Organisational theorist and Crossing the Chasmauthor Geoffrey Moore’s words are among the internet’s favourite pithy quotes on big data. Certainly, in the 21st century, to not harness university marketing data is to operate at a significant disadvantage.

We can play a little with this imagery to make a slightly different point, however. Imagine you were in the described situation, when suddenly hearing and vision were restored. Imagine, the sense of noise and confusion. Of not knowing which way to go, or what to do.

This, no doubt, describes the feelings of many a university marketer when first exposed to big data. We have all these numbers…but what do we do with them? How do we even read them?

Here are our five tips to avoid drowning in data:

University marketing takes place over a number of channels – each generating its own datasets. This can make it tricky to compare and contrast. You can make your life a lot easier by investing in software which pulls all this data into a single dashboard. Forbes columnist Kelly Richardson recommends Cyfe, Domo, and Looker.

Data analytics is a complex discipline, for which marketers simply may not be qualified. Get a team in-house who understand how to harvest data, how to pull reports, and how glean genuine insights. This can be the difference between drowning in data and surfing to success.

Big data can exacerbate the risks associated with working in siloes. If teams are not communicating, contradictions may arise, crucial insights may not be missed, and efforts duplicated. Well-intentioned teams may end working at cross purposes. The message is simple: collaborate and communicate.

Be very clear on the results you’re looking to achieve, and how exactly you can fairly measure success. Don’t be distracted by the wealth of data available – just because it’s there, doesn’t mean it’s all relevant. If you begin by focusing on key indicators, you can add complexity as you become more confident.

Big data offers unprecedented levels of insight. That does not mean, however, that we must be in its thrall. Data is essentially sterile – it’s what we do with it that counts. And what we do with depends on things like expertise, intuition, and inspiration. Things that not even the most sophisticated analytics program can offer.