PIM vs. MDM – 10 Key Pointers to Help You Decide

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MDM covers more data than just products, but PIM creates a dedicated repository specifically for product information. While PIM focuses only on products, MDM also includes customer, vendor, and other business data. ERP systems are not designed for data governance, workflow, or providing real-time data in the way that PIM and MDM are. MDM and PIM require an ongoing business philosophy and must adapt to new processes and requirements over time, not just be a one-time implementation. Single domain MDMs focus on only one type of data like products or customers, whereas multi-domain MDMs can handle multiple different data types.

Technology

The 5
most popular
misconceptions
PIM MDMVS

1
MDM covers
product data,
so no need for PIM.
Yes, MDM covers product data,
but not like the way PIM does,
by making a golden repository
exclusively for product data.

1 2
If You have a PIM,
You don‘t need
a MDM.
MDM has a different role to play
than PIM. While PIM is solely for
product information, MDM’s scope
covers customers, vendors,
distributors, partners, products,
suppliers, locations as well as
employees’ data.

2 3
If You have ERP,
why need a
PIM or MDM?
ERPs are not designed for
enterprise master data governance,
data workﬂow, data enrichment
or providing updated data at all
times. They cannot store digital
assets too.

3 4
MDM Implementation
is a one-time thing.
MDM and even PIM must be
understood as a business philosophy.
It thrives on accommodating new
processes, business requirements,
mergers and acquisitions.

4 5
Every MDM is
multi-domain,
multi-vector.
Single domain MDM platforms
emphasize on product or customer
data and are programmed to collect
data for one domain only.
They have predeﬁned data model
templates and other domain-speciﬁc
aspects.

5The Revolution in
Data and Experience
Management.

Both CRM and ERP have one goal, to automate businesses and help companies all around the world become faster and more efficient and foster their growth. While both Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) are important for a business, let us take a look at some of the key differences between both. Read the full blog here: http://suyati.com/crm-and-erp-know-the-difference/ Reach us at: jghosh@suyati.com

Why marketers need a Product Information Management (PIM) Solution

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David Sultan, Director of Sales Engineering for PIM software company inRiver, discussed product information management (PIM) solutions in detail. During this webinar, David provided a comprehensive explanation of what a PIM does, how they work, and who would benefit from using one. David will also give specific methods that a PIM can be used to generate revenue, save money through improved processes, speed up marketing initiatives, and improve customer satisfaction. What you will learn: - The definition of a PIM solution - How a PIM integrates with marketing systems and functions - Who would benefit from implementing a PIM - How a PIM addresses operational inefficiencies, disparate product information, and inconsistent product messaging - How a PIM can generate increased revenue – improved conversions, higher revenue per order, etc. - Ways that a PIM can increase customer satisfaction and help retain customers in the long run - Real-world case studies showing the effect that a PIM implementation has had on the business

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More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise\’s core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data.

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Data Ownership: Most companies and organizations have this notion that data governance should be taken care of , by the Information Technology department, because IT owns the system which stores the data. The owner of the data is responsible for providing attributes to the data and answerable to any questions regarding data. The people answerable to these kinds of data are generally the ones involved in defining business rules, data cleaning and consolidation.? Data Stewardship:? Data stewards should be favorably those people who are familiar with the data. It is often seen that there is need to deploy several people, to handle and correct data, whereas a single data steward could have done the same job. Since the data being handled involves organizational level data, it is important that there are governance rules for this process.? If there is some certain rule in the data which causes large data volumes to fail, this rule should be fixed while data cleansing. So it is important to take care of the amount of clean data sent to the stewards, since we are not aware of which rules might trigger what amount of data.? Choice of data stewards is again a difficult selection. Data Security:? Although the master data is data on organization level, but there is some confidentiality level linked to it.? Not every employee has the authorization to view its aspects. Security rules can be applied to the data. The various departments in the organization must set different rules to the data they own. They need to grant permissions to these rules , so that the user can view the data. A large company can have data sourced out of many regions. It is to be ensured that they are responsible to correct only their own data.? Data survivorship: There are some guidelines which are set up by data governance. These rules can often change over hthe time according to new data sources being added. The changes made to the data , are communicated to the organization so that data stewards and users can understand the process. So from a data steward's point of view, it is important to apply security rules to the people who are involved in data handling and correction. This is a result of how data governance and data security can be applied while implementing MDM.? ?

Reference master data management

Dr. Hamdan Al-Sabri

Reference matter data management: Two categories of structured data : Master data: is data associated with core business entities such as customer, product, asset, etc. Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail. Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .

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Acolyance (€522M+revenues) is a leading French agriculture and wine cooperative serving a network of 3,500 agriculture members and 7,000 wine producers. Management is very focused on innovation and is preparing a large-scale transformation for 2014 wherein ERP will be deployed to support most of business processes (finance, accounting, harvest, retailing, procurement, sales, …). Unlike traditional viewpoints with ERP systems as the solution to manage all master data within their perimeter because they were intended to be *the* company core system, Acolyance has decided to make its master data program a prerequisite of its ERP implementation. By having an MDM approach synchronized with the ERP strategy, Acolyance is convinced that ERP will be able to concentrate on its core business processes and to deliver quicker and better. Additionally, MDM enlarges the ERP scope by facilitating collaboration with trading partners. In this session, topics to be discussed include: - Applying MDM as a key approach to secure ERP implementation projects - Leveraging MDM to fill in functional weaknesses of ERP systems - Using MDM to facilitate the update cycle of master data that cannot be updated directly in production systems without ERP customization

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