I spent a couple of days with thousands of SAS users this week at the SAS Global Forum 2009. There were some great sessions and, as usual with SAS, some terrific customer stories and I suspect I will write a couple of posts. This post, though, is about the theme – Leading with Confidence in an Era of Uncertainty.
The idea behind the theme, as I heard it, is that SAS delivers fact-based confidence for your decisions. Solid business analytics (of which more later) replace hunches with facts and take the guesswork out of decisions. While I understand that individual SAS users want to feel confident in their decisions, I think that the companies that use SAS want much more – they want accuracy, better decisions, optimal decisions. Sure, they want to be confident in them too but the confidence is secondary to the need for decisions that are materially, measurably, practically better. Leading with accuracy rather than with confidence.
Multiple speakers brought up the need to “understand data and information quickly” as though this was a business objective in its own right. But I don’t think it is. Businesses need to act on data quickly and accurately (there’s that word again). Understand.…
I spent a couple of days with thousands of SAS users this week at the SAS Global Forum 2009. There were some great sessions and, as usual with SAS, some terrific customer stories and I suspect I will write a couple of posts. This post, though, is about the theme – Leading with Confidence in an Era of Uncertainty.
The idea behind the theme, as I heard it, is that SAS delivers fact-based confidence for your decisions. Solid business analytics (of which more later) replace hunches with facts and take the guesswork out of decisions. While I understand that individual SAS users want to feel confident in their decisions, I think that the companies that use SAS want much more – they want accuracy, better decisions, optimal decisions. Sure, they want to be confident in them too but the confidence is secondary to the need for decisions that are materially, measurably, practically better. Leading with accuracy rather than with confidence.
Multiple speakers brought up the need to “understand data and information quickly” as though this was a business objective in its own right. But I don’t think it is. Businesses need to act on data quickly and accurately (there’s that word again). Understanding it is a critical step but not the payoff.
“Deliver the right information to the right person at the right time”. Well yes but why? So that the right decision gets made – that’s the purpose of it all, that’s what adds value to the business. So why not focus on making the right decision and if that means delivering information to the decision maker, great, make sure its the right information etc etc. But perhaps it means putting the right rules in the system or optimizing the constraints correctly or some combination of these things. Decision first, everything else only after.
Stephen Baker spoke about his book Numerati and one of his examples made this point, at least to me. He was talking about his own industry – media – and the challenges analytics are creating for it. In particular he used an example of ad pricing and the need to tie ad pricing to analytics about the impact of the ad. All true but companies just like the one he works for are automating ad pricing using rules (there are lots – color, size, scope etc) already. Using knowledge-worker focused analytics would let a few pricing analysts make analytically based decisions but the business cannot afford to go back to only having a couple of specialized pricing analysts who can calculate the price (that’s why they automated it, after all). Analytics should be fed into that process to alter/influence the pricing rules so that the automated decision is correct but getting this right is going to take more than just analytics, it is going to take decision management with rules and analytics.
Over and over I hear SAS customers talk about the great results they get when they put their predictive analytics to work in operational systems. They use the integration with Teradata, batch scoring, hand-coding of predictive models, loading SAS models into rules engines and more. They understand the power of predictive analytics to improve their bottom line by improving the operational decisions in their business. I am even certain that SAS understands this. Yet somehow this never seems to come up in the core SAS pitch and that worries me.
Confidence is not the issue, accuracy is. Information is not the goal, better decisions are.
