Monday, January 30, 2012

How would your enterprise's social graph look like?

Imagine having a Facebook-like rich social graph tailor-made for your company with just the right information about  relevant entities and their activities being captured and maintained. You can derive accurate insights about your customers; excel in marketing with highly segmented campaigns using personalized content; get better returns on marketing by focusing on influencers; convert more of the resulting leads with personalized offers and strong referrals; get the best return on support by focusing on the key (vociferous?) customer segments; improve key product dimensions by analyzing feedback; keep a tab on competitors in the context of your most profitable products; ... If you are getting ideas, read on!

Before you do anything else, convince yourself that you might be unknowingly taking undue risk by hinging your enterprise wagon entirely on data from third-party social networks - here are a few factors you might want to consider.

Assuming you are at least ready to weigh the benefits and costs of building your own enterprise social graph, here are some seeds for your thought process. 

What would an Enterprise Social Graph (ESG) look like?
The ESG is a highly interrelated graph that comprises 
  1. Entities like people (customers, employees), companies (the enterprise itself, competitors, partners, suppliers), products (those owned by the enterprise and its competitors)
  2. Defined Relationships among these entities
  3. Activities with one or more entities as actors and/or subjects - Documents can represent these activities
Though the ESG does not really have layers or strata, it would be useful to visualize it as a layered graph with connections running between nodes in the same layer as well as across layers.

Starting with the Enterprise Graph
If you represented the relationships between entities that can be gleaned from enterprise systems (not knowledge hiding in employees" minds!) as a graph, it might look something like this:

You might have noted the following:

  • Enterprise Entities:The first layer is illustrative of the kinds of entities that are typically seen within the enterprise. The taxonomy of the entities will be industry-specific and the canvas is as large as it can get, with technologies, partners/suppliers and competitors all coming into play.
  • Activities: The kinds of actvities that are captured correspond to one of those in CRM, HR, PLM and ERP systems. You can see that this already catches relationships like "Customer A owns Product P", "Customer B complained about product Q", "Employee E manages Employee F", "Employee G knows Influencer I", etc.
  • People: Several links between people are missing in this and that is deliberate. e.g. relationships between influencers and employees/competitors. You might argue that someone in the company "knows" about this but the counter to that is if you ran an algorithm to weigh the influence of that person, we would miss out on this fact! In general, as you move further away from your employees and customer entities, you have lesser information on people and activities available for analysis.
  • Mostly the relationships captured in this version of the graph (no social dimension to the entities) are less dynamic (e.g. something more defined like a Product-Technology hierarchy). Many enterprises do not have the capabilities to process data such as Survey free-form fields, chat transcripts, etc which are captured and stored in the enterprise systems.

Adding the Social to the Enterprise Graph
Lets add the social dimension which means we incorporate the following additional information that we can glean from social media monitoring:
  • People's likes and dislikes; skills, preferences
  • People's personal and professional connections
  • Social Media activities of people and companies around technologies and products of your interest
  • Competitive moves and directions
  • Motives, Intents and Drivers for people's buying behavior
  • and so on ...

Adding just a few illustrative examples from the above list, the ESG might now look like this:

As you can see, this graph holds information for use within the enterprise as well as the external world. You can start to see missing links in your corporate puzzle that might explain several business trends that you see in your traditional BI systems but can't find reasonable causes and remedies for!

How to build and use the ESG?
You can imagine this to grow immensely dense for large enterprises or even for small enterprises which want to include more information in this graph. Beyond a point, the scale of this graph will necessitate Big Data capable solutions. You can of course start small and build this graph over time but eventually you should plan for large scale graphs especially if you want to (and you should) do temporal analysis of trends.

Looking at the building blocks for a social data integration solution, the ones that will be key for building the ESG are the Text Analytics modules and the infrastructure components to reflect the "graph" nature of the information. Topic for a joint follow-up post from our dev team and me!

As a parting thought, there are several vendors with patents around this area, but not very many published success stories of enterprises building powerful social graphs. (If you know of any, please do leave a link or two in the comments or tweet them to @ramsgopa).

There are two ways to react to that fact - either wait for other enterprises to taste success (and then follow them) or be one of them. How will you react?

Ram Subramanyam Gopalan - Product Management at Informatica
My LinkedIn profile | Follow me on Twitter
Views expressed here are personal and do not necessarily represent those of Informatica.


  1. The solution presented here will be astronomic as the mash-up (enterprise and social) data grows over time; thus needing a grid of such graphs.

    Eventually, what we'll have to build what I call the Info-Grid. Here is a thought on how it may work.

    1. Each user has its private view of content. The content created (profiles, status) and enterprise (forums, employment history).

    2. Mash up this with content of network (Identity resolution required to do profiling and suggestion of related relevant information)

    Using #1 and #2 a graph for user is created, such graphs can exist in silo or can be mashed up further with other users (a complex grid of inter-connected information) using certain semantics. This can be tenentable information.

    The Info-Grid can be considered as an Enterprise-Hub which consolidated information (using distributed processing technologies).

    There can be other applications (aggregation/BI aspects built on top of it for fulfilling vertical specific use cases.

  2. Very interesting. Thought you might be interested in this...

  3. Thank you for sharing. You ask a very interesting question. Data integration is critical in sustaining competitive operations.

  4. there are several vendors with patents around this area, but not very many published success stories of enterprises building powerful social graphs.

    Media Monitoring