My fel­low Princeterns and I arrived at Spry Incor­po­rated not quite sure what to expect from our three day stay. We had read the mate­ri­als the com­pany had sent us and we had combed through its web­site, but what Spry actu­ally did was still not quite clear. How­ever, once we met Spry CEO Brooke Steven­son ‘01, the Prince­ton alum who brought us out to Mary­land, she showed us around the office and fully elu­ci­dated the company’s field of work. Spry endeav­ors to allow client com­pa­nies and cor­po­ra­tions to eas­ily search their data by con­nect­ing their indi­vid­ual data­bases that are often dis­jointed and inflex­i­ble. As she duly noted, scat­tered data pre­cludes any attempts at a thor­ough analy­sis, pre­vent­ing a com­pany from mak­ing impor­tant and piv­otal deci­sions in this fast-paced world that waits for no one. At the heart of their approach is the use of ontolo­gies to orga­nize the data. This kind of seman­tic archi­tec­ture empha­sizes the rela­tion­ships between infor­ma­tion, mak­ing it eas­ier to query for the desired answers. An equally impor­tant aspect of Spry is their agile approach to devel­op­ment. Instead of the tra­di­tional water­fall approach where a com­pany releases a sin­gle end-product after com­plet­ing devel­op­ment, Spry espouses an agile scheme that allows them to not only quickly turn over a work­ing inter­me­di­ate prod­uct, but also show clients real­ized value. The incre­men­tal releases allow Spry to eas­ily meet the ever-changing needs of its clients.

We quickly real­ized that this Princetern­ship was not going to be the typ­i­cal shad­ow­ing oppor­tu­nity. Instead, it was going to be much bet­ter. Usu­ally, it seems that those who shadow are given tasks that are either menial or bor­ing, but Spry was going to give us a crash course on seman­tic and query lan­guages so that we could develop a solu­tion to an exist­ing prob­lem. This was both excit­ing and refresh­ing; we were tack­ling an issue that no one else had ever solved. After learn­ing Tur­tle (Terse RDF Triple Lan­guage) to write ontolo­gies and SPARQL (SPARQL Pro­to­col and RDF Query Lan­guage) to write queries, we wrote a user-customizable query cre­ator in MDQO (Model Drive Query Ontol­ogy) that Spry will con­tinue to improve.

Spry Incor­po­rated has a casual dress code that many tech star­tups often have, but it also has a lively and enjoy­able atmos­phere that may not nec­es­sar­ily reside in com­pa­nies of any size. The employ­ees feel com­fort­able bounc­ing ideas around daily progress off their cowork­ers, regard­less of their senior­ity at the com­pany. Spry thrives on this mutual assis­tance atti­tude that nat­u­rally man­i­fests itself.

Spry Staff, David, and fel­low Princeterns

This Princetern­ship let me explore the new and constantly-changing field of ana­lyt­ics. Per­haps the most pro­found dis­cov­ery was that this sort of job exists. It is fill­ing a much-needed role in the relentlessly-expanding global data par­a­digm. The most valu­able knowl­edge I gained is that, accord­ing to many of the employ­ees with whom we spoke, learn­ing how to learn in col­lege is the most impor­tant thing to take away from those four years. Although the actual mate­r­ial of aca­d­e­mic courses is def­i­nitely impor­tant, the abil­ity to problem-solve proved to be the most help­ful skill from col­lege in their daily projects.

I would like to thank Brooke, Meg, Christina, and the rest of the Spry team for their immense help and sup­port dur­ing the Princetern­ship. They really made the three days stim­u­lat­ing and pleas­ant. I would def­i­nitely rec­om­mend this Princetern­ship to other stu­dents, because I gained valu­able insight into my future plans. This expe­ri­ence has encour­aged me to con­sider ana­lyt­ics as a pos­si­ble career path. For that, I am also grate­ful to Career Ser­vices for offer­ing this program.