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Data Squads

What are data squads?

This is how we refer to our teams of experts in various specialised fields.

We customise these teams to the requirements of your project by assembling them from the following roles:

Data Squad roles

The Data Stra­te­gist is respon­si­ble for gui­ding the entire life­cy­cle of a data pro­ject, from its con­cep­tion to its ongo­ing main­ten­ance and impro­ve­ment. They play a cru­cial role in alig­ning data initia­ti­ves with busi­ness objec­ti­ves, ensu­ring data qua­lity and com­pli­ance, and maxi­mi­zing the value deri­ved from data assets.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Define Objec­ti­ves and Goals, Data Gover­nance, Data Coll­ec­tion and Inte­gra­tion, Data Ana­ly­sis and Insights, Data Stra­tegy Road­map, Stake­hol­der Com­mu­ni­ca­tion, Con­ti­nuous Impro­ve­ment, Com­pli­ance and Ethi­cal Con­side­ra­ti­ons, ROI Assess­ment.

The data scientist’s role is to extract meaningful insights from data and deve­lop models that can be applied to solve spe­ci­fic pro­blems or make infor­med decis­i­ons within a data pro­ject. They are cen­tral to the entire data life­cy­cle, from data acqui­si­tion and pre­pa­ra­tion to model­ling and deploy­ment, and they are cri­ti­cal in unlo­cking the value of data for an orga­niza­tion.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Data Coll­ec­tion and Pre­pa­ra­tion, Data Ana­ly­sis, Model Deve­lo­p­ment, Data Visua­liza­tion, Fea­ture Engi­nee­ring, Eva­lua­tion and Vali­da­tion, Col­la­bo­ra­tion, Con­ti­nuous Impro­ve­ment, Ethi­cal Con­side­ra­ti­ons, Com­mu­ni­ca­tion.

Data engi­neers are respon­si­ble for buil­ding and main­tai­ning the infra­struc­ture that enables data pro­jects to func­tion effec­tively. They ensure that data is coll­ec­ted, stored, and pro­ces­sed in a way that is con­sis­tent, relia­ble, and ready for ana­ly­sis by data sci­en­tists and other stake­hol­ders. Their work is fun­da­men­tal to the suc­cess of data-dri­ven initia­ti­ves.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Data Inges­tion, Data Sto­rage, Data Trans­for­ma­tion, Data Pipe­line, Data Model­ling, Data Inte­gra­tion, Data Qua­lity and Vali­da­tion, Per­for­mance Opti­miza­tion, Sca­la­bi­lity, Data Secu­rity, Docu­men­ta­tion, Col­la­bo­ra­tion, Moni­to­ring and Main­ten­ance, Data Gover­nance.

Data con­sul­tants ana­lyse data to gain insights and sup­port busi­ness decis­i­ons. They coll­ect, orga­nise and inter­pret data to solve pro­blems, iden­tify trends and deve­lop stra­te­gies. Their tasks often include data cle­an­sing, model­ling and visua­li­sa­tion as well as advi­sing cli­ents on how to opti­mise their data stra­tegy and infra­struc­ture.

The soft­ware architect’s role in a data pro­ject is to design the soft­ware infra­struc­ture that sup­ports data coll­ec­tion, pro­ces­sing, and ana­ly­sis. They pro­vide the tech­ni­cal vision and gui­dance nee­ded to create sys­tems that are sca­lable, secure, and ali­gned with the project’s objec­ti­ves. Their work is essen­tial for the suc­cessful imple­men­ta­tion of data-dri­ven initia­ti­ves.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Sys­tem Archi­tec­ture, Tech­no­logy Sel­ec­tion, Data Inte­gra­tion, Sca­la­bi­lity and Per­for­mance, Secu­rity, Data Flow and Pro­ces­sing, Midd­le­ware and APIs, Data­base Design, Data Access Layer, Error Hand­ling and Resi­li­ence, Moni­to­ring and Log­ging, Docu­men­ta­tion, Col­la­bo­ra­tion

A data­base architect’s role in a data pro­ject is to design and manage the data­base sys­tems that store and manage pro­ject data. They ensure that the data­base is well-struc­tu­red, secure, and opti­mi­zed for data access and ana­ly­sis while alig­ning with the project’s goals and requi­re­ments. Their exper­tise is essen­tial for main­tai­ning the inte­grity and per­for­mance of data-dri­ven initia­ti­ves.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Data Model­ling, Data­base Design, Tech­no­logy Sel­ec­tion, Query Opti­miza­tion, Data Secu­rity, Data Inte­grity, Sca­la­bi­lity, Data Migra­tion, Per­for­mance Moni­to­ring, Backup and Reco­very, Docu­men­ta­tion, Col­la­bo­ra­tion, Com­pli­ance and Gover­nance, Capa­city Plan­ning.

The role of a pro­ject mana­ger in a data pro­ject is to pro­vide over­all lea­der­ship and coor­di­na­tion to ensure that the pro­ject is exe­cu­ted effi­ci­ently, on time, and within bud­get. They act as a bridge bet­ween tech­ni­cal teams and stake­hol­ders, hel­ping to main­tain focus on the project’s objec­ti­ves and navi­gate the com­ple­xi­ties of data-dri­ven initia­ti­ves.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Pro­ject Plan­ning, Resource Manage­ment, Bud­get Manage­ment, Risk Assess­ment and Miti­ga­tion, Stake­hol­der Com­mu­ni­ca­tion, Scope Manage­ment, Time­line and Mile­stone Track­ing, Qua­lity Assu­rance, Docu­men­ta­tion, Issue Reso­lu­tion, Change Manage­ment, Resource Coor­di­na­tion, Per­for­mance Report­ing, Adhe­rence to Best Prac­ti­ces, Clo­sure and Eva­lua­tion.

A pro­duct manager’s role in a data pro­ject is to define, deve­lop, and deli­ver data pro­ducts that meet user needs, align with busi­ness goals, and pro­vide value through data-dri­ven insights. They act as the advo­cate for users and the dri­ver of the product’s suc­cess, ensu­ring that it evol­ves to meet chan­ging requi­re­ments and remains com­pe­ti­tive in the mar­ket­place.

Key respon­si­bi­li­ties and con­tri­bu­ti­ons: Define Pro­duct Vision and Stra­tegy, Mar­ket Rese­arch, Pro­duct Plan­ning, Prio­ri­tiza­tion, Requi­re­ments Defi­ni­tion, User Expe­ri­ence (UX) Design, Deve­lo­p­ment and Deli­very, Test­ing and Qua­lity Assu­rance, User Enga­ge­ment, Ana­ly­tics and Metrics, Inter­ac­tive Deve­lo­p­ment, Mar­ket Launch, Com­pe­ti­tive Ana­ly­sis, Data Gover­nance, Bud­get Manage­ment, Stake­hol­der Com­mu­ni­ca­tion, User Feed­back Inte­gra­tion.