Archive for June 2005

New Web Service for Sparklines

June 27th, 2005 — 3:57pm

From some­one else named Joe, a free ser­vice that gen­er­ates sparklines:

Now I can plot the truly dis­at­is­fy­ing long-term per­for­mance of my 401ks using a con­ve­nient net­worked infra­struc­ture service…

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Three Contexts for the Term "Portal"

June 27th, 2005 — 3:37pm

I’m work­ing on a por­tal project at the moment for a health­care client, so I’ve heard a great deal about how the con­cept of ‘por­tal’ is so diluted as to be effec­tively mean­ing­less. Fol­low­ing a series of sur­pris­ingly mud­dled con­ver­sa­tions with tech­nol­o­gists, busi­ness types, and end users rep­re­sen­ta­tives around the con­cept for this new por­tal, I real­ized that much of the hand-wringing and con­fu­sion comes from sim­ple lack of per­spec­tive — on the dif­fer­ent per­spec­tives rep­re­sented by each view­point. Ambi­gu­ity or dis­agree­ment about which per­spec­tive is the frame of ref­er­ence in any given dis­cus­sion is the biggest source of the con­fu­sion and fric­tion that makes these projects need­lessly dif­fi­cult.
There are (at least) three dif­fer­ent per­spec­tives on the mean­ing of the term por­tal.
To tech­nol­o­gists and sys­tem devel­op­ers, a por­tal is a type of solu­tion deliv­ery plat­form with stan­dard com­po­nents like authen­ti­ca­tion, an appli­ca­tion server, inte­gra­tion ser­vices, and busi­ness logic and pre­sen­ta­tion lay­ers that is gen­er­ally pur­chased from a ven­dor and then cus­tomized to meet spe­cific needs. Exam­ples are Plumtree, BEA, IBM, etc.
To users, a por­tal is a sin­gle des­ti­na­tion where it’s pos­si­ble to obtain a con­ve­nient and — likely, though not always — per­son­al­ized com­bi­na­tion of infor­ma­tion and tools from many dif­fer­ent sources. Some exam­ples of this sense of the term include Yahoo, MSN, and a well-developed intranet.
To a busi­ness, a por­tal is a bounded vehi­cle for aggre­gat­ing infor­ma­tion and tools to address diverse con­stituent needs in a coör­di­nated and coher­ent way, with low­ered man­age­ment and admin­is­tra­tion costs real­ized via frame­work fea­tures like per­son­al­iza­tion, cus­tomiza­tion, and role-based con­fig­u­ra­tion.
One case where all three of these frames of ref­er­ence inter­sect is with Exec­u­tive Dash­board projects. A dash­board is a por­tal in all three of these senses (unless it hap­pens to rest on a dif­fer­ent archi­tec­ture / tech­nol­ogy stack, in which case I main­tain that it’s some­thing else, so as an IA it’s pru­dent to keep in mind the dif­fer­ing impli­ca­tions and assump­tions asso­ci­ated with each per­spec­tive while deal­ing with their representatives.

1 comment » | Building Blocks, Dashboards & Portals, Information Architecture

Common Findings of Social Informatics

June 23rd, 2005 — 4:44pm

Found via via, orig­i­nat­ing in an arti­cle titled Social Infor­mat­ics: Overview, Prin­ci­ples and Oppor­tu­ni­ties from the ASIST Bul­letin spe­cial issue on Social Infor­mat­ics, which, inci­den­tally is one of those very inter­est­ing dis­ci­plines I don’t have enough time to keep up with, but that has much to offer prac­tic­ing infor­ma­tion archi­tects.
On com­put­er­i­za­tion, Sawyer says, “Com­put­er­i­za­tion, to para­phrase soci­ol­o­gist Bev­erly Bur­riss, is the imple­men­ta­tion of com­put­er­ized tech­nol­ogy and advanced infor­ma­tion sys­tems, in con­junc­tion with related socioe­co­nomic changes, lead­ing to a fun­da­men­tal restruc­tur­ing of many social orga­ni­za­tions and insti­tu­tions.“
Add in a client man­age­ment clause, and this is essen­tially my job descrip­tion as an archi­tect / designer / cre­ator of infor­ma­tion envi­ron­ments that solve busi­ness prob­lems. I don’t know Bur­riss’ work — does any­one else?
Directly address­ing the role of a con­structed prob­lem Sawyer says, “…social infor­mat­ics is problem-oriented. This work is defined by its inter­est in par­tic­u­lar issues and prob­lems with com­put­er­i­za­tion and not by its adher­ence to cer­tain the­o­ries or par­tic­u­lar meth­ods (as is oper­a­tions research).“
In what looks like a neatly phrased snap­shot of user research, Sawyer says, “The strong empir­i­cal basis of social infor­mat­ics work, how­ever, is com­bined with both method­olog­i­cal and the­o­ret­i­cal plu­ral­ity. Social infor­mat­ics work typ­i­cally includes an array of data col­lec­tion approaches, sophis­ti­cated large-scale analy­ses and com­plex con­cep­tu­al­iza­tions.“
Here’s a longer excerpt:
The Com­mon Find­ings of Social infor­mat­ics
More than 30 years of care­ful empir­i­cal research exists in the social infor­mat­ics tra­di­tion. As noted, this work is found in a range of aca­d­e­mic dis­ci­plines, reflects a mix of the­o­ries and meth­ods, and focuses on dif­fer­ent issues and prob­lems with com­put­er­i­za­tion. Here I high­light five obser­va­tions that are so often (re)discovered that they take on the notion of com­mon find­ings rel­a­tive to com­put­er­i­za­tion.
1. Uses of ICT lead to mul­ti­ple and some­times para­dox­i­cal effects. Any one ICT effect is rarely iso­lat­able to a desired task. Instead, effects of using an ICT spread out to a much larger num­ber of peo­ple through the socio-technical links that com­prise con­text. An exam­i­na­tion of this larger con­text often reveals mul­ti­ple effects, rather than one all-encompassing out­come, and unex­pected as well as planned events. For exam­ple, peer-to-peer file shar­ing may help some musi­cians and hurt oth­ers.
2. Uses of ICT shape thought and action in ways that ben­e­fit some groups more than oth­ers. Peo­ple live and work together in pow­ered rela­tion­ships. Thus, the polit­i­cal, eco­nomic and tech­ni­cal struc­tures they con­struct include large-scale social struc­tures of cap­i­tal exchange, as well as the microstruc­tures that shape human inter­ac­tion. An exam­i­na­tion of power often shows that a system’s imple­men­ta­tions can both rein­force the sta­tus quo and moti­vate resis­tance. That is, the design, devel­op­ment and uses of ICTs help reshape access in unequal and often ill-considered ways. Thus, course man­age­ment sys­tems may pro­vide added ben­e­fits to some stu­dents, put added pres­sure on some fac­ulty and allow some admin­is­tra­tors to use the sys­tem to col­lect addi­tional evi­dence regard­ing the per­for­mances of both stu­dents and fac­ulty.
3. The dif­fer­en­tial effects of the design, imple­men­ta­tion and uses of ICTs often have moral and eth­i­cal con­se­quences. This find­ing is so often (re)discovered in stud­ies across the entire spec­trum of ICTs and across var­i­ous lev­els of analy­sis that igno­rance of this point bor­ders on pro­fes­sional naïveté. Social infor­mat­ics research, in its ori­en­ta­tion towards crit­i­cal schol­ar­ship, helps to raise the vis­i­bil­ity of all par­tic­i­pants and a wider range of effects than do other approaches to study­ing com­put­er­i­za­tion. For exam­ple, char­ac­ter­iz­ing errors in diag­nos­ing ill­nesses as a human lim­i­ta­tion may lead to the belief that imple­ment­ing sophis­ti­cated computer-based diag­nos­tic sys­tems is a bet­ter path. When these sys­tems err, the ten­dency may be to refo­cus efforts to improve the com­put­er­ized sys­tem rather than on bet­ter under­stand­ing the processes of triage and diag­no­sis.
4. The design, imple­men­ta­tion and uses of ICTs have rec­i­p­ro­cal rela­tion­ships with the larger social con­text. The larger con­text shapes both the ICTs and their uses. More­over, these arti­facts and their uses shape the emer­gent con­texts. This can be seen in the micro-scale adap­ta­tions that char­ac­ter­ize how peo­ple use their per­sonal com­put­ers and in the macro-scale adap­ta­tions evi­dent in both the evolv­ing set of norms and the chang­ing designs of library automa­tion sys­tems. Library automa­tion is not sim­ply about recent devel­op­ments of appli­ca­tions with sophis­ti­cated librar­i­an­ship func­tion­al­ity; it is also about patrons’ dif­fer­en­tial abil­i­ties to use com­put­ers, library bud­get pres­sures, Inter­net access to libraries and the increas­ing vis­i­bil­ity of the Inter­net and search­ing.
5. The phe­nom­e­non of inter­est will vary by the level of analy­sis. Because net­works of influ­ence oper­ate across many dif­fer­ent lev­els of analy­sis, rel­e­vant data on com­put­er­i­za­tion typ­i­cally span for­mal and infor­mal work groups; for­mal orga­ni­za­tions; for­mal and infor­mal social units like com­mu­ni­ties or pro­fes­sional occupation/associations; groups of orga­ni­za­tions and/or indus­tries; nations, cul­tural groups and whole soci­eties. This com­mon find­ing is exem­pli­fied by the tremen­dous pos­i­tive response by younger users to peer-to-peer file shar­ing, the absolute oppo­site response by music indus­try lead­ers and the many approaches taken by orga­ni­za­tional and civic lead­ers regard­ing the legal­i­ties and responses to use.

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