Social media data collection

An enterprise social network (ESN) is an internal, private social network used to assist communication within a business [2]. As the number...

An enterprise social network (ESN) is an internal, private social network used to assist communication within a business [2]. As the number of companies investing in ESNs grows, employees are discovering more and more ways to conduct business on the ESNs. IDC expects the worldwide enterprise social networks market revenue to grow from $1.46 billion in 2014 to $3.5 billion by 2019, representing a compound annual growth rate (CAGR) of 19.1%[3].

We broadly divide this domain into internal and external. A majority of our discussion so far has focused on insights that can be gleaned from content available in external public social media data collection (such as Twitter). In this post, we focus on social media content that is generated inside a company’s firewall. This, we believe, is a very rich source of information, and a variety of business insights can be generated from it. Here, we discuss the concepts and a few specific examples regarding how companies can take advantage of this ever-growing collection of social data mining inside their social networks.

Employee privacy is an important consideration when looking at analytics based on enterprise social networks. We discuss how this was addressed for the Personal Social Dashboard use case in IBM. We want to utilize people’s individual posts to help create and leverage organization knowledge, but we need to do this without compromising the privacy rights of individual employees. We also describe how this was accomplished for the Personal Social Dashboard project.

Most of the content in this chapter is based on our personal experience with an IBM project called Project Breadcrumb, and its primary use case is the IBM Personal Social Dashboard (or PSD). This project is a collaborative effort involving many people in IBM over the years. We want to specifically highlight the contributions of Marie Wallace (social media strategist), David Robinson (big data architect), Jason Plurad (lead developer), Shiri Kremer- Davidson (chief data scientist), Aroop Pandya (data architect), Santosh Borse (data acquisition), and Hardik Dave (initial CIO project lead). Marie Wallace has written and spoken about this project in a variety of external venues (including TED Talks). Most of her writing on this project can be found on her website at [4].

Social Is Much More Than Just Collaboration

Many articles have been written about the benefits that companies are deriving from their investments in ESNs [5]. IBM first deployed an ESN in 2009. ESNs were initially deployed to facilitate collaboration among employees. In our experience, they have turned out to do much more for the companies. Figure 11.1 illustrates some of the significant advantages afforded by ESNs, beyond other traditional means of collaboration and communication as described further in the section that follows.

social media data collection

Transparency of Communication

When a CEO or a senior leader writes a blog in the ESN about the latest quarterly results and encourages employees to openly respond with their comments, there is a great deal of transparency in communication . This is not possible using traditional means of communication.

Frictionless Redistribution of Knowledge

When a piece of new information, technique, or finding is shared in an ESN, it reaches many members of the network without any special extra effort on the part of the originator. We (Avinash) recently encountered an IT problem with my Lotus Notes Mail system . I worked with Help Desk and got my issue resolved. I shared this experience on my wall, and this information was available to members of my network immediately. If those people further shared this information with their networks, this timesaving tip would be available to hundreds of employees within a short span of time and without any barriers.

Deconstructing Knowledge Creation

Discussion forums are a common feature of ESNs. These forums are created to elicit comments and ideas from different people on a given topic. Many times the forums are started with a description of a problem, and during the course of the discussion, after several members contribute, a solution emerges. This process not only creates knowledge that the company can leverage in the future but also distinctly identifies knowledge creators.

Serendipitous Discovery and Innovation

By far, the biggest benefit of ESNs in my experience is accidental discovery of useful nuggets of information. In October 2014, I was in the process of preparing a presentation for a talk that I was going to give at IBM’s Insight 2014 conference in Las Vegas. As part of our ESN’s features, I get a newsfeed of posts from the people I follow. One day I came across a post that referenced a study by MIT and Deloitte directly related to the subject of my talk. I was able to quickly take this information into account and enhance my presentation.

Enterprise Social Network Is the Memory of the Organization

Employee-to-employee interactions in an ESN are obviously quite valuable. When I have a question about a new product or process, somebody else generally responds with an answer. In many ways, then, this transaction is complete .

Figure 11.2 illustrates a subset of actual transactions that can take place in an ESN. You can see that, over time, various transactions are initiated and completed in the network. As time goes on, the network becomes the memory of the organization. Even though this may not have been the intent or the goal, the ESN ends up leaving breadcrumbs to indicate what people are doing and how they are behaving in different business situations. We contend that this is a very critical but underutilized resource in companies. This information can be mined for valuable insights with the help of analytics.

Understanding the Enterprise Graph

We have now established the value of the information left behind by social interactions in an ESN. The storage technology that is optimal for this type of information is called graph storage. A specific instance of this type of graph for a specific company is called the Enterprise Graph. In an ideal implementation, the Enterprise Graph combines transactional, social, and business data and also provides a knowledge base to perform analysis such as influence, social proximity, reputation, retention, performance, expertise, and more.

The basic components of an Enterprise Graph, shown in Figure 11.3, are as follows:
■ Data Sources—This is any source of data or metadata that we are interested in including in the Enterprise Graph. This includes social interaction data from an enterprise social network. For example, consider profile status updates and comments from everybody in a person’s social network data, including likes and tags.

■ Data Services—Data Services enable business applications to contribute data to the ESN via data APIs. For example, an application that compensates employees with a reward or recognition will utilize services (APIs) to contribute to profile status updates with the information about awards.

■ Graph Store—This is the physical storage utilized to store all of the interactional data that we are interested in analyzing. Graph APIs make interactional data easily consumable by analytics and business applications. For example, for an organization of 100 people, the graph contains a node for each person in the organization and an edge for each transaction between two nodes. As a result, the graph contains information about who did with what with whom and when
(in the ESN).

■ Analytics—Analytics algorithms generate graph-centric insights such as influence, expertise, and reputation. For example, the algorithms can compute a rank-ordered list of people with the maximum number of likes on their profiles.

■ Analytics Services—Analytics services make graph analytics easily consumable by business applications via analytics APIs. For example, an Expertise Locator application can query the graph using analytics services to identify a topic expert who is also very active in the ESN.

We can implement a number of useful applications that leverage the information from the graph. In the following sections, we describe one such application called the Personal Social Dashboard.

Personal Social Dashboard: Details of Implementation

The Personal Social Dashboard (see Figure 11.4) is based on a project in IBM. Internally, IBM uses an ESN based on IBM Connections, which includes a variety of features such as profiles, communities, forums, blogs, wikis, files, and bookmarks. The ESN is available to all IBMers, and there are varying levels of adoption and use of connections across business units and across geographies. Two groups of stakeholders are served by this application: individual employees and management.

The main objective for the individual employees is to help them understand their social impact and how to effectively activate their network for maximum value. For management, the objective is to help managers and executives understand how teams collaborate and to help them increase collective value.

Before we could process metadata about IBM employees’ social activities, we needed to follow an internal privacy evaluation and approval process. We were granted the approval based on the following agreements:

1. We will share the individual scorecard only with the individual who is signed into the system.

2. The overall engagement score or individual component scores will not be made available to anybody else.

3. We can share aggregate reports utilizing overall scores and component scores for groups, divisions, or countries to management teams; however, all such reports will be anonymized.

Some benefits that can be derived from an implementation of an application like the Personal Social Dashboard include the following:

■ The scorecard evokes a sense of competition, and people tend to want to improve their scores, which improves adoption of the enterprise social network and thus improves social collaboration.

■ The component scores give employees hints about what activities will contribute to their scores (activity versus reaction). This will enable employees to focus more on creating content that is valued by others and thus improve their individual eminence, which in turn adds value to the business unit.

■ The Personal Social Dashboard also provides a very detailed (anonymized) view of average scores by country and by business unit. This has already assisted managers and executives in understanding how ESNs are being utilized in their organizations and countries.

Key Performance Indicators (KPIs)

Based on extensive research conducted by the IBM Research team in Haifa, the IBM project team settled on four KPIs to represent or measure employee engagement [8]. The four KPIs represent four social behaviors:

■ Activity Scorecard—Measures the person’s periodic social activity effort; computed based on number of activities.

■ Reaction Scorecard—Measures how the person’s content is perceived by others; focuses on quality of activity based on reactions to it.

■ Eminence Scorecard—Measures how the person is perceived by others; focuses on number of people interacting with the person or his content.

■ Network Scorecard—Measures the person’s network; initially focuses on size—both network and followers.

The main page of the dashboard shows a composite score as well as the score of the four individual subcomponents (see Figure 11.5). To illustrate the capabilities of this application, we utilize Avinash’s scores and scorecard. The scorecard shows an overall score of 81. There is also a trend graph that plots Avinash’s score over the past six months. The bottom half of the screen shows the four subcomponent scores and a comparison of Avinash’s scores against an average of the organizational unit that he belongs to. For instance, his activity score of 76 is higher than the average activity score for his organization, which is 18. Similarly, we are able to compare Avinash’s other component scores against his organization’s average score.

How Does This Score Compare Against Others?

Figure 11.6 shows that Avinash’s overall score gives him a rank of 410 within IBM, and there are 84 other people who have the same score as he does. Within his organization (T&O), he has a rank of 88, and there are 20 other people in the organization who have the same score.

Activity Scoreboard

The discussion in this and the following three sections considers the reader as an employee.

Are you, as an employee, actively engaging with people on a regular basis? This measure is calculated by analyzing your various activities in relation to other employees, placing emphasis on more involved contributions (creating content, commenting, and so on) and less emphasis on the lighter-weight ones (reading, liking, and so on). This is illustrated in Figure 11.7.

This scorecard also shows the counts of the following six additional factors that collectively influence the overall Activity score:

■ Containers You Created—Are you creating collaboration spaces where people can engage? Are you taking an active leadership in providing an environment for sharing? This drill-down provides the number of blogs, communities, forums, or wikis you have created.

■ Content You Created—Have you been making an effort to provide content to the social network? This drill-down provides a count of what you have created (files, posts, status updates, and the like).

■ Content You Shared—Have you been sharing interesting content with your colleagues? This drill-down provides a count of content you have shared (files, posts, status updates, and so on).

■ Content You Edited—Are you contributing to the content created by your colleagues? This drill-down provides a count of content you have edited (pages, files, and so on).

■ Content You Liked—Are you generous with your positive feedback? This drill-down provides a count of the content you have liked.

■ Content You Tagged—Do you like to help the system to better qualify and organize the content? Or do you give your colleagues a thumbs-up by specifically tagging them with an expertise or skill? This drill-down provides a count of the content you have tagged.

Reaction Scorecard

Does your content generate a reaction from your fellow employees? This measure (see Figure 11.8) is calculated by analyzing your colleagues’ activities on your content, placing more emphasis on engaged contributions (comments, likes) and less emphasis on the more passive ones (reads, follows).

It also shows the counts of the following six additional factors that collectively influence the overall Reaction score:

■ Commented on your content—Do people like to give you feedback on your content and share their opinions? This drill-down provides the number of comments that your content has received.

■ File downloads—Do people download and read the content in the files that you uploaded? This drill-down provides the number of file downloads you have received.

■ Liked your content—Do people like your content? This drill-down provides the number of likes your content has received.

■ Tagged your content—Do people tag your content? This drill-down provides the number of tags that people have applied to your content.

■ Shared your content—Do people like to share your content with their colleagues? This drill-down provides the number of times people have shared your content.

■ Followed your content—Do people like to keep track of your content by following it so they get updates when it is modified? This drill-down provides the number of times that people have followed your content.

Eminence Scorecard

Do people value your opinion? Do they listen when you talk? This measure is calculated by analyzing how your colleagues interact with you (as an individual) and your content (as a reflection of their opinions). This is illustrated in Figure 11.9.

It places more emphasis on actions directed at you (tagging your profile, mentioning you in status updates, or following you) and less emphasis on those directed at your content (comments, likes, and so on). It also shows the counts of the following four additional factors that collectively influence the overall Eminence score:

■ People interacted with you—Do people like to regularly engage with you directly? This drill-down provides the number of people who have interacted with you directly (posted to your board, mentioned you in a status update, tagged you, shared something with you).

■ People interacted with your content—Do people like to regularly engage with your content? This drill-down provides the number of people who commented on your content (shared their opinion).

■ People who value your content—Do people value your content? This drill-down provides the number of people who liked your content.

■ People connected with you—Do people like to be part of your network? This drill-down provides the number of people who followed or befriended you .

Network Scorecard

The Network scorecard focuses on the network size and diversity (see Figure 11.10).

It also shows the counts of the following three additional factors that collectively influence the overall Network score:

■ People you are following—How many people do you follow to keep up with what they are doing and saying? This drill-down provides the number of people that you are following.

■ People who are following you—How many people like to follow what you are doing and saying? Are you speaking into the void? This drill-down provides a count of the people following you.

■ Friends—Do you have a rich network of colleagues that you are connected to? This drill-down provides a count of the people you are friends with .

Assessing Business Benefits from Social Graph Data

An analytics team in IBM decided to study the impact of social behavior on business outcomes. Over the first half of 2014, the team analyzed IBM’s Enterprise Graph (year 2013 data), IBM patents (about 4,500 patents), and customer advocacy data (for about 4,000 people). They concluded that there is a statistically significant positive correlation between high social engagement scores, as captured in the social graph, and the number of patents. In addition, there is a statistically positive correlation between high social engagement scores and the likelihood of getting selected as a customer advocate. Additional insights can be gathered by studying the scores of groups of people. Figure 11.11 shows the hypothetical score distribution of a possible department or division in an organization.

An analysis of these scores can yield different results and insights. One such insight is that a large number of people seem to have a decent Network score, but only a small number of people are doing well in terms of other scores, as illustrated in Figure 11.12. One interpretation is that people focus on building their networks but forget to engage. By the way, this is a common pattern that we observe when we study different groups.

What’s Next for the Enterprise Graph?

The Personal Social Dashboard has been used extensively to provide platform level metrics such as the number of social activities of certain types. The team is looking to move beyond this to some metrics related to business outcomes. Can we show that increased social behavior of certain types can result in positive business outcomes at the department, business unit, or company level?

■ Sales outcomes—If we include sales-related data such as number of leads, number of opportunities, number of proposals completed, and amount of revenue generated, can we attempt to find social behaviors that contribute to positive sales performance?

■ Employee performance outcomes—If we include data such as p romotions, awards, successful project completions, and recognitions, can we attempt to find social behaviors that improve employee performance outcomes?

■ APIs—Application Programming Interfaces enable other business applications to provide new data that can be added to the Enterprise Graph and to consume and enhance (or report) graph analytics.



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