“
BONUS 2: Deloitte’s Fueled by better information: How investment management can fully embrace alternative data. July 2023. … This article made the rounds this past week.
What else I am reading:
Matt Ober’s Wealthtech is hard when Envestnet is the only public market comp. July 2023.
Xinhua published FAO, NSA join forces to strengthen agricultural sector through data initiatives. July 2023.
LinkedIn collaboratively published How do you evaluate the quality and accuracy of data after applying a data transformation tool?. July 2023.
Andrew Welsch published in Barron’s How Voya Is Blending AI With Humans to Pick Stocks. July 2023.
Doug Laney’s Your Vogue New Subsidiary: A Data Company. August 2023.
Source: Satyen Sangani of Data Radicals published Asking the Right Questions with Frank Farrall. July 2023.
My Take: Frank Farrall (unfortunately, no relation to the purveyor of this Alt Data Weekly) leads data & AI Alliances at Deloitte. Frank is in a great seat to witness how these new technologies are impacting real companies. His role is educating clients about these new technologies. Importantly, these are not theoretical discussions … they are looking to implement new tools with clients in ways that make a real impact.
Of most interest to me was the idea that clients are now coming to Deloitte asking how to implement these new AI tools (“iPhone moment”).
Frank makes the point that you have to have good data for AI to work well. The biggest challenge is getting data out of silos, still seeing data in a mix of on-prem vs cloud. The data then needs some level of explain-ability, or the ability to cite sources … data lineage & governance (see more centralized governance).
Lastly, I like the idea that data gains value when it is shared. This value accelerates when it is shared more widely.
Highlights (46-minute run time):
Minute 02:00 – interview starts, background & “what does Frank do?”
Minute 05:00 – how to keep up with all the change
Minute 08:00 – how do they make the decision to invest in new technologies
Minute 13:00 – what about “right now” makes this time particularly exciting (AI’s iPhone moment)
Minute 17:00 – intersection of data & AI
Minute 23:45 – prompt engineering discussion (telling AI what you want; lots of nuances)
Minute 30:00 – Frank’s perspective on the modern data stack (still interesting? bullish?)
Minute 32:00 – where are companies investing?
Minute 36:30 – which industries are investing now (data heavy, regulatory heavy)
Minute 39:30 – “data gains value when it is shared”
Minute 42:00 – what are you most excited about?
Source: Deloitte’s Fueled by better information: How investment management can fully embrace alternative data. July 2023.
This chart has generated a lot of attention in recent weeks. Let’s hope Deloitte is right in their prediction.
Source 2: Michael Segner of Monte Carlo published the annual data quality survey. Full results. July 2023. Monte Carlo commissioned the survey completed by Wakefield Research.
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Source: Several noted below.
“The thing that took me 20 years to learn: sales is more science and engineering is more art.” - Auren Hoffman
The more I learn, the more I understand the benefit of structure around your selling effort. Long gone are the days of arming a friendly salesperson with an expense account & target list.
You need a process that can be measured. Picking the right metrics can be hard, but worth spending time on to make sure you are measuring the right things.
What SAAS companies do well (Chris Orlob) … I think this applies to DaaS as well.
They get ultra-clear on their ideal customer profile. It's so easy to try to sell to everyone. And it's so counterintuitive NOT to if you're trying to build a big company. Future unicorns find their sweet spot, plus their 2nd and 3rd 'tiers.'
They get ultra-clear on their ideal REP profile. Before they scale and add headcount, they know exactly WHAT they want in AEs (and what they don't). They live the wisdom that success in one context doesn't automatically transfer to another.
They build a 'semi-repeatable' sales process before scaling. Repeatability is a little bit of a misnomer when scaling: you're never perfectly 'there.' But the best SaaS companies get their playbook to 'good enough' before they scale. Otherwise, you'll add headcount that doesn't know wtf they're doing.
They build a repeatable hiring process. Their hiring processes are 'boring.' They do the same thing every time with every candidate. Which gives them a gift: pattern recognition, and a crazy ability to identify whether someone 'fits' their ideal rep profile.
They maintain a 3:1 pipeline coverage ratio. They watch this metric closely. If most reps don't have at least 3x pipeline against their number, there's fixing to do before scaling further.
They ensure 80% of their reps hit at least 80% of their number. It's a little different in this economy. But if you're far below this? Scaling will accelerate expense without accelerating revenue.
They maintain a CAC payback period of less than 12 months and a 3:1 LTV to CAC ratio. This really should have been #1, because it's an indicator that you're product/market fit is ready for scale.
They build a structured onboarding program. You can't just throw new AEs into a sink or swim environment. Again, expenses will far outdistance new income. Bring new reps through a rigorous program so they can produce new ARR on a predictable schedule.
They don't sacrifice their bar for talent in the name of hitting headcount targets. This is arguably the hardest one, because the temptation to soften is so great. Better to leave an open territory unfilled than to fill it with someone you'll have to backfill later.