Data ana­lysts per­form a vari­ety of tasks relat­ed to col­lect­ing, orga­niz­ing, and inter­pret­ing sta­tis­ti­cal infor­ma­tion. The pre­cise nature of the job varies some­what from pro­fes­sion to pro­fes­sion, as an ana­lyst work­ing for a hos­pi­tal would nec­es­sar­i­ly focus on dif­fer­ent things than would some­one work­ing for a depart­ment store or a super­mar­ket chain. In any capac­i­ty, though, peo­ple with this job look for ways of assign­ing numer­i­cal val­ues to dif­fer­ent busi­ness func­tions, and are respon­si­ble for iden­ti­fy­ing effi­cien­cies, prob­lem areas, and pos­si­ble improvements.

Infor­ma­tion Compilation

One of the most impor­tant things any data ana­lyst does is col­lect, sort, and study dif­fer­ent sets of infor­ma­tion. This can look real­ly dif­fer­ent in dif­fer­ent set­tings, but is usu­al­ly relat­ed to nail­ing down a fixed val­ue to some process or func­tion so that it can be assessed and com­pared over time. A gro­cery store might want an ana­lyst to col­lect all the hours that cer­tain employ­ees work along with prof­it mar­gins for cer­tain days, weeks, or even hours, for instance; an Inter­net busi­ness might want to see hard num­bers on where cus­tomers are com­ing from, how much they are spend­ing on pur­chas­es, and whether deals like free ship­ping have any bear­ing on over­all profits.

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There are sev­er­al dif­fer­ent strate­gies peo­ple can use to com­pile data, but there are typ­i­cal­ly three uni­ver­sal goals. The data must be reg­u­lat­ed, nor­mal­ized, and cal­i­brat­ed such that it could be tak­en out of con­text, used alone, or put in con­junc­tion with oth­er fig­ures and still main­tain its integri­ty. Ana­lysts typ­i­cal­ly use com­put­er sys­tems and com­plex cal­cu­la­tion appli­ca­tions to get their num­bers nailed down, but there is still a lot of intel­lec­tu­al know-how that goes into mak­ing these sys­tems work.

Extrap­o­la­tion and Interpretation

Once the infor­ma­tion has been col­lect­ed, ana­lysts are usu­al­ly respon­si­ble for com­ing up with some con­clu­sions about what it means, as well as edu­cat­ing busi­ness exec­u­tives on how to use it. Get­ting hard num­bers on sales fig­ures for a giv­en hol­i­day sea­son, for exam­ple, is some­what use­ful in and of itself, but is usu­al­ly most valu­able when stacked against num­bers from pre­vi­ous years or oth­er sea­sons as a point of com­par­i­son. These pro­fes­sion­als may also be called on to help busi­ness own­ers and lead­ers under­stand what dif­fer­ences in num­bers mean when looked at from year to year or across var­i­ous depart­ments. They usu­al­ly have the exper­tise to not only assign sta­tis­ti­cal val­ues to things, but also to explain what they mean.

Pro­jec­tions and Advi­so­ry Responsibilities

In some com­pa­nies, ana­lysts are charged with actu­al­ly advis­ing project man­agers and lead­ers about how cer­tain data points can be changed or improved over time. They are often the ones with the best sense of why the num­bers are the way they are, which can make them a good resource when think­ing about mak­ing changes. A health clin­ic that wants to improve patient wait time might ask an ana­lyst to iden­ti­fy the main rea­sons for delays, for exam­ple, just as an adver­tis­ing firm might look for sta­tis­ti­cal feed­back on pri­or cam­paigns as a way to design and plan future slogans.

Research and Writ­ing Tasks

Advi­so­ry respon­si­bil­i­ties often go hand in hand with writ­ing and research. Most ana­lysts are com­fort­able prepar­ing writ­ten sum­maries to accom­pa­ny graphs and charts, but the posi­tion often calls for addi­tion­al writ­ing tasks, too, such as draft­ing com­pa­ny mem­o­ran­dum, press releas­es, and for­mal reports. Ana­lysts typ­i­cal­ly also col­lab­o­rate with data­base pro­gram­mers and admin­is­tra­tors to write sys­tem mod­i­fi­ca­tion rec­om­men­da­tions or in-house instruc­tion and train­ing materials.

Sys­tem Exper­tise and Troubleshooting

Most of the work ana­lysts do is com­plet­ed with the help of com­put­ers and dig­i­tized sta­tis­ti­cal soft­ware pro­grams, which means that pro­fes­sion­als need a cer­tain degree of tech­ni­cal exper­tise as a mat­ter of course. Mak­ing the sys­tems work is the first and most impor­tant part, but the job usu­al­ly also requires pro­gram trou­bleshoot­ing and sys­tem secu­ri­ty mea­sures, as well as an abil­i­ty to adapt to chang­ing tech­nol­o­gy and keep­ing updates cur­rent and use­ful across mul­ti­ple platforms.

Types of Work Settings

Almost every indus­try imag­in­able has a need for data analy­sis, at least at some lev­el. Just the same, the fields of sales, mar­ket­ing, and health­care tend to have the most jobs avail­able for these pro­fes­sion­als at any giv­en time. Most pro­fes­sion­als work on teams to tack­le spe­cif­ic projects or prob­lems as need­ed. A lot of the work is done on the com­put­er, and much of it can be done from home or from a remote office though this some­times depends on the type of data being gath­ered. Pro­fes­sion­als can typ­i­cal­ly expect to work stan­dard hours, though impor­tant projects or loom­ing dead­lines can and often do require some over­time and week­end work.

Required Train­ing

A uni­ver­si­ty edu­ca­tion is almost always essen­tial for this sort of work. Most employ­ers require data ana­lysts to hold at least a bachelor’s degree, prefer­ably in sta­tis­tics, com­put­er sci­ence, or busi­ness admin­is­tra­tion, though there are times when oth­er course­work may be accept­able if the can­di­date can also demon­strate sub­stan­tial expe­ri­ence work­ing in a relat­ed field. Many of the best paid and most suc­cess­ful ana­lysts hold master’s degrees or doc­tor­ates, which gives them more exper­tise and usu­al­ly also guar­an­tees high­er pay.

See the hun­dreds of Data Ana­lyst jobs now avail­able in New York City below: