I often ask marketing leaders how they organize their resources for social, and the responses are rarely the same. I hear everything from: "We have one person in PR who does social part-time" to "We have hundreds of full time social marketing managers across the globe." Despite this disparity, I find that marketers often share the same level of frustration when they try to advance their social marketing initiatives. Whether they have one social marketing manager or hundreds of social marketing managers, marketers claim that their existing resources are stretched.
Quantity does not equate to quality
Marketers tell us that a lack of dedicated employees is a big pain point. And if you dig a bit deeper, you will find that this is a daunting obstacle that prevents many organizations from scaling and optimizing their social marketing efforts. Marketers often feel that the only way to scale and optimize is to hire more social marketing managers. Yes, more dedicated headcount helps, but it is not the panacea. In order to be truly organized for social marketing success, you need a new perspective.
I’ve been presenting research on big data and data governance for the past several months where I show a slide of a businesswoman doing a backbend to access data in her laptop. The point I make is that data management has to be hyper-flexible to meet a wider range of analytic and consumption demands than ever before. Translated, you need to cross-train for data management to have cross-fit data.
The challenge is that traditional data management takes a one-size fits-all approach. Data systems are purpose built. If organizations want to reuse a finance warehouse for marketing and sales purposes, it often isn’t a match and a new warehouse is built. If you want to get out of this cycle and go from data couch potato to data athlete, a cross-fit data training program should focus on:
Context first. Understanding how data is used and will provide value drives platform design. Context indicates more than where data is sourced from and where it will be delivered. Context answers: operations or analytics, structured or unstructured, persistent or disposable? These guide decisions around performance, scale, sourcing, cost, and governance.
Data governance zones. Command and control data governance creates a culture of “no” that stifles innovation and can cause the business to go around IT for data needs. The solution is to create policies and processes that give permission as well as mitigate risk. Loosen quality and security standards in projects and scenarios that are in contained environments. Tighten rules and create gates when called for by regulation, where there are ethical conflicts, or when data quality or access exposes the business to significant financial risk.