Bad Big Data? Get More Data Then
March 2, 2017
I like the idea that more is better. The idea is particularly magnetic when a company cannot figure out what it’s own, in house, proprietary data mean. Think of the legions of consultants from McKinsey and BCG telling executives what their own data “means.” Toss in the notion of a Big Data in a giant “data lake,” and you have decision makers who cannot use the information they already have.
Well, how does one fix that problem? Easy. Get more data. That sounds like a plan, particularly when the professionals struggling are in charge of figuring out if sales and marketing investments sort of pay for themselves.
I learned that I need more data by reading “Deepening The Data Lake: How Second-Party Data Increases AI For Enterprises.” The headline introduces the amazing data lake concept along with two giant lake front developments: More data and artificial intelligence.
Buzzwords? Heck no. Just solid post millennial reasoning; for example:
there are many marketers with surprisingly sparse data, like the food marketer who does not get many website visitors or authenticated customers downloading coupons. Today, those marketers face a situation where they want to use data science to do user scoring and modeling but, because they only have enough of their own data to fill a shallow lake, they have trouble justifying the costs of scaling the approach in a way that moves the sales needle.
I like that sales needle phrase. Marketers have to justify themselves and many have only “sparse” data. I would suggest that marketers have often useless data like the number of unique clicks, but that’s only polluting the data lake.
The fix is interesting. I learned:
we can think of the marketer’s first-party data – media exposure data, email marketing data, website analytics data, etc. – being the water that fills a data lake. That data is pumped into a data management platform (pictured here as a hydroelectric dam), pumped like electricity through ad tech pipes (demand-side platforms, supply-side platforms and ad servers) and finally delivered to places where it is activated (in the town, where people live)… this infrastructure can exist with even a tiny bit of water but, at the end of the cycle, not enough electricity will be generated to create decent outcomes and sustain a data-driven approach to marketing. This is a long way of saying that the data itself, both in quality and quantity, is needed in ever-larger amounts to create the potential for better targeting and analytics.
Yep, more data.
And what about making sense of the additional data? I learned:
The data is also of extremely high provenance, and I would also be able to use that data in my own environment, where I could model it against my first-party data, such as site visitors or mobile IDs I gathered when I sponsored free Wi-Fi at the last Country Music Awards. The ability to gather and license those specific data sets and use them for modeling in a data lake is going to create massive outcomes in my addressable campaigns and give me an edge I cannot get using traditional ad network approaches with third-party segments. Moreover, the flexibility around data capture enables marketers to use highly disparate data sets, combine and normalize them with metadata – and not have to worry about mapping them to a predefined schema. The associative work happens after the query takes place. That means I don’t need a predefined schema in place for that data to become valuable – a way of saying that the inherent observational bias in traditional approaches (“country music fans love mainstream beer, so I’d better capture that”) never hinders the ability to activate against unforeseen insights.
Okay, I think I understand. No wonder companies hire outfits like blue chip consulting firms to figure out what is going on in their companies. Stated another way, insiders live in the swamp. Outsiders can put the swamp into a context and maybe implement some pollution control systems.
Stephen E Arnold, March 2, 2017
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