There is no denying that data science helps with online content but for many and most content publishers is often unattainable with their current data sets. Vanity metrics may be readily available but are less flexibility. For instance vanity metrics may not be able to tell you about unique users. Aggregated pageview counts are generally not enough to demonstrate growth and stickiness. Beware of Vanity Metrics (HBR) can also point to the other pitfalls of relying on counting beans on the surface. Most likely, medium to large content publishers are moving from or adding metrics to WordPress plugins or Google analytics tools.
Originally posted on LinkedIn Pulse on January 4, 2016.
Social Media Week Chicago 2015 in November featured a master class on business intelligence using social analytics. The three speakers on the panel were from start-up fashion publisher (Clique Media Group, Inc. or CMG), digital marketing agency (Tenthwave Digital), and retail pharmacy chain (CVS Health). The panel discussed ways to leverage social media to make business decisions, gain customer insights, and stay competitive. Insights in social media in this class were leveraged from Digimind’s social listening and analytics platforms, which are known as software as a service (SaaS) products. Read the complication of tweets and panel notes in this storify link. I was invited as a panelist to share successes at CMG, my slides can be viewed here.
Data engineering is a crucial subset of the data science toolbox because access to data is required for high quality analysis and story telling. A data scientist must understand the tasks and time required for data engineering and be prepared to roll up her sleeves, which may include hammering out low-level scripts or developing company-wide software to create a fully functional data science environment. The data can set up successes or failures at an organization. A data scientist’s work is only as good as her access to data. This posts provides questions to evaluate time requirements for engineering the ideal environment, data access, and resources?