Demystifying Data Science, Part III: Data Wrangling

Reading Time: 6 minutes

Ask any Data Scientist and they will tell you that the process of ‘wrangling’ (loading, understanding and preparing) data represents the lion’s share of their workload – often up to as much as 80%. However, that number is not as alarming as it may at first seem. To understand why, let me tell you about my living room.

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Does your marketing need a customer graph?

Reading Time: 8 minutes

The relentless rise of social networks in recent years has made many marketers familiar with the concept of the social graph—data about how people are connected to one another—and its power in a marketing context.

Facebook’s social graph has propelled it to a projected annual revenue of around $40B for 2017, driven primarily by advertising sales. Advertisers are prepared to pay a premium for the advanced targeting capabilities that the graph enables, especially when combined with their own customer data; these capabilities will enable Facebook to snag over 20% of digital ad spend in the US this year.

Partly as a result of this, many marketers are thinking about how they can exploit the connectedness of their own customer base, beyond simple “refer a friend” campaigns. Additionally, it’s very common to hear marketing services outfits tack the term graph onto any discussion of user or customer data, leading one to conclude that any marketing organization worth its salt simply must have a graph database.

But what is a graph, and how is it different from a plain old customer database? And if you don’t have a customer graph in your organization, should you get one?

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