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The Five Facets Of Product Data How to Turn Your Product Data into Actionable Results with MDM


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Table of Contents Executive Summary ......................................................................................... 3

MDM for Material Data ................................................................................... 7

Use Case and Business Benefits ................................................................................................. 7

Capabilities Needed from a MDM Solution for Material Data .................................................. 9

MDM for Lean Managed Services (or Product as a Service) ......................... 11

Use Case and Business Benefits ............................................................................................... 11

Capabilities Needed from a MDM Solution for Lean Managed Services ................................ 12

MDM for Regulated Products ....................................................................... 13


Capabilities Needed from a MDM Solution for Regulated Products ....................................... 14

Product Information Management ............................................................... 16


Capabilities Needed from a PIM Solution ................................................................................ 17

MDM for Anything ........................................................................................ 18


Capabilities Needed from a MDM for Anything ....................................................................... 19

About Talend and Talend MDM .................................................................... 20


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EXECUTIVE SUMMARY

Product data is everywhere in your organization. To maximize its value, product data

needs to be in the hands of many stakeholders, including your customers and employees.

The activities that need to access, author, and share critical information about product

data depends on your industry, but the following group functions can leverage this

information: Research and Development, Purchasing, Sales and Marketing, After Sales

Services, Manufacturing, Supply Chain, Risk and Compliance, Finance, Human Resources,

etc. Because of this wide reach and diversity of context, controlling the way that data is

created, updated and replicated is difficult. Making sure that accurate information can be

accessed at the right time and right place to drive the correct action is also a complicated

and time‐consuming exercise.

New trends have raised business expectations, such as cross‐channel marketing and e‐

commerce; personalization of customer offers including personalized product

configuration and/or pricing; incorporating data from the Internet Of Things for new

insight; extensive regulations for product safety; and requirements to speed the product

introduction process. Despite significant investments in IT including Enterprise Resource

Planning, Product Life Cycle Management, Digital Marketing and e‐commerce systems,

organizations are still struggling to maintain a sanctioned, accurate, and actionable set of

product data that they can safely expose to their employees, customers, stakeholders, and

applications.

Typical business drivers of MDM for Product Data


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The only way to solve this challenge is to establish a dedicated initiative, backed up with

people, processes and technology, and this is called Master Data Management. Master

Data Management is a data‐centric business initiative that focuses on data that is highly

shared across the enterprise. Master Data can be segmented by data domains, and the

most commonly considered domains in the context of those initiatives is “party” data

(such as customers, members, citizens, patients, employees, or vendors) and product

data. MDM for Product Data focuses on the management of the domain relating to

products and other things. Product data includes finished products, parts, material, assets,

services and financial instruments.

MDM for Product Data is a well‐established market, representing somewhere between

$500M and $1B depending on analysts’ estimates, and has a five year annual compound

growth rate of 9% (according to Gartner). Based on our experience and the feedback from

Talend MDM customers, we have seen several product data use cases.

The MDM market (Gartner estimates)


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MDM for Product Data is often incorrectly considered as a synonym for Product

Information Management (PIM). PIM is one obvious use case of MDM for Product Data,

but there are many others.

The purpose of this whitepaper is to analyze five frequent use cases of MDM for Product

Data: MDM for Material Data, MDM for Lean Managed Services (or Product‐as‐a‐Service),

MDM for Regulated Products, Product Information Management and MDM for

“Anything”. Each use case reviews the connected systems, processes, business benefits

and key capabilities.

1‐ MDM for Material Data aims to centrally manage information about spare parts,

raw materials and final products, and share this trusted data across organizations,

processes and information systems.

2‐ MDM for Lean Managed Services is a use case for companies that operate a large

amount of equipment. For example, this can be a facility manager that operates a

set of devices to deliver IT or network capabilities to its customers; or a utility

provider that manages a networked grid; or a provider of Maintenance, Repair and

Operations (MRO) related services. For a company that sells products, this use

case will also support activities occurring once the product is in the hands of the

customer ‐ this includes the “Product‐as‐a‐Service” opportunity that comes with

the Internet of Things.

3‐ MDM for Regulated Products happens when products must comply with

government or industry regulations. It mandates adherence to standard

codifications with information traceability on how products are sourced, tested,

manufactured, packaged, documented, transported or promoted.

4‐ Product Information Management or PIM refers to “processes and technologies

focused on centrally managing information about products, with a focus on the

data required to market and sell the products through one or more distribution

channels. A central set of product data can be used to feed consistent, accurate

and up‐to‐date information to multiple output media such as web sites, print

catalogs, ERP systems, and electronic data feeds to trading partners” (Wikipedia).

5‐ MDM for Anything focuses on any specific products or things that an organization


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has to share across lines of business and has specific data structures and related

processes. For example, Professional Services companies have work breakdown

structures, Environmental Services have waste types, Life Sciences companies have

compounds, etc. When these “things” are widely shared across activities and the

enterprise is struggling with multiple and inefficient data entry or reporting

systems, then there is a place for MDM for Product Data.

The Five facets of Product Data


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MDM FOR MATERIAL DATA

Use Case and Business Benefits

MDM for Material Data is a use case that applies

to the manufacturing industry for managing

product data across engineering, procurement,

manufacturing, and the supply chain, as well as

along the customer facing processes to sell and

configure products. “Material” refers to

potentially any inventory item that can be

uniquely identified by a SKU (Stock Keeping Unit)

from the raw material to the customer delivered

finished goods. Product parts can also be taken in

consideration for this use case.

One would think that most companies have already established a single point of control to

manage material data across their business processes in their ERP system, but the reality

is that few companies have consolidated all their processes in a single ERP instance. Many

companies have a specific system (or systems) and related process for Research and

Development, the so‐called Product Lifecycle Management (PLM) systems ; for CRM,

sales and distribution; and for procurement and maintenance, etc. They may also have

different systems and processes across lines of business or geographies as well.. This

application heterogeneity tends to increase over time due to mergers and acquisitions,

business processes that are being outsourced or insourced, or on‐premise and cloud

systems that have expanded at the periphery of IT rather than at its core.

This “accidental architecture” results in

inconsistent and replicated views of product

data with cascading process inefficiencies

across the product life cycle. By engaging in

MDM for Material Data initiatives,

companies can create shared, up‐to‐date

and accurate views of products. This starts

early in the product lifecycle, where new

product characteristics have to be shared

beyond the Research & Development team


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with other activities such as procurement, marketing, supply chain, or accounting and

finance. Reducing time to market while launching new products or reducing procurement

and manufacturing costs through improved parts reuse are examples of the business

benefits achieved.

Once the product is ready for distribution, strong alignment is needed between numerous

stakeholders across sales, logistics, finance and manufacturing, especially in the case of

build‐to‐order products. Benefits then include inventory optimization, cost reduction,

optimized spend management when negotiating procurement contracts, and improved

visibility into procurement and sourcing. Although some of these functions may be

covered by an ERP system, organizations may want to expand its scope. For example,

many organizations are looking for more integrated, agile and synchronized planning

processes instead of using ad‐hoc spreadsheets or disparate planning applications.

IT agility is another strong business driver for MDM for Material Data due to the increasing

costs of data migration and data quality management. Business agility is another potential

benefit, especially in the case of mergers and acquisitions where rapid consolidation of

activities leads to more efficient procurement and distribution of items across the supply

chain.


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Capabilities Needed from a MDM Solution for Material Data

Evaluating the efforts to create the single source

of trusted data may not be straightforward since

the company may not know the content of the

data that they will have to deal with. As a result,

the MDM solution must enable progressive

implementation. It may just start with gathering

the data and assessing it with a data profiling

environment before starting to model the master

data. Strong data quality management, including

profiling, standardization, categorization and

matching, together with flexible modeling, are

therefore key required capabilities.

Standardization and categorizing capabilities should be of particular importance, because

this will drive the accessibility of the product data. It will enable hierarchy browsing,

faceted search or search by synonyms. In some industries, codification standards exist to

ease the exchange of production information between business partners to ensure

compliance with regulations. MDM solutions should support these standards which are

often relatively complex well‐defined XML schemas.

Data integration is an important component for an MDM solution. The implementation

style of MDM often starts with a so‐called consolidation style where the data flows in

batch mode from the operational systems to the MDM hub. Then the trusted data flows

back in batch mode to the source systems. Some companies prefer to start with a model

where there are real‐time bidirectional communications between the MDM system and

source systems (this is sometimes referred as the transactional, or coexistence,

implementation style), but the prerequisite is that those existing applications have well‐

defined access points such as web services that can be reused for the MDM project. This

may be the case for companies that have achieved high maturity in adopting strong

architectural standards for application interoperability across their IT landscape.

Otherwise, the batch mode should be an easier, less intrusive and faster way to connect

applications to start your MDM for Material Data initiative.

Whether real‐time or batch, integration is an important building block, especially with ERP

and PLM solutions. Integration represents a significant portion of development efforts and


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significantly impacts the total cost of ownership. The integration capability will be of

particular value for companies that see their business applications moving to the Cloud.

Analysts such as Ventana Research have shown that the Cloud tends to improve

integration efficiencies, e.g. to fulfill a customer order, different cloud applications may be

used from CRM to billing to warehouse management and distribution. MDM for Material

Data would then be a key component to provide a uniform and shareable product view

across these applications, but then its ability to connect easily and efficiently to those

external applications is critical.

Data stewardship capabilities are also important to consider. In order to be implemented

incrementally, MDM for Material Data should have minimal impact in the way material

data is authored in the system, in the existing ERP and/or PLM applications. The goal is

generally not to re‐engineer these steps, but to add data governance capabilities to

ensure data accuracy, trustability and accessibility. Data quality issues linked to

reconciliation, accessibility, categorization and standardization of data has to be managed

a posteriori in this use case. It requires data stewardship involvement on an ongoing basis.


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MDM FOR LEAN MANAGED SERVICES (OR PRODUCT‐AS‐A‐SERVICE)


MDM for Lean Managed Services is a use case that

we are seeing in companies that operate a large

amount of product or equipment. For example,

this can be a facility manager that operates a set

of devices to deliver IT or network capabilities to

its customers; a utility provider that manages a

networked grid; or a provider of Maintenance,

Repair and Operations (MRO) related services.

Sometimes also referred as MDM for Asset Data,

this use case also applies to enterprises that are

providing support and services for their product

line or more generally companies that need to manage equipment for maintenance

purposes or provide value added services once it is in the hands of their customer,

Companies, no matter if they insource shared

services or outsource business processes, are

under pressure to reduce costs and add value to

their customers. MDM provides a shared, single

and accurate view of the equipment being

managed. MDM enables you to standardize

equipment characteristics and behavior, and in

some cases even make them actionable by

orchestrating their remote operations. With this

consolidated view, you can more efficiently

manage equipment allocation leading to reduced

costs.

Consider MRO processes in the manufacturing

industry. Once companies have re‐engineered their supply chain, they find that MRO

processes represent a significant portion of their remaining product costs. Surveys show

that for many organizations the MRO inventory accounts for 15 to 40 percent of the

annual procurement budget. By improving inventory control and management through

MDM, you can reduce parts procurement and inventory costs. Also companies can


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differentiate themselves by selling their product as a service, and providing innovative

“after sales” services to their customers.

Now that we are entering the Internet of Things (IoT) era, where devices can report on

their behavior and be operated remotely, we see a new, innovative use case for MDM.

General Electric is a pioneer in this area with their Industrial Internet initiative, which is

about operating "ecosystems of connected machines to increase efficiency, minimize

waste, and make the people operating them make smarter decisions". This is a data‐

driven initiative at the crossroads of MDM and Big Data, which should take a central role

in digital transformations : Gartner predicts that “by 2017, more than 20% of customer‐

facing analytic deployments will provide product tracking information leveraging the IoT”.

Capabilities Needed from a MDM

Solution for Lean Managed Services

MDM for Lean Managed Services is about creating

a uniform and actionable view across a network of

equipment. Many companies start with a very

heterogeneous set of applications or complex

spreadsheets to manage equipment. To

implement a MDM system, you will first need

strong data migration capabilities. The data quality

component of your MDM solution is required, not only to assess and profile your data, but

also to help you standardize data. Since this is an ongoing effort, other requirements

include strong data quality usability features together with robust reporting and

monitoring capabilities.

Once populated, the MDM may become the place where the master data is authored,

requiring workflow capabilities with collaborative authoring.

To make your MDM a hub that can remotely trigger actions to the objects that it

references, strong ESB capabilities, including advanced security, fault tolerance and audit

trail management, are needed.


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MDM FOR REGULATED PRODUCTS


MDM for Regulated Products is a use case when

products must comply with government or

industry regulations. It mandates adherence to

standard codifications with information traceability

on how products are sourced, tested,

manufactured, packaged, documented,

transported or promoted. Traceability mandates

are increasing in velocity and precision, requiring

standardization of product attributes and

characteristics.

More and more industries need to report on the

supply chain related to their products. Life Sciences is an obvious example of an industry

whose products are heavily regulated, from the different phases of trials to manufacturing

and distribution. As an example, in December 2014 regulators in France, Germany,

Belgium and Luxembourg suspended the marketing approval of 25 generic drugs due to

concerns over the quality of data from clinical trials conducted by a leading Clinical

Research Organization (a large global company with 2,400 employees dedicated to Clinical

Research). Other industries with increasing regulation include Financial Services,

Agriculture, Healthcare, Chemicals and Consumer

Products.

The primary benefit of this use case is to minimize the

costs and risks of compliance. Even when codification

standards are not mandatory, an extended supply

chain can reap benefits in adopting standards for real‐

time exchange of information between business

partners. The supply chain process then becomes

more uniform, more efficient, higher quality, and

open for continuous improvement because it is

carefully and precisely controlled and monitored.

With a more holistic and shared view of product data

and related processes, better and more informed


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decisions about the regulated products will be made. By capturing information earlier in

the supply chain, you will be able to detect and react faster to any non‐compliance alerts.

Finally, a major reason to improve how you manage regulatory information is illustrated

by a Gartner prediction: “By 2016, 20 percent of CIOs in regulated industries will lose their

jobs for failing to implement the discipline of information governance successfully.”

Capabilities Needed from a MDM Solution for Regulated Products

One of the most established standards bodies for

products is GS1, known for providing the Global

Trade Item Number (GTIN) as the Universal

Identifier for Consumer Goods and Healthcare

Products. GS1 also provides a standard for

capture, identification, classification, sharing and

traceability of product data between business

partners. According to Wikipedia, “GS1 has over a

million member companies across the world,

executing more than six billion transactions daily

using GS1 standards”.

Complying with standards mandates that your MDM solution easily support relatively

complex semi‐structured data formats, such as EDI and XML (for example, GS1 lets you

choose between the “traditional” EDI format, EANCOM, and GS1 XML). The MDM

platform should also allow data mappings between well‐defined standards and internal

data structures with the ability to interactively view products according to both the

internal, “proprietary” view and the standardized view. Modeling capabilities such as

hierarchy management and inheritance are important to make sure that standards are

embedded into your specific data models, and to ensure that changes are automatically

applied to all the data structures.

Regulated products need precise product supply chain traceability and a well‐defined and

auditable workflow. Many stakeholders need access to shared product information, and

the ability to update information according to their activity under their control. Workflow

capabilities for data authoring and compliance checking might be important as well.


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This use case also mandates strong data quality capabilities including parsing,

standardization, entity resolution and reconciliation. This is needed to get standardized

classifications out of your legacy product data, as product categories may have been

initially coded into long freeform text in legacy systems rather than in well‐defined and

structured attributes.

Interfaces may be as “basic” as import and export but they should be more sophisticated

to connect business partners and regulatory institutions in real‐time. For scenarios where

MDM integrates with an Enterprise Service Bus, security, access control, fault tolerance

and audit trails become important along with workflow capabilities for data authoring and

compliance checking.


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PRODUCT INFORMATION MANAGEMENT


Product Information Management (PIM) is the

most popular use case of MDM for Product Data.

PIM has become critical for companies that

distribute off‐the‐shelf products, especially where

those products are distributed across multiple

channels. As PIM is focused on customer facing

processes, business drivers are relatively

straightforward to define. Trusted and easily

accessible product data boosts product

attractiveness, drives new customer demand, and

increases customer cross‐sell, up‐sell and

conversion rates.

The business case for PIM is pretty well documented by consulting companies and

industry analysts, e.g. Ventana Research shares very interesting benchmark data in a

survey whose extracts are publicly available in a blog by Mark Smith called “Product

Information Management is for Business” They found that PIM benefits include

eliminating errors while referencing product data, improving sharing of data with

customers, increasing cross‐sell and up‐sell rates, and improving the customer experience.

Although PIM is not new, the research shows that organizations believe they still have a

long way to go on their maturity curve, with only 25% trusting their PIM processes

completely. 94% use spreadsheets heavily or

moderately in their processes despite the fact that

20% find major errors in them frequently.

In addition to improving the product introduction

process and boosting product attractiveness, PIM

also drives the expansion of the product catalog.

Many companies are in the process of extending

their product portfolio into what is now commonly

referred as the long tail by creating online

marketplaces where third party vendors can

participate. This mandates a very efficient and low


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cost approach when referencing new products, or changing characteristics like price or

availability in real‐time. The goal is also to ensure that product data is accurate as early as

possible in the information supply chain, delegating the responsibility of data quality at

the source of input, sometimes at the vendor level.

Capabilities Needed from a PIM Solution

MDM for PIM needs both strong front‐end and

back‐end capabilities. The front‐end holds the

process of referencing products and making sure it

is the most complete and accurate data for

efficient distribution. Some use Commercial Off‐

The‐Shelf products that come with pre‐configured

data models, business processes and workflows,

while others prefer to work with a platform that

they can customize to their own needs and

connect to separate front‐end platforms like e‐

commerce applications.

The solution must provide strong capabilities for search, and integration with third party

products such as online catalogs, digital asset management systems, and search‐based

applications. Collaborative authoring, workflow, Business Process Management, product

categorization (such as to GS1 Global Product Categorization or ecl@ss) are important

capabilities to consider, too.

With PIM, the goal is to integrate incoming data and ensure it is of high quality before

being delivered to customer facing activities. Strong rule‐based data quality capabilities

should be provided to assess incoming data, measure and analyze its quality, delegate its

stewardship to the right stakeholders in case of inaccuracy, and control the overall

process through a rules‐based approach.

The PIM solution must connect to business partners through a supplier portal, through

open APIs, or by connecting to a Global Data Synchronization Network. Data mapping to a

standard format and connectivity to external exchanges are needed as well if applicable.

Once dominated by specialized solutions that were fully focused on managing the

customer‐facing side of product data, the market is evolving towards more flexible


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solutions that can deliver a wider scope of functionality. For example, personalization of

offers ‐ a key capability for customer‐facing applications around product ‐ need to take

into consideration both product and customer data. Considering PIM as a separate

solution than the ones that as mastering essential party data such as Customer or

Supplier, may therefore appear as a short‐term choice that may turn to a mistake as the

initiative evolves over time.

MDM FOR ANYTHING


The final use case is MDM for “Anything”, or

product data that does not belong to the

aforementioned categories.

MDM for Product Data deals with the things that

you are producing, as well as the things that you

are using to produce them. Their data structure,

together with the processes to create, distribute

and use them, may be very specific to an industry,

use case or enterprise.

For example, Professional Services companies and

any company that deals with large projects, have work breakdown structures.

Environmental Services have their waste types. Life Sciences companies have their

compounds. When these things are widely shared across activities and

the enterprise is struggling with multiple and inefficient data entry or

reporting systems, then there is a place for MDM for Product Data.

In case you have found in your business such "things" that would benefit

from MDM, you may feel a little lonely while searching for literature,

well‐documented business cases to inspire you, or predefined models

and templates. But the good news is that the core discipline of Master

Data still applies. You will be able to leverage best practices to create

your shared definition of data, manage data accuracy, establish data

governance and stewardship, and connect master data to your data

sources, applications, business processes and users.


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Capabilities Needed from a MDM for Anything

With MDM for Anything, you need to define on a

case‐by‐case basis what is required from your

solution, e.g. data modeling, data quality, data

accessibility, data stewardship, and/or master data

services. To support all of these scenarios, the

flexibility of the solution is important and should

allow designing very specific data models to

connect easily to any source of data and

eventually to applications in real‐time.

What is expected from the MDM solution

corresponds to what analyst firm Gartner

describes as “multi‐vector MDM”, defined as “the application of multi‐domain MDM

across multiple industries, use cases, organizational structures and implementation

styles”. In a report called “IT Market Clock for Master Data Management”, Gartner notes

that many MDM solutions fail to provide this capability, although the market urges them

to provide it as soon as possible.

The multi‐domain dimension requires flexibility in areas such as data modeling and

defining rules for data quality controls. In the context of MDM for Anything, it allows

adaption to “fuzzy products”. It also allows management of relationships between

products and other domains such as manufacturing plants (the locations dimension),

Work Breakdown Structure (the project dimension), Supplier, Customer, etc. Note that

this is a capability that is needed for all use cases described in this paper, not only for

MDM for Anything.

The multi‐industry capability allows integrating industry specific master data and data

codifications, a topic that we investigated already in this white paper while focusing on the

MDM for Regulated Products.


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Adapting to the organization mandates that the MDM front‐end provides strong people‐

centric and collaborative capabilities for data authors and data stewards. Flexible pricing

models for key users such as authors and data stewards may be very important too in the

case of decentralized stewardship organizations that aim to move the accountability of

the data to operations. Adapting to the organization also mandates from the back‐end

side of the MDM solutions to operate seamlessly in centralized, federated or

decentralized environments.

The ability to adapt to any MDM implementation style, from very distributed to

centralized, and from “after the fact” batch models to highly transactional processes, is

also important.

Last, an MDM solution must adapt to any use case, e.g. operational, analytical, etc. With

the advent of Big Data, more and more use cases appear overtime. In this paper, we

highlighted the product‐as‐a‐service trend, where a manufacturer provides online services

around his connected products once it is in the customer’s hands. This is just one example

of the multiple product data use cases that Big Data is enabling, and that MDM solutions

will have to take into consideration in the future.


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ABOUT TALEND AND TALEND MDM

At Talend, it’s our mission to connect the data‐driven enterprise, so our customers can

operate in real‐time with new insights about their customers, markets and business.

Founded in 2006, our global team of integration experts builds on open source innovation

to create enterprise‐ready solutions that help unlock business value more quickly.

By design, Talend integration software simplifies the development process, reduces the

learning curve, and decreases total cost of ownership with a unified, open, and

predictable platform.

Talend Master Data Management (MDM) provides the foundation for successful MDM

initiatives. It unifies any amount of data ‐ from customers to products to suppliers and

beyond – into a single, actionable “version of the truth.” Talend MDM combines real‐time

data, applications, and process integration with embedded data quality and stewardship.

Talend helps you deliver on the promise of MDM in weeks, not months or years.

Although Talend MDM is designed to fit to any Master Data initiative across domains,

industries use cases, and organizations, the following five key capabilities make it an

excellent fit for the Product Data use cases described herein.

The five key capabilities of Talend MDM are:

- An active and open data model is at the core of the platform and adapts to specific

needs, business glossaries, and data models. It allows MDM initiatives to expand

across domains over time. It includes trigger‐based control and propagation rules,

role‐based access and full audit trail and data lineage capabilities.

- End‐to‐end data quality acts as a companion for effective and sustainable data

governance, even when faced with heterogeneous data sources and sometimes fuzzy

content found in MDM of Product Data initiatives. With strong capabilities for

automatic data cleansing and reconciliation, it limits manual processing of data, thus

diminishing the costs and efforts of data governance.

- The data stewardship console, together with a strong Business Process Management

and workflow engine, empowers data stewards to create and certify trustable and up‐

to‐date master records. Business Process Management is core to many MDM for

Product Data initiatives to support well‐defined collaborative business processes that

cross lines of business such as for referencing or de‐referencing products.


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- Strong data, application and process integration foundations allow Talend Platform for

MDM to stand as the information backbone and smoothly integrate in any IT

landscape ‐ in batch mode or real‐time. In MDM for Product Data initiatives, we see

many cases where Talend MDM connects in real‐time to ERPs, PLM, and even

becomes the backbone on the Internet of Things, e.g. operating and fine tuning a

network in the utilities or telecommunication industry.

- Talend Platform for MDM with Big data turns MDM of Product Data into a holistic 360°

view of products augmented by data on intentions (e.g. clickstreams), sentiments,

feedback and user generated content (e.g. customer product feedback extracted

verbatim from conversations with the contact center or from social networks),

locations (e.g. for store locators in retail), etc.

Key capabilities of Talend MDM

A solid data platform, although a prerequisite, is not the only parameter for success in

MDM of Product Data initiatives. This is why Talend also provides a large eco‐system, from

Open Source contributors of connectors; to specialists on address verification,

standardization and geocoding; to Consulting and Systems Integrators with a proven track

record of MDM success.

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