Clarity comes from interesting sources. My clarity comes from my tinkering with a snake cube – a simple 3x3x3 wooden cube chain. How are your puzzle skills?
I have been considering different approaches for capturing, filtering and modeling social media engagement data for some time. Like most other practitioners I talk with – we are generating more data than we can analyze. I have a stack of Short URL data, another with SEO data, another with Media data, another from Web Analytics, more from Social Media Analytics – Facebook or YouTube Insights, newly added Listening data, updates from CRM, with the newest and largest stack from Moderation. Certainly you may have more …. What do you do with your data stacks?
The burden of ownership of these stacks is that they are a combination of structured and unstructured data. We can neatly capture the structured data like time, date, profile name, network, location, browser type, tags, etc … The unstructured information is where many times the real insights exist. This is where we will find comments, blog posts, video responses, photos shared, etc … It is the balance of content with context that provides us with the real world relationships that describe the editorial measurement, community health or overall social engagement. This is the challenge of social media measurement as it is combines your web analytics standards with those of your social media data. O’Reilly Media (@oreillymedia)author, Sean Power (@seanpower) lead an Enterprise 2.0 Boston (@e2conf) session this week that aligned with my approach and offered a deep course in this integrated analytics or communilytics approach [see slide 95 to visualize this synthesis]. This brings the insights team off the sidelines and into the planning and delivery of the program goals.
Insights are in the eye of the beholder when it comes to measurement. The lens that I view these data stacks is very different than my colleagues that are observing from the lens of eCommerce, Marketing, Brand Manager, Public Relations, Customer Service, Investor Relations or Information Technology. Each functional organization has different data elements that can be organized to model a possible solution set relevant for their insights. This dynamic is what drives the collection of these data stacks but the hoarding of data does not benefit the enterprise if it is not accessible and flexible. Your collection of spreadsheets containing silos of data – only useful to the limited asynchronous owners. This model is single dimensional and focused on the structured data.
This brings me back to the snake cube. The view of this information reminds me of a project I worked on a project a few years ago where we modeled structured and unstructured video data to help inform a fantastic video search engine – dabble. The model we used was the OLAP cube to bring dimension to the data stacks we had maintained to develop new products with the core meta and search data. Capturing these data stacks as a cube offers as one of many forms of business intelligence to model your analytics. Bringing this agility to your data stacks to unlock insights will open the possibilities for you and other supporting organizations to develop formulas and benchmarks that bring the insights and evidence to fund, scale and better engage your communities. Embracing this flexible and business intelligence can move the social media efforts into the center of your brand planning rather than an after action review. Gatorade (@gatorade) and PepsiCo (@pepsico) has harnessed this approach to power the Zeitgeist and Mission Control center. Are you ready for your analytics and analysts to have a seat at your digital table?