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Sperm Banks

Overview

Sperm banks are complex organizing systems spanning both physical and digital realms, supporting unique and sensitive interactions for a niche user group. Sperm banks are really two different organizing systems: one to manage the physical collection of specimen (typically in the form of vials), and another to organize description or surrogate resources of each specimen. The primary objective of a sperm bank is to allow collection and storage of sperm specimen from donors, and search and retrieval by sperm recipients or health clinics acting on their behalf.

What resources are being used?

The scope and scale of a sperm bank can be large, to include donors and donations numbering in the hundreds or even thousands. Typically a sperm bank serves people and clinics within a geographical area. The nature of the resources collected is such that a high level of granularity is required in resource description assigned to each donor and each specimen collected. Organizing principles for both the physical system and digital system rely heavily (almost exclusively) upon the resource descriptions applied. Each instance of the resource must be uniquely identifiable, but can be classified into equivalence classes of donor attributes, such as ethnic heritage, genetic profile, and blood type.

The systems must allow for continual addition to the collection as new donors are added and contribute to the bank. Realistically, collected sperm specimen is not stored in the system indefinitely; however, the systems must be designed to accommodate storage over an open-ended amount of time, since the bank does not know in advance how quickly a specimen will be used or how long a person will serve as a donor. In a similar vein, records and specimens need to be properly expunged as donors withdraw from the bank or otherwise leave the system.

A sperm bank’s overall objectives of allowing storage and retrieval of sperm specimens naturally impose requirements about intentional arrangement of both physical specimens and digital records of resource descriptions. This leads to the creation of resource categories based on number and types of resource descriptions being exploited by the chosen organizing principles. Once the systems achieves enough standardization and richness in resource descriptions, the systems can employ computational processes to automatically create resource descriptions, such as from lab test data, which also facilitates search and retrieval at a later stage.

Why are the resources organized?

At minimum, sperm banks must be organized to support specimen acquisition or purchase by sperm recipients, either individual women trying to conceive through artificial insemination or fertility clinics serving such women. Sperm banks may choose to create a web-based description resource organizing system to enable online searching or “auctions.” To me, the most intriguing interaction is the online “bidding war,” where potential sperm recipients bid on donors who market their genes with self-descriptions such as “Good at math and science!” or “Tall and athletic.” I have been told that the competition is fierce, and users have to act quickly if she sees a suitable donor, else the inventory might deplete and the donor might exit the auction.

Lastly, fertility treatment is an emotionally charged procedure requiring its information systems to support sensitive and context-appropriate interactions; for example, some women may want to choose donors who physically resemble their husbands or partners, but the women may not actually want to see photos of the donors. Hence, the overarching goal of the digital organizing system should be to enable retrieval of donor information while respecting delicate and highly individualized preferences on access and privacy.

How much are the resources organized?

All donors undergo rigorous review and testing before they are selected to contribute to a sperm bank. Information collected about every donor includes: medical history, ethnic lineage, genetic history, family history, professional/educational background, inventory of physical characteristics, even personality evaluations. The classification scheme of the organizing system is explicitly prescribed and predetermined at the design stage of the system. When making choices about the underlying technology supporting the digital organizing system, the system designer must make trade-offs between using a strict schema to ensure standardization in information collected, and possible future or incremental changes in description vocabularies and classification schemes, resulting in possible database schema changes.

When are the resources organized?

Sperm banks are continuously organized as new resources become part of the collection. The organization is never “just in time” since it does not make sense to defer organization until retrieval. The information collected on donor attributes determines the categories a particular specimen would belong to, or collocation requirements of a physical specimen unit. It is possible to devise a faceted classification scheme to enable search by facets. Organization also includes ongoing evaluation of resource descriptions to ensure consistency and adherence to the established schema.

Who does the organizing?

Organization is performed by staff members of the sperm bank, under rigorous protocols for classification and storage. Staff members are trained to handle this sensitive data and to ensure that the systems comply with both privacy laws and design objectives to support sensitive and context-appropriate interactions. To meet this goal, the systems can remove or reduce individual authority in the human beings doing the organizing by employing a strict controlled vocabulary, predefined forms, and tag suggestions for resource descriptions.

Retrieval of information in the description or surrogate organizing system can also be a computational process that filters results by a user’s requirements. Staff members will need to periodically refine and maintain the search algorithms and computational processes as the system collects data over time on users’ query patterns and search terms.

Other considerations

System designers also need to be mindful of potential growth in collection size, which may impose a requirement on current technological or implementation decisions. At minimum, there should be a continuity plan in place in the event of natural disasters or other obstructions to business functions.

case studies

Overview

While we witnessed tremendous technological innovation, approximately 1.2 billion people are still said to live in extreme poverty. It has been a big question in international community how we can solve this problem more rapidly and effectively. The World Bank, a world largest aid agency making 52.6 billion commitment in 2012, believes data can accelerate the effort. They have been collecting data through their research activity as well as project operations and leading knowledge sharing in the industry. Since 2010, they started open data initiative and make available over 850 financial data sets, statistical data on 11,000 projects, and data collected through 700 surveys. This renewed, open information system includes various digitized datasets, various information retrieval functions and interactive visualization features. In this essay, I am going to focus my analysis on the Data Catalog, the online catalog provides the list of available datasets with various meta data and allows users to understand what particular datasets are and how they can access these datasets.

 

What resources are being used?

The Data Catalog contains 162 items, which are collection level resources such as “World Development Indicators”. These items are collection of indicator data typically organized in faceted classification of regions, countries, topics, and years. Alternative design choice could be to set granularity at more specific level, particular topic indicators such as “Access to electricity”. But this way would make it difficult to see how the overall survey system is organized and structured. As that is important information for users to think of application of the data, I believe the designer took the current granularity level.

As I stated in overview, the scale of the Data Catalog is already very large. It is expanding infinitely because the new survey results will be constantly added. When existing items get new set of annual data, it goes to “Archive” and does not increase total number of item in the Catalog. This way, the designer successfully shows large amount of data in a very simple manner. The Data Catalog has various description resources and data in highly compatible format to support interaction related to primary resources, which I would discuss in next paragraph.

 

Why are resources being organized?

The highest-level goal of this information system is to accelerate poverty reduction by enabling effective knowledge generation and sharing. To support the goal, the Data Catalog provides access and platform where users can retrieve, download and interact with data. Users are typically international aid practitioners, activists, researchers and software developers. There used to be little awareness about software developer’s role because they are not traditionally main stakeholders in the industry. But now, reflecting the World Bank’s strong commitment for utilizing data more effectively, the system provides the developers even APIs of the Catalog. It helps to pass the data from the Catalog to computational processes.

In addition, we can search information by useful features such as faceted classification, sorting based on multiple conditions. Links to download resources such as annual datasets are displayed so clearly in blocks that even first-time users can find them without problems. Most data are available in excel and csv formats so that users can manipulate, analyze and obtain meaningful findings. To support data manipulation by users with little technical knowledge, some of the items in the Catalog can be viewed in interactive visualization system. Most contents are available in multiple languages.

 

How much are the resources organized?

To enable precise browsing and search, the Data Catalog uses controlled vocabulary for various descriptions resources and well designed categories. Like authority control in library science, this system precisely follows standardized names and terms. For example, as “Economy Coverage”, there are standardized categories and names of regions such as “East Asia & Pacific” and “Europe & Central Asia”. However, some items in the Catalog are quite different from others in terms of contents, making it difficult to categorize everything to the same degree. Therefore, these items seem to use standardized descriptions wherever applicable. I do not observe social tagging system with any resources in the Catalog at all. I believe that the designer did not include social tagging system because most users have specific information needs and would search information based on controlled vocabularies.

 

When are the resources organized?

The resources in the Catalog are all collected officially by the World Bank. The World Bank has obligation of publishing these resources, and so organizes items soon after the studies are completed. Because most survey results are digitized, the process of inserting the new survey results to the Catalog might be automated. Description resources are generated at the time when a primary resource is added to the Catalog, providing users as efficient interaction as possible.

 

Who does the organizing?

As the system is very large scale, there must be staff who are in charge of overall governance and decision making about the organizing system. These staff work to analyze users’ interaction and improve the system by changing interfaces, keeping data consistent, and possibly generating new description resource categories.  On the other hand, the staff who are responsible for particular surveys work to add more resources in the approved formats with description resources in controlled vocabularies.

 

Other considerations

One of the biggest challenges of the Catalog is to support interaction in multiple languages. This is not a trivial issue as users are from various countries with different culture and languages. Current interface of the Data Catalog is not ideal for non-English speaking users. Even if I switch the language setting to Chinese, many description resources are still displayed in English.  Because these are official terms which probably have corresponding official translated terms, I believe the designer did not want to use auto-generated translation. In addition, information retrieval features are currently only available in English. If the same feature would be implemented in Chinese, a non-segmented language, it could be more complex. As OECD successfully provides statistical data in both English and French, the World Bank may be able to learn from their practice.

Technical Call Center Organization

Overview:

Most customer support is handled via call centers staffed by a large number of front-line customer service representatives. These front-line representatives are tasked with either resolving the customer’s technical issues or referring the customer’s request to other more experienced Tier1/Tier2 personnel who have more specific domain expertise to resolve the inquiry. Most call centers are large operations that have the majority of front-line representatives. This analysis will focus on IT technical support call centers supporting the internal employees of Fortune 500 companies with an average employee count over 100,000.

What resources are being used?

The key resources supported by the IT call center are 1) Personnel tasked with responding to customer issues 2) Knowledge Base Articles which contain written instructions about how to resolve technical problems and 3) Service Tickets which document a specific instance of a customer calling in with a technical issue. The combination of these resource determines the key task of the call center which is to satisfactorily address a pre-defined number of technical support calls within a period amount of time. The capacity of a call center is firstly determined by the aggregate number of personnel that are able to respond to phone calls. Secondly, the ability of any given call center person’s ability to resolve a customer inquiry is determined by the quality and accuracy of the Knowledge Base Articles that are created to help resolve the issue. A Knowledge Base Article documents the recommended steps to resolve a specific type of technical issue. Lastly, the service tickets are the key resource that enables the call center to determine the volume of technical service calls and calculate the amount that are resolved versus the amount that are open.

Why are the resources organized?

Service Tickets are organized because they categorize and classify what are the nature of the technical support issues faced by customers calling the call the center. This allows the call center to spot trends and staff appropriately. For example, if a Fortune 500 company is rolling out a new operating system upgrade, the amount of service tickets opened pertaining to the operating system can alert call center management to increase the amount of front-line staff to answer the call volume. Also, one of the key categorizations of the service tickets is between the states of “opened” versus the state of “closed”. This is especially crucial because call centers often have pre-defined service level agreements pertaining the maximum amount of time that “open” tickets can remain open without appropriate resolution.

Knowledge base resources are organized because the speed and accuracy of front-line technician’s ability to find the pertinent resolution to a customer technical issue determines the capacity of a call center to meet pre-defined service level agreements. Therefore the call center needs to provide comprehensive descriptions of each knowledge base article such that the front line technician can easily locate that specific knowledge base article when needed in a short amount of time.

People are organized into specific technical domains to answer customer issues because the call center can increase the accuracy of problem resolution by maximizing the individual technician’s expertise by training staff thoroughly in pre-defined technical domains. People also need to have clear delineations of what types of technical issues are scoped appropriate for the specific role and what technical issues should be delegated to other staffers with increased technical knowledge. Again, this strict triaging of capability is to maximize the ability of the call center to “close” service tickets.

How much are the resources organized?

All three key resources of the call center are highly organized to increase maximum efficiency supporting the key goal of resolving customer service tickets as fast as possible.

Service Tickets are highly structured records that require the front-line personnel to accurately and consistently provide descriptions of technical service issues. Each service ticket is assigned a unique identifier with descriptive information such as demographics concerning the employee calling, descriptions of the IT resources generating the technical issue and lastly information concerning the technical problem encountered. All of these sets of descriptions often conform to a specific vocabulary or taxonomy defined by the call center. This is to ensure consistency for the call center management to eventually spot trends in the customer support issues. Also, consistency is required to accurately determine what are the most common new “open” issues that are not resolvable with guidance from the current Knowledge Base articles. This information can help direct personnel resource to which technical support topics merit the generation of instructions to resolve the issue. The service tickets are also highly descriptive because service tickets are often delegated to different personnel who much be able to get the full context of the customer support issue without having the ability ask the customer to provide more information. A key example is escalating a technical support issue from front-line technicians to more trained Tier1/Tier2 experts.

Knowledge Base articles are also highly organized because the call center intends to provide search and discovery mechanisms with both high recall and high precision for frontline technicians. To ensure high recall, Knowledge Base articles often have a hierarchal categorization according to the common customer service complaints. To improve precision, each knowledge base article must provide sufficient description of the symptoms of the technical service in which the knowledge base article is intended to address allowing front-line technicians to expend time reading further into the actual resolution steps.

 

When are the resources organized?

Service Tickets are organized at the time a customer calls the call center providing descriptions of their technical issue. Sometimes part of the organization can be pre-determined through unaided categorization through telephone prompts. Customer often must navigate through a variety of telephone prompts such as “Press 1 for X issue” or “Press 2 for Y issue”. The rest of the organization is determined based on the front-line technician’s description of technical service issues. Knowledge Base Articles are organized by Tier 1/Tier 2 domain experts. These people are able to take multiple instances of technical problems and create an abstraction containing a generic solution that can apply to a general situation that satisfy common criteria. Knowledge Base Articles are often created when there are multiple open service tickets that have been escalated to tier1/tier2. Therefore, to speed time to resolution by frontline technicians, Tier1/Tier2 creates the Knowledge Base article that can be searched and retrieved by frontline technician bypassing the need to escalate. Management organizes people into the different service tiers based on their initial expertise capabilities or trained capabilities when they are hired to work in the call center.

Who does the organizing?

Front-line technicians must be trained to accurately organize service tickets because they provided the initial description information that governs all future action such as issue escalation or generation of aggregation of call volume statistics. Every person in the call center must provide sufficient descriptions of resources for inputting information into Service Tickets. This is because to resolve an “open” service ticket multiple call center people might have to interact with an instance of a service ticket to input information. For example, if a front-line technician is unable to resolve a customer issue, that front-line technician needs to escalate the service ticket to other staffers. When another person is assigned the task to resolve a service ticket issue, that person often times must enter information into the service ticket describing what actions have been taken.

Other considerations

Call centers often have high turnover of personnel especially on the front-line tier. Knowledge transfer is especially important because the call center is often training a large number of rookie employees who often have no significant prior experience in the technical field.  The ability for the call center to record knowledge into searchable information systems allows the call center to more easily use that knowledge to onboard new employees. Imagine if all of the knowledge management was governed purely through word of mouth references. The call centers would have inconsistent problem resolution statistics because many people will be “re-building the wheel” to create resolutions to problems that have already been faced before by other employees.