Guide to Building Data-Driven Organizations in the Public Sector

Data on Teams

Team 4: Martha Ramos, Philip Schlotter, Randolph Wilkins

Data on Teams Overview (Randy Wilkins)

If you are one to embrace social groups, then these chapters, both about Big Data and Social Groups may alter your view about learning and how you interact within those groups. Because the masses have left untold amounts of “breadcrumbs” behind from their use of smartphones, the Internet, personal GPS systems and other devices, one Author was able to study their behaviors and actually correlate similarities to that of a hive of Bees…Bees you say? Yes, Bees! This research is fascinating at the least and provides an endless array of empirical data which may be used in daily life and especially within social groupings, to increase collective intelligence and the effectiveness of the group.

To back up the science found in Social Physics, the second chapter focuses not on the typical aspects of Big Data; however, specifically upon on how the actual data affects human attitudes towards change, and specifically, the furtherance of knowledge in social groupings. The key take-away from Eagle, N., & Greene, K. (2014) and their writing, Reality mining: Using big data to engineer a better world, is that although Big Data is a burgeoning and albeit prosperous approach to science and applied research combined, the sources of the data retrieved (consumer habits, etc.) not only feeds the research; however it hinders the research as well, wherein I would assume is the wishy-washy-ness of consumer habits, specifically those of social groups or groupings of groups that may skew or alter outcomes of any such study. Needless to say, the remaining chapters of both these works, deserve a look through, for anyone interested in human behavior and how it relates to private and public groups or organizations. The Facebook references alone, are quite intriguing.

Chapter Summaries

How Patterns of Interaction Translate into Collective Intelligence (Philip Schotter)

Pentland states that a major influence in predicting group intelligence was the equality of conversational turn taking. Teams where few members dominate the narrative were less collectively intelligent that those teams with greater team interactive discussion. Additionally, by analyzing patterns of team interaction and idea flow, research suggests that the best way to improve team performance is to encourage a pattern of engagement and exploration or discovery of new ideas. In this context, engagement is defined as idea flow between a work group or team.

Alex Pentland and various graduate students created an innovative way to measure how teams are managed with sociometric badges. The team of students turned colleagues, spun-off a company Sociometric Solutions, that became Humanyze https://www.humanyze.com. Humanyze are pioneers of organizational analytics. According to their website, they help organizations understand how their teams interact in order to increase performance.

Gathering Data from Small Heterogeneous Groups (Judy Ramos)

Chapter three of Reality Mining from Eagle and Greene, provided an overview of the challenges of collecting data from small groups and the “technical, legal, and sociological challenges” that researchers face. One of the largest concerns with small group data gathering is the loss of anonymity for participants. Eagle and Greene identified small groups data collection as making individuals feel “vulnerable and exposed;” therefore, making data collection more difficult at this level. Eagle and Greene described the various technologies used in four research areas: conference connections, knowledge management for work environments, neighborhood environmental concerns, and media consumption. To clarify, small groups were identified as those between 10 to 1,000 people.

The use of “Smart Badges” that are equipped with an RFID chip is a way for researches to collect data on the conference attendees such as sessions attended, social interactions, meals and time spent at various information booths, and have the capabilities to send text alerts to various vendor attendees about the participants profile. The badges were described as lightweight, however, expensive and the badges did not provide the users any control over the data they wanted to share. Another technology described was the use of electronic badges which provided more flexibility as to the information the user wanted to share and it also allowed the user to interact with other conference participants and share items such as business cards and connect with speakers, and conference staff. A Reality-Mining study that was conducted at MIT used “nTag” and offered the participants the ability to utilize a “stealth mode” when they did not want to be tracked. The outcome of the study was that by providing the users the ability to have control in the data they wanted to share, most of the participants did not use “Stealth Mode.” Since these technologies are expensive, there is belief that companies will start using the Smart Phone technology and program specific software for individual events.

Data-Mining is a way for an employer to gather information about employees and their work environment. This type of data collection can check email usage, record calls, collect data on web sites visited, monitor productivity, calendar entries, communication within staff, instant messages, and monitor movement through an office. However, the use of this technology faces legal issues around employee protection and privacy. For example, monitoring phone calls for business related work is legal. However, once an employer begins to monitor personal calls, there could be other issues if both parties are not aware that this type of monitoring is occurring. In 2010, a Supreme Court case City of Ontario V. Quon determined that it was legal for the employer to access data from a police officers City provided pager. However, this is still in question for employees in the private sector. In general, there are concerns about the use of these systems to increase workplace productivity especially when an employee feels that there is an infringement on their privacy and employee rights. However, there was indication that employees would be more willing to be monitored if there were incentives such as pay increases tied to the monitoring.

UCLA has been using portable personal air-sensors to track environmental air quality, smog, weather, visits to fast food restaurant and trash collection within neighborhoods. These tracking systems are used through a mobile device due to the capabilities of the camera usage and the GPS to track locations. However, there is still a lot of concerns regarding the effectiveness of the data collected due to the sensors in phones. UCLA also used this type of data collection to determine the best bike paths and they were able to track traffic data, air quality, and accidents. Once of the methods that UCLA has used to encourage participation is by giving the phones away as incentives. Another study conducted by UCLA used ambient audio technology to track the type of media used by consumers. The way this technology works is off a smart phone or computer and records an individual’s “soundscape” several times per minute. This is technology can be telling regarding the type of movies, games, television, media, and music the user listens to throughout the day. Since the audio was converted immediately to a “digital signature of the predefined media,” there user did not need to worry about having their personal conversations recorded which made people more adapt to participate in the study.

In summary, people value their personal information and there is a huge interest in individuals wanting to protect and choose the data they want to share. Therefore, there is still a lot to be explored in providing data collecting software that allows the user to choose the data he/she wants to share and in the use of incentives to increase participation in small group data collection.

Discussion Questions (for Yellowdig)

  1. Do you feel that Big Data is here to stay, or in the alternative, here to stay; however, morphed into a more streamlined, consumer specific and useable for non-invasive group tracking and research?

  2. What are your fears about others having/viewing/using your data for research or anticipatory functions related to your specific groups, choices or desires?

  3. Would you be willing to be watched or listened to at work in order to improve performance?

  4. How would you react if your supervisor gave you feedback that you were dominating your team and not allowing enough interactions and diversity from the other team members?

  5. For small groups studies, what type of incentives do you believe send the message to the participants that their anonymity is protected?

  6. Do you believe that data is compromised when you provide a small study group the ability to choose the information that is shared?

References