I think maybe. Like any tool though, wielded by many a practitioner, there are those who use it well and with good intent, and those that well, use it. I read the recent blog post by Vala Afshar titled ‘Why A Data Scientist Should Be Your Next Marketing Hire’ with great interest, as one of my major focuses in social media is big data. I was not disappointed! As marketers, I think we all understand the importance of making decisions based on data. Or, more accurately perhaps, based on INFORMATION. The distinction between data and information? Let’s save that for a future #SMXChat.
But one thing is for certain, data analysis, while a marketing-related discipline, takes much more technical expertise than the average marketer possesses. The principle is pretty simple: There is a wealth of information out there about how our customers search, discover and behave. And about their feelings, sentiments, and actions too! The key is capturing the right data and using it to provide actionable information that benefits all. What I mean by this is people are not necessarily opposed to companies collecting and acting upon information about them if it adheres to specific guidelines, such as relevance, timeliness, context, and alignment in purpose.
We all know that our data is being collected. And we sometimes must tolerate its use for seemingly selfish purposes. But what if we change the paradigm of data science? What if we are transparent about data collection and what precisely we hope to accomplish in finding out more about our customers (and potential ones)? Things like intimacy, trust, and respect. We can all relate to and get on board with that, right?
Q1) How do data science and marketing interrelate? Does data science and analysis fall under marketing?
Q2) Should companies and brands collect lots of data and sift through it later? Or collect only what they need?
Q3) Do companies and brands protect peoples’ private information appropriately?
Q4) Do you think companies and brands that collect data are transparent enough about thewir purposes in doing so?
Q5) Where is the intersection of marketing and data science? How should the two be managed together?