Social Measurement: Love it or hate it?

A couple of weeks ago I was at IBM Connect, IBM’s Social Business & Smarter Workforce conference in Orlando, demoing my latest analytics project entitled “Engagement #Analytics: Increase Social Adoption & Business ROI”. As always it was a hugely valuable, if not insanely busy, week with great dialog and feedback from customers, partners, analysts, and press. So in the spirit of openness and sharing I thought I’d share some of the feedback and my observations with you.

  1. Social measurement generated very mixed opinions. Some people loved the idea and were hugely enthusiastic about concept of having greater transparency around their social footprint, while others viewed this with some suspicion particularly since we were demoing an employee scenario. In terms of breakdown l would estimate, and this is approximate as I wasn’t keeping a track of every opinion, that it was probably 60% for, 20% against, and 20% on the fence.
  2. Folks in general loved the simple look & feel (screenshot) and felt that it presented the analysis in a way that was easy to understand, with the purpose of each KPI clear and the evidence drill-down giving folks a sense of how the scores were being calculated and what was contributing to them. The biggest point of criticism was that we needed more capabilities in the dashboard to help with the journey of becoming more social, such as recommendations on how to be more effective, learning, feedback, etc. This was good feedback as these are all features on our TBC, we just wanted to take an Intrapreneural approach and get an initial MVP out there and then prioritize the next set of items in partnership with our customers.
  3. The actual concept of presenting a score was a very contentious point and strangely divided right down the middle. I’ve always been a bit on the fence about the concept of presenting an actual number vs. a form of bucketing, however wasn’t prepared for the level of emotion around this, on both sides of the fence. So I was very glad that we kept the numbers in this first MVP as it helped get us some great feedback. On one hand some folks absolutely hated, in the strongest possible terms, the idea of presenting numbers and felt that it was judgemental and a more qualitative measure would be better. However, on the other hand there were folks that vociferously argued for numbers and were even going as far as asking for color coding, which seems even more judgemental to me. The only conclusion that I’ve come to is that this number preference is very personal and likely to depend on the company, geography, or maybe industry in which the solution is deployed.
  4. Now privacy & ethics is on topic that also generated lots of discussion, with a majority of folks expressing some concerns about the privacy implications, although my fellow Europeans were definitely the most vociferous. On this point I’m very thankful that IBM had already taken the high road and we literally designed this solution in partnership with our colleagues in legal going through the privacy and ethical review process before we’d even finished the solution design. It was so the right way to go since we now have a solution that has put the rights of the employee first above all else, with the organization coming in second place. And trust me, this is the way it should be, because in the end the employee has to be secure in the knowledge that “big brother” isn’t watching them. We achieved this by ensuring that the details of their analysis was totally private & confidential and the purpose of the dashboard was to make them more effective and not something to beat them over the head with. The organization got aggregated results that gave them a detailed (and honest) view of how their business was functioning at the macro level; how the KPIs were distributed across their teams, geos, roles, skills, etc., how teams interacted (or not), were some parts of the organization isolated or some acting as key communication hubs? All massively valuable information that you could not get without the trust and openness of the employee.
  5. At the event I was testing out some new language to see how it was received by folks, specifically I was dropping the term social to allow me to talk much more broadly about the Enterprise Graph encapsulating any type of human interaction and not just social. So I switched, as much as possible, from talking about social networks to systems of engagement and from social analytics to engagement analytics. Totally coincidentally Geoffrey Moore, who coined or at least popularized the term systems of engagement, was doing the partner keynote on Sunday which seemed timely, and as I’ve been a huge fan of his work for years I trotted along to his talk (which was great) and was like a lovesick puppy when I spoke to him after the session. Gosh I’m such a nerd :-) Anyway back to the feedback… I believe this new language was positively received and folks particularly liked the idea of the enterprise graph being able to represent a complete set of interactions that describes what happens across the business. We don’t spend all our days in social and spread a lot of our interaction breadcrumbs across many social, collaboration, communication, and business applications, so it seems reasonable that they should all be able to participate and contribute to the enterprise graph and associated analytics.
  6. And finally the pièce de résistance; The Enterprise Graph. For what seems like forever, or at least since my semantic web days in 2005 when I was pushing linked data, ontologies, and knowledge graphs at anyone with a heartbeat, I’ve been convinced in the power of the graph; from using knowledge graphs to add context to text analysis to using social graphs to add context to people analysis. With our engagement analytics solution we were able to show-off what you can to do with a bigdata platform, a property graph, social data, some interesting graph algorithms, alongside a solid business need and solution design :-) This hybrid IBM/open source big data architecture elicited universally positive feedback from the IT folks with everyone generally happy to see IBM’s continued commitment to open source and large scale graphs.

So that’s pretty much it in a nutshell. All in all a positive event with lots of energetic dialog from all involved, so thanks to all those who visited us in the lab. You made our week :)

2 Comments to “Social Measurement: Love it or hate it?”

  1. I guess I belong in the “positive bucket”. What I liked the most was the multidimensional character of the dashboard and the possibility to dig deeper into the reasons behind the scores. I don’t mind the numbers as long as you avoid the over-simplified approach of Klout. ONE number is making it too easy and too judgmental. What Klout seem to do really well, and which I suggest you incorporate going forward, is to identify the topics of influence.

    I fully agree with the approach to the privacy issue of making these data visible only to the individual, at least as long as the data are individual. Aggregating scores to the level of “hiding” the individual, though, could make the analysis much more operational in driving change through managerial targets.


    • Your point about associating topics to the various scores is well taken, and is definitely on our list of things to add. It’s also valuable in associating topics of interest to individuals for other solutions like recommendation systems. If you are considered eminent on a topic there is a good chance you are interested in it :-)


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