In the age of Facebook, blogs and YouTube, information is no longer created exclusively by “trusted” sources. Rather, content is created by anyone with access to the Internet. It is the sharing of these thoughts, experiences and knowledge that defines social computing. Social computing provides the forum by which people can communicate their experiences and thoughts. It further enables others to locate and engage with each other for a mutually beneficial exchange of information. For example, Facebook allows you to search for people that you know or have something in common with, such as work or school. Once located, adding that person as a friend allows you to interact and receive updates from them. These people make up your social network of trusted or knowledgeable acquaintances, with whom you want to communicate, exchange information or follow their activities.
In the enterprise, much like Facebook, you are looking to connect with acquainted or like-minded people. Locating these colleagues provides an opening for an information or knowledge exchange. Through this type of collaboration, people can find the “experts” they need to solve business issues more easily. In an age of real-time collaboration and instant expectations, meaningful collaboration can mean the difference between success and failure. And, it is fast becoming perceived as essential for business success. Reducing the delay involved in connecting, sharing, understanding and making better use of information becomes increasingly important in today’s fast-paced world.
A Microsoft Research study titled, Thin Slices of Online Profile Attributes, states “by examining users’ decisions in an experimentally controlled social network, we show that users need only a ‘thin slice’ of profile information in order to form impressions of others online.” As a searcher, you can look for these “slices” to find the right people, but this is not always efficient and may sometimes lead to questionable results. For example, if profiles are not updated frequently or do not contain consistent terms and context, your search can be skewed.
Following a proven and consistent methodology to define organization expertise is critical to establishing a concise “expert search” and being able to solve business problems effectively and efficiently. The rest of this article outlines a simple methodology for companies seeking to take advantage of these Facebook-like searches to enable greater productivity:
- Define – Identify key elements of an organization that are crucial to achieving your corporate objectives. The definitions should be consistent with the vocabulary of the organization. For example, let’s say we need to locate Spanish-speaking data modelers with at least 5 years of experience. Languages spoken, skills and tenure should contain specific domain values in this case.
- Capture – Identify the sources that contain the system of record data for the elements defined.
- Store/Integrate – Combine these sources within a data storage system that enables indexing, thus organizing the defined elements and making the search more efficient.
- Manage – It is essential to insert updates as people gain new experience, expand as new terms are added to the vocabulary, and adjust as people join or leave the organization.
- Locate – By using a consistent vocabulary in a search, locating an expert that meets the needs is enabled by searching the index mentioned above. Fine-tuning results helps to adjust the relevancy weighting of each aspect or even control how many results are returned.
- Action – Once located, interacting with this expert is critical. The searcher must be able to engage effectively in order to successfully address their business concerns.
Defining an expert depends upon each organization’s composition such as industry, geography and size. As the methodology is executed, expertise should be defined according to the following areas:
- Profile Information – This includes the person’s name, title, office location and contact information. It may also include certifications, spoken languages and associated proficiencies.
- Skills and Experience – This would include skills relevant to their vertical/horizontal experiences such as healthcare, and projects or functions performed throughout their career.
- Knowledge Contribution and Relevance – This would be process documentation, articles, white papers, etc. that they have authored/co-authored and are available for use by other employees.
Organizations within the same industry will have consistent concepts about product, service or process. Across industries, horizontal functional experience such as datacenter management should be similar as well. Companies should look to create a common vocabulary of terms that represent their business workers. To facilitate expert identification, each person’s identity is defined according to this vocabulary. For example, when a person’s skills are defined as “human resources,” another person with the same experience should not be defined by “HR.”
Often, organizations have systems in place that manage dimensions of a person’s expertise. Capturing other elements of expertise might require more creative ways. As a person advances through an organization, their tenure and roles would typically be maintained within an HR system. This should be extracted and included within the expert store. Additionally, when a person is involved in something where clear vocabulary is not defined, these terms should be added and this activity should then be associated.
Capturing knowledge contributions and relevance also requires a social shift within most organizations. This involves employees producing content such as documents, blogs or wikis that relate their experiences and talents to their entire organization. This could be as simple as sharing a financial predictive model
that you are familiar with in order to prepare future employees who will perform the same task. The next critical step is to gather feedback from individuals, providing ratings or reviews depicting how helpful the spreadsheet has made their job. These exchanges are populated into the expert store and will provide results when “predictive model” is searched, allowing you to contact that person.
Data from expertise systems of record, content publication and ratings should be centrally stored and integrated for each “expert” according to organizational definitions. This is one of the most technically challenging aspects of this type of effort. In the same way that business intelligence
is only as good as your data, this “expert locator” is only effective if the data is integrated into a single repository.
Maintenance is critical to the continuity of the system. Once defined, if the system does not grow to reflect new business dynamics or accommodate the fluctuation of staffing, then any searches become based upon stagnant, static data, undermining any potential business value.
By entering keywords, based upon the vocabulary, the experts meeting the criteria are returned. Along the way, an organization can tune relevancy by assigning greater weight to an attribute to make the results more accurate based on the top criteria for each search.
Finding the expertise is nothing without the ability to collaborate with the expert or group of experts. Like Facebook or Twitter, collaboration platforms can enable people to tag this person as a colleague or even create a group from the top results. And, with an integrated collaboration platform, companies can enable immediate interactions through email, phone or instant messaging. With “presence” technologies, people can connect immediately, or if someone is unavailable, they can select someone else that is in the same time zone and free.
Social computing adds value to an organization in several ways. By encouraging employees to create, consume and offer feedback on content, an organization ensures that knowledge stored within one employee’s head is now documented and available to others who need it.
The entire organization’s information is now available to help identify critical information about the organization to ensure optimal execution of business processes or resolution of business problems. When a common vocabulary is established and made readily available to the company, communication dramatically improves and prior confusion is eliminated. New employees are effective sooner because they can connect with experts within an organization as defined by this common vocabulary. And, when issues arise, people know who to contact and when to contact them. Ultimately, collaboration can have a positive impact on the bottom line and foster a “collective intelligence” within the organization to solve business issues more effectively.
SOURCE: Social Computing: The Business Value of Collective Intelligence
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