This blog post is part of our Data Gov­ernance series. In the first post, we presen­ted Data Gov­ernance (DG) as the driver to achiev­ing data excel­lence, and noted that a crit­ical factor in imple­ment­ing DG suc­cess­fully is to focus on tan­gible, out­come-driven ini­ti­at­ives. DG shouldn’t be a merely the­or­et­ical exer­cise involving data, roles, and own­er­ship; it must cover essen­tial tech­nical domains like the Data Cata­logue, Data Qual­ity, Mas­ter Data Man­age­ment (MDM), and DevOps. In this art­icle, we’ll explore the Mas­ter Data Man­age­ment domain in depth, examin­ing how it enhances data gov­ernance by fos­ter­ing trust and ensur­ing the qual­ity of core data entit­ies across the organisation.

Over the years, organ­isa­tions have encountered a range of data chal­lenges, many of which have been driven by the grow­ing volume, velo­city, and vari­ety of data, along with the rapid evol­u­tion of tech­no­logy and busi­ness pro­cesses. One chal­lenge that con­tin­ues to grow – likely due to the increas­ing diversity of applic­a­tions and data sys­tems – is the lack of con­sol­id­ated core entit­ies across sys­tems and busi­ness units. Core entit­ies, like cus­tom­ers, products, sup­pli­ers, and employ­ees, serve as the back­bone of organ­isa­tional data, and when these core entit­ies are not fully stand­ard­ised and integ­rated, incon­sist­en­cies and duplic­ates can neg­at­ively impact all areas of the com­pany, from daily oper­a­tions to ana­lyt­ical reporting.

For instance, busi­ness users may find incon­sist­en­cies in cus­tomer inform­a­tion between ERP and CRM sys­tems, thus wast­ing valu­able time resolv­ing such issues. BI teams may face com­plic­a­tions when mul­tiple rows from dif­fer­ent source sys­tems rep­res­ent the same sup­plier, requir­ing addi­tional effort and com­plex logic in backend ETLs. And in reports, the same product may appear in mul­tiple bars or lines, mean­ing mis­lead­ing con­clu­sions and the need for spe­cial­ised know­ledge to inter­pret res­ults correctly.

MDM addresses these chal­lenges by cre­at­ing a single, uni­fied view of core entit­ies’ data, known as the golden record, ensur­ing that all sys­tems and stake­hold­ers work with con­sist­ent, accur­ate, and reli­able data. How­ever, imple­ment­ing MDM can be a com­plex and chal­len­ging pro­cess, involving sev­eral factors that require care­ful consideration.

These factors include identi­fy­ing the required fea­tures and func­tion­al­it­ies, select­ing the appro­pri­ate tech­no­logy stack, fos­ter­ing col­lab­or­a­tion between busi­ness and IT teams, determ­in­ing which data sys­tems require integ­ra­tion, and decid­ing the right imple­ment­a­tion approach:  ana­lyt­ical, trans­ac­tional, or a com­bin­a­tion of both.

Mas­ter Data Man­age­ment – Features

When imple­ment­ing MDM, it’s import­ant to recog­nise that while some fea­tures are essen­tial for every organ­isa­tion, oth­ers may be optional depend­ing on spe­cific busi­ness and IT needs. Under­stand­ing which fea­tures are crit­ical helps to build an MDM strategy that aligns with your objectives.

The three must-have fea­tures are:

  1. Data match­ing and data dedu­plic­a­tion: Data match­ing focuses on identi­fy­ing and uni­fy­ing records that belong to the same entity, even when they dif­fer due to incon­sist­en­cies, vari­ations or errors, whereas data dedu­plic­a­tion elim­in­ates duplic­ate records rep­res­ent­ing the same entity to estab­lish a single, accur­ate ver­sion (the golden record). This pro­cess can be per­formed manu­ally or auto­mated using tech­niques such as determ­in­istic or fuzzy matching.
  2. Stew­ard­ship and gov­ernance: Appoin­ted own­ers and stew­ards play a key role in defin­ing how data match­ing and dedu­plic­a­tion should be executed, estab­lish­ing guidelines for manual pro­cesses or set­ting rules and thresholds for auto­mated tasks. Addi­tion­ally, they review, cur­ate, and approve the res­ult­ing golden records, man­age excep­tions, and col­lab­or­ate with data cre­at­ors and manip­u­lat­ors as necessary.
  3. Data integ­ra­tion and syn­chron­isa­tion: Mas­ter data should be con­sist­ently avail­able, up-to-date, and aligned across the organisation’s vari­ous data sys­tems, includ­ing data ware­houses and trans­ac­tional source sys­tems. As explained in the fol­low­ing sec­tion, the sys­tems which are integ­rated and syn­chron­ised with the MDM applic­a­tion will depend on the type of MDM implementation.

Another three optional (but rel­ev­ant) fea­tures are:

  • Data explor­a­tion: Before appoin­ted own­ers and stew­ards can pro­ceed with data match­ing and dedu­plic­a­tion to define the golden record, it is essen­tial to explore and under­stand the exist­ing data. How­ever, this step may not be neces­sary if users already have access to other data explor­a­tion tools.
  • Hier­archy Man­age­ment: MDM ensures con­sist­ency by stand­ard­ising busi­ness terms, data prac­tices, and attrib­ute hier­arch­ies. It allows users to define not only the val­ues for each attrib­ute, but also their hier­arch­ical rela­tion­ships, enabling a struc­tured approach to data organ­isa­tion. This fea­ture may not be required in the MDM applic­a­tion if it is already handled by the organisation’s report­ing or ana­lyt­ical platform.
  • Work­flow col­lab­or­a­tion: MDM sup­ports seam­less team­work by enabling task-shar­ing and coordin­a­tion. For instance, users can man­age approval pro­cesses when defin­ing new golden records or new data match­ing rules, ensur­ing that appoin­ted own­ers always have vis­ib­il­ity and con­trol of new definitions.

MDM Imple­ment­a­tion Types

A crit­ical decision in MDM is choos­ing the right imple­ment­a­tion type. This choice not only determ­ines the cap­ab­il­it­ies of the solution—for instance, in an Ana­lyt­ical MDM imple­ment­a­tion, the out­puts (golden records) are applied only to the ana­lyt­ical plat­form, whilst in Oper­a­tional and Hybrid MDM imple­ment­a­tions, the out­puts are applied to the source sys­tems and onwards—but it also determ­ines the com­plex­ity of the solu­tion. Some imple­ment­a­tion types can be sig­ni­fic­antly more chal­len­ging depend­ing on the tech­no­logy stack and sys­tem integ­ra­tions required.

In an Ana­lyt­ical MDM, the MDM hub con­nects solely to the ana­lyt­ical plat­form, leav­ing source sys­tems unchanged and focus­ing exclus­ively on enhan­cing report­ing and decision-making:

In con­trast, in an Oper­a­tional MDM, the MDM hub con­nects and syn­chron­ises with trans­ac­tional sys­tems. This not only enhances report­ing and decision-mak­ing, but also improves the accur­acy and effi­ciency of day-to-day busi­ness oper­a­tions as well. How­ever, this approach demands greater organ­isa­tional matur­ity and is more com­plex than an Ana­lyt­ical MDM:

Lastly, the Hybrid-Enter­prise MDM imple­ment­a­tion com­bines both Ana­lyt­ical and Oper­a­tional approaches. It con­nects to both trans­ac­tional sys­tems and the ana­lyt­ical plat­form, sup­port­ing daily busi­ness oper­a­tions, report­ing, and decision-mak­ing, whilst offer­ing enhanced scalab­il­ity and flex­ib­il­ity across sys­tems and data domains. How­ever, this approach is highly com­plex and demands a sig­ni­fic­ant level of MDM matur­ity for suc­cess­ful execution:

MDM Tech­no­lo­gies

When imple­ment­ing MDM, organ­isa­tions typ­ic­ally have two main tech­no­logy options: lever­aging their exist­ing data plat­form and tech­no­logy stack, or invest­ing in a ded­ic­ated MDM-spe­cific tool, based on the required fea­tures and their resources.

  1. Using the Exist­ing Ana­lyt­ics Stack: This approach is well-suited for organ­isa­tions that require only basic or essen­tial MDM fea­tures. Lever­aging the exist­ing ana­lyt­ics stack allows busi­nesses to imple­ment MDM without incur­ring addi­tional licens­ing expenses, mak­ing it a cost-effect­ive start­ing point. A com­mon first step is to adopt an Ana­lyt­ical MDM imple­ment­a­tion type using the organisation’s cur­rent ana­lyt­ics infra­struc­ture. This strategy enables teams to estab­lish found­a­tional pro­cesses and work­flows, gradu­ally improv­ing MDM prac­tices whilst main­tain­ing flex­ib­il­ity. How­ever, this option often requires greater tech­nical expert­ise, as the lack of a user-friendly inter­face can make it less access­ible for non-tech­nical users.
  2. Using an MDM-Spe­cific Tool: MDM-spe­cific tools are designed to offer advanced fea­tures and an intu­it­ive UI, mak­ing them ideal for organ­isa­tions with more com­plex MDM require­ments. These tools sim­plify the pro­cess of defin­ing, review­ing, and approv­ing MDM ele­ments – i.e. rules and golden records – enabling seam­less col­lab­or­a­tion between tech­nical and non-tech­nical users. In addi­tion to core func­tion­al­it­ies like data match­ing, dedu­plic­a­tion, and hier­archy man­age­ment, many MDM tools provide value-added cap­ab­il­it­ies such as AI-driven recom­mend­a­tions, auto­mated work­flows, and built-in com­pli­ance fea­tures. These advanced options not only enhance oper­a­tional effi­ciency but reduce the depend­ency on IT for routine tasks too. Although MDM-spe­cific tools often require a higher ini­tial invest­ment, they provide a more com­pre­hens­ive solu­tion for organ­isa­tions seek­ing to scale their MDM prac­tices and to address com­plex data gov­ernance chal­lenges effectively.

Con­clu­sions

The import­ance of MDM as a found­a­tion for effect­ive Data Gov­ernance can­not be over­stated. By ensur­ing the accur­acy, con­sist­ency, and trust­wor­thi­ness of core data entit­ies, MDM addresses one of the most press­ing chal­lenges that organ­isa­tions face: the lack of con­sol­id­ated and stand­ard­ised data across sys­tems and busi­ness units.

Today we have explored how MDM strengthens Data Gov­ernance by cre­at­ing a golden record for core entit­ies and provid­ing key fea­tures such as data integ­ra­tion, explor­a­tion, dedu­plic­a­tion, and work­flow col­lab­or­a­tion. We’ve also seen the vari­ous MDM imple­ment­a­tion approaches and out­lined the key tech­no­logy options available.

As busi­nesses con­tinue to gen­er­ate and rely on vast amounts of data and diverse sys­tems, invest­ing in a robust MDM solu­tion is no longer optional: it is essen­tial! Organ­isa­tions that pri­or­it­ise MDM will not only enhance oper­a­tional effi­ciency but also enable bet­ter decision-mak­ing, pro­mote col­lab­or­a­tion, and increase trust in data.

Ready to trans­form your DG prac­tices? At ClearPeaks, we spe­cial­ise in imple­ment­ing robust MDM solu­tions tailored to your organisation’s unique needs. Whether you’re look­ing to stream­line oper­a­tions, improve decision-mak­ing, or pro­mote col­lab­or­a­tion through trus­ted data, our experts are here to help. Con­tact us today to learn how we can guide your organ­isa­tion on the jour­ney to data excel­lence and unlock the full poten­tial of your data assets!