This title will sound provocative to many of you. Why on
earth would someone want to build a Master Data Management solution when there
are so many off-the-shelf products that can do this for you? Why not simply
install turnkey software from a market leader such as Informatica, Stibo,
Semarchy, SAP, or IBM just to name a few?
That would certainly save costs, speed up the
time-to-market, and provide a much more robust and stable solution than a
Are we so presumptuous as to consider that we can do better?
And if not, why then reinvent the wheel? In this series, we will explore this
question through three distinct articles, each shedding light on a different
facet of the decision to build a custom Master Data Management solution.
We ran this project for a global construction company
present on every continent. Through its strategy of growing by acquisition,
many of the company’s local subsidiaries retained their own ERP and production
Needless to say, negotiating better deals with suppliers
or sharing up-to-date information about customers across the company is rather
challenging in such an environment!
Another limitation was the lack of integration across
systems even within the same legal entity. Next to the ERP which is essentially
used by accounting and cost control, additional stand-alone tools are being
used to cater for each department’s specific processes. There is a tool for
recruitment, another for people management, one (actually many) for tendering,
some more for document management, etc…
In this ecosystem, finding its way becomes an art in itself!
The economic context at the time when the MDM initiative
started was quite unfavorable. In the midst of the Covid crisis, the company
faced the financial impact of lockdown and was forced to take drastic cost-saving measures to keep the boat afloat.
These uncertain times were far from being the ideal period
to suggest the funding of an MDM project, especially knowing that the ROI of
such a solution is difficult to estimate and its positive effects on the
business are not always immediately tangible.
What was obvious though is the reality of the need for an
So many hours were wasted by associates in numerous
departments, to compile Excel reports where extractions from various sources
were aggregated and cleansed, month after month.
It could take literally hours to find information on a
running construction project, just because its name is different in every
Information was scattered around, sometimes complementary,
eventually conflicting, and seldom aligned.
In order to provide easy and accurate corporate reporting,
it was crucial to combine all of the data generated by the company activity
into a single harmonized view, for each of core business entities, which were
in our case:
- legal entities (subsidiaries)
- (construction) projects
- resources (employees, contractors)
As the business was already suffering from the lack of
integration, the aim was ideally to deliver value in a fairly short term.
The first question that came up was related to the strategy.
Not doing anything was definitely not an option. Should we
then buy a solution off the shelf to address the needs, or develop our own
A series of criteria were analyzed to compare and weigh the
opportunity of going one way or the other. 2 of them were considered key
With Covid restrictions knocking at the door, the initial
investment required for an off the shelf solution was deemed too expensive by
the board, leaving no other option than the BYO model.
The conditions for such a choice to take shape were quite
- experienced in-house software development team
- availability of key functional roles (functional analysts, testers & project manager)
- ETL and modeling knowledge
- Cloud environment ready to support the initiative
The next question that needed answering was the type of the
MDM solution. Should it be an Analytical or Operational MDM?
For those who are not yet familiar with these terms, an
analytical MDM is primarily focused on improving reporting and analytics by
harmonizing source data. It usually sits in between business applications and
the data warehouse.
An operational MDM is however an integral part of the
operational process. It is used to manage centrally the master data and acts as
a unique source for downstream business applications.
For our project, the decision was to go for an analytical
MDM rather than an operational MDM in order to be the least disrupting to business
processes. In the back of our minds, the idea of making MDM a core operational
element was still strongly anchored but we would first need to show evidence of
how powerful such an application can be, by starting with its positive impact
- 1 data
proved to be the most valuable to the business on short term
- 1 MDM
entities from different sources together to produce a single view
interface allowing data stewards to interact with the system
This small scope allows us to work agile, providing a fully
functional deliverable in a short timeline. Building on this initial
implementation, other data entities and MDM features can be progressively added
as extensions of the core module.
In theory, this looks great. Dependencies and enthusiasm
however surreptitiously forced additional items to the scope, as we realized
that the project entity was connected to legal entities, clients, and resources.
Delivering the project entity therefore required that we first dealt with 3 of
the remaining 4 data entities that composed the full scope!
As we opted for an analytical MDM, existing processes didn’t
require a redesign, no integration to existing systems other than the data
warehouse was needed, and real-time updates were not mandatory.
That being said, the front end provided to the data stewards
should not only let them consult MDM data but also interact with it by matching
data entities together on demand. The output would be stored in a new database
and ETL processes were to be foreseen to ingest source system data and apply
the actions performed by frontend users.
There are hundreds of tools out there and as many
programming languages, more or less fit for what we wanted to achieve. How
could we choose the most appropriate?
What is important when building your own solution is to
capitalize on the internal skills and knowledge. Unless these skills refer to a
technology no longer supported, or so obscure that it is hardly known outside
of your team, don’t try to be too creative and stick to what you know best!
Development will be faster and more stable.
For this project, several elements encouraged us to go for
an Azure-based solution:
team was already familiar with the Azure cloud environment
data warehouse was already running in Azure. As mentioned earlier, an
analytical MDM is built to support reporting & analysis. It therefore
belongs close to the data warehouse
a cloud architecture allows to quickly deploy a mockup while limiting
upfront costs and yet supporting a future increase of scope
Below is the architecture that we designed:
As we conclude this initial exploration, the question lingers: why choose to
forge a unique path in the realm of Master Data Management?
Part 2 of our
journey takes us deeper into the heart of this decision-making process,
focusing on the intricacies of design. Here, we unravel the blueprint that
guides the creation of a bespoke MDM solution. Delving into the architecture,
customization, and scalability considerations, we aim to shed light on the
nuanced artistry that distinguishes a purpose-built solution.
Join us in Part 2
as we navigate the captivating landscape of design, where innovation meets
necessity, and where the decision to craft a distinctive MDM solution begins to
unfold its true potential.
Keyrus is a leading data, digital, and business consulting firm that leverages its extensive business and technical expertise to transform data into insightful and valuable solutions.
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