The Importance of the Right Tool
The debate on how to imple­ment data pro­jects often revol­ves around tech­ni­cal choices bet­ween pro­pon­ents of free code and no-code/­low-code solu­ti­ons. Yet, expe­ri­en­ced data sci­en­tists using Python and R frame­works, as well as busi­ness users depen­dent on a user inter­face, often have moti­va­tions that extend bey­ond mere tool affi­nity.

The Core of the Issue
As this topic is a recur­rent con­cern in data pro­jects and we have amas­sed con­sidera­ble expe­ri­ence, we want to share our obser­va­tions and ana­lyze how this appa­rent con­tro­versy can be resol­ved by focu­sing on the needs of pro­ject par­ti­ci­pants.

Imple­men­ting Pro­jects in the Digi­tal World
In an era domi­na­ted by digi­tal inno­va­tion and data-dri­ven decis­i­ons, the pres­sing ques­tion is: How do we effec­tively imple­ment pro­jects? Choo­sing the right tool can greatly influence the qua­lity and effec­ti­ve­ness of the pro­ject. While open source has pro­pel­led data sci­ence and machine lear­ning in recent years, there has also been a rapid increase in ana­ly­ti­cal soft­ware.

Key Fac­tors for Pro­ject Suc­cess
What is the decisive fac­tor for pro­ject suc­cess? Cle­arly: the result, and in par­ti­cu­lar, the com­pany-wide accep­tance and vol­un­t­ary use of data-based solu­ti­ons.

Ana­ly­zing the Pro­cess
If we con­sider this pro­cess from its con­clu­sion, the ques­tion ari­ses: what can con­tri­bute to a posi­tive out­come? Ima­gine employees of a com­pany on their way to the office, repre­sen­ting the suc­cessful com­ple­tion of the pro­ject in this thought expe­ri­ment. Each per­son tra­vels in a dif­fe­rent manner—by train, bus, car, or bicy­cle. Alt­hough per­so­nal cir­cum­s­tances play a role, cer­tain pre­fe­ren­ces and beliefs also influence the choice of trans­por­ta­tion. Buses or trains involve costs, are often fas­ter, but ope­rate (at least in prin­ci­ple) in a stan­dar­di­zed and the­r­e­fore relia­ble way. You may also meet col­le­agues on the bus taking the same route and can exch­ange ideas. In a highly abs­tract sense, there is a cer­tain simi­la­rity to no-code/­low-code data solu­ti­ons. Cycling, on the other hand, costs not­hing, might be slower than the train, allows you to choose rou­tes to suit your own taste, and navi­gate around any con­s­truc­tion site inde­pendently of a time­ta­ble. Howe­ver, it also requi­res energy expen­dit­ure and some­ti­mes bra­ving the wind and wea­ther. Upon arri­ving at the office, having found a ’solu­tion’ for the route, one feels proud and can share the expe­ri­ence with others. Every data sci­en­tist will reco­gnize cer­tain par­al­lels to pro­gramming here.

NoCode/LowCode Solu­ti­ons: Strengths for People and Pro­ces­ses

Empowe­ring users wit­hout pro­gramming skills: Soft­ware plat­forms with user-fri­endly inter­faces enable indi­vi­du­als with vary­ing levels of tech­ni­cal exper­tise to engage in data ana­ly­sis. It is cru­cial that sub­ject mat­ter experts, such as engi­neers, che­mists, and pro­duc­tion mana­gers, par­ti­ci­pate in the ana­ly­sis pro­cess, ther­eby con­tri­bu­ting their tech­ni­cal strengths.
Time effi­ci­ency: By using soft­ware plat­forms, data ana­lysts and busi­ness experts can con­cen­trate on the actual ana­ly­ses and their appli­ca­tion to busi­ness pro­ces­ses, rather than coding and trou­ble­shoo­ting. Howe­ver, it should also be noted that users must be trai­ned in both the use of the plat­form and the asso­cia­ted (sta­tis­ti­cal) methods and inter­pre­ta­tion of results.
Col­la­bo­ra­tion and team dyna­mics: NoCode/LowCode solu­ti­ons are often desi­gned for col­la­bo­ra­tion, fos­te­ring a team spi­rit. This can enhance team dyna­mics as mem­bers can seam­lessly col­la­bo­rate on pro­jects, share insights, and make data-dri­ven decis­i­ons tog­e­ther. With col­le­agues from the spe­cia­list depart­ment, the leap into ope­ra­tio­nal use is quicker.
Trust in the com­pany: Most data tools also gra­phi­cally depict the ana­ly­sis pro­cess. This greatly aids in explai­ning solu­ti­ons to stake­hol­ders and ensu­ring no one is excluded.

Free Code Solu­ti­ons Also Have Their Merits

Skills deve­lo­p­ment: Opting to pro­gram ana­ly­ti­cal solu­ti­ons can lead to skills deve­lo­p­ment, par­ti­cu­larly in pro­gramming and scrip­ting. Team mem­bers who choose this path may find it per­so­nally rewar­ding and pro­fes­sio­nally advan­ta­ge­ous, which in turn increa­ses moti­va­tion and com­mit­ment to the pro­cess.
Fle­xi­bi­lity and adap­ta­bi­lity: Pro­gramming solu­ti­ons offer unpar­al­le­led fle­xi­bi­lity and adap­ta­bi­lity, allo­wing data ana­lysts, engi­neers, and sci­en­tists to tailor their tools to spe­ci­fic pro­ject requi­re­ments.
Deeper under­stan­ding: Pro­gramming ana­ly­tics solu­ti­ons can lead to a deeper under­stan­ding of the under­ly­ing ana­ly­tics pro­ces­ses, which is par­ti­cu­larly valuable for data sci­en­tists see­king a com­pre­hen­sive grasp of the methods they employ.

Every pro­ject must be eva­lua­ted to deter­mine which tools and resour­ces will achieve the best result for the team and the com­pany. Fun­da­men­tally, howe­ver, buil­ding trust is cru­cial. Good data sci­en­tists know how to pre­sent the solu­ti­ons they have pro­grammed them­sel­ves and how to make the path to them com­pre­hen­si­ble. But this also costs time and energy. With NoCode/LowCode, this can some­ti­mes be achie­ved in a more resource-effi­ci­ent way.

Talk to us about your ana­ly­ti­cal ques­ti­ons and pro­ces­ses! We are fami­liar with the ana­ly­ti­cal infra­struc­ture and know how to design people-ori­en­ted pro­ces­ses. Tog­e­ther with you, we at Data­he­arts will deve­lop a plan that will get you to your goal.