Last weekend I realized I wanted something very specific: a great-looking lawn by mid-October that would require minimal effort from me. This meant that the output I needed to produce this desired outcome was the target date on which I needed to have aeration and over-seeding done in my yard. At that point, the analytical side of me took over, and I started thinking through the input variables that would affect this outcome such as: current lawn quality, soil composition, current ground moisture level, seed variety, and weather forecasts.
I could have created a model (or multiple models), collected relevant measurements and historical data, and simulated the next month a few thousand times to arrive at a best-odds forecast. My main barrier in doing that – in addition to competing Saturday afternoon interests like trail running or watching college football – was a lack of specialized knowledge and access to the right kind of algorithms and data. Whoever says a nice-looking lawn is left to chance is wrong – you have to be analytical when it comes to having a lawn that neighbors envy.
Organizations of all sizes and in all industries face this same dilemma every day. Do they invest in acquiring the assets and developing the competencies required to address specific business problems? Or do they have someone else do it for them, understanding that the economic terms for that arrangement may be quite unique?
In a world long on instrumentation, data, hardware, and software, but short on expertise in using those inputs in specific ways, it makes sense that more customers and providers are focusing on the concept of managed analytics services (MAS). The MAS model allows providers to add specialized knowledge or know-how and deliver that value to customers in a quick and repeatable way. Providers can price this service in a value-oriented way and, in some instances, can even share in the economic benefits delivered to their customers.
In exchange, customers get quick, accurate, and targeted outcomes for specific needs without having to invest money and time in resources (aka inputs), many of which can change rapidly over time. Customers who are willing to experiment with creative financial arrangements can also reap the benefits of pay-for-performance outcomes – essentially guaranteeing themselves ROI vs. some more traditional ways of contracting with service providers. Focusing on outcomes and MAS models makes operational and financial sense in so many ways, which is why many customers are now incorporating this model. In fact, Gartner estimates that by 2017 customers may have shifted as much as 50% of their sourcing portfolio to managed service models, like the one SAS just announced this week.
I’ll be in New York City in two weeks at Strata + Hadoop World. In that environment of machine-learning, Hadoop, data science, IoT, and other technology-focused topics, there will be many opportunities to discuss business problems that can be addressed via partnerships and MAS models. I’m looking forward to sharing some of my experiences there in my next post.
And, in the meantime, I’ll be relying on a MASP who can better address my lawn issues while I catch some college football over the next few Saturdays.