Organizations’ growing focus on digitalization has intensified the proliferation of new channels and touchpoints. On the other side, customers improved experience day in and day out. This has made effective management of key demand product data imperative. Now, organizations cannot ignore the importance of Product Information Management (PIM) platforms to ensure consistent, shareable, accurate, and unified product data.
Your PIM Strategy: More Than a Plug-N-Play Game
Even if you are taking all the measures for controlled scalability, a growing business has to account for dynamic system requirements, unforeseen operational workflows, increased staffing, and so on. In such a scenario, without a dedicated system in place, it is very difficult to avoid product data from getting scattered, flawed, duplicated, or siloed. Over time, information like item numbers, references, catalogs, SKUs, images, videos, translations, localizations, and attributes become increasingly difficult to manage. This leads to discrepancies, higher workloads, slower processes, and ultimately reduced conversion rates, especially in organizations with large, complex product catalogs that need to be updated frequently, or in real-time across multiple channels.
Consider a situation when you are introducing a new product, and the launch process itself has 30 steps. The workflow might involve as many as 15 employees, and the attributes of the new product can reach up to 100 across the value chain. With such an arrangement, delaying the launch can result in a significant fallback. You need fast implementation and an agile model to steward all the data into one unified platform. It is nearly futile to install any solution without structure, schema, and/or architecture to support it. After all, you cannot always halt the operations to structure the database into one relational schema that can be integrated with a product information management system.
Having a dedicated PIM system in place does mitigate all the aforementioned troubles in a viable and user-friendly manner. It enables you to maintain all your product catalogs and product information in an accurate, unified, and consolidated form. It provides the required flexibility for eCommerce when handling large volumes of products, frequently updated catalogs across multiple channels, markets, or geographies, including:
1: Facilitates unification of product data across the eCommerce ecosystem for B2C and B2B organizations
2: Assures semantic consistency of product data throughout the supply chain
3: Adheres to regulatory compliance (a boon for industries like FMCG, F&B, Pharma, etc.)
5: Improves the use/reuse of product data across entities like manufacturers, distributors, and retailers
6: Provides agility and accelerates time-to-market of new products
Lowers risks and reduces the cost
However, implementing a PIM system is just one half of the solution. Where organizations bank on PIM for effective and flexible support for their product master data, they cannot harbor its true potential without suitable data modeling.
Data Modeling Flexibility: A Requirement of the Digital Age
Data modeling provides a generalized, user-defined view of data. When you create a data model, you define the data, its attributes, its interrelationships, and its constraints. This helps comprehend how to address data needs and design the database most suitable for the organization. It also allows the user to easily handle multiple data scenarios, attributes, locations, and relationships. For example, you can create a data model for a product where the vendor attribute links to vendor data stored in a separate spreadsheet.
When this model is made flexible, it further enables data editing and translation of interfaces for the effective manipulation of product data. Such a model facilitates you to access, view, and manipulate complex business requirements without having to reconfigure the system for performance optimization.
Flexible data modeling in a PIM software makes it easier to cover all the structured and unstructured data (or metadata) and, if needed, modify them later on. It primarily addresses the need for continually upgrading data sets as the business grows and requirements change. The whole setup allows you to add another data source or make changes to PIM interfacing. Consequently, the PIM integration, modification, and restructuring at any stage in the business become fast, non-iterative, and of course, flexible!
Benefits of Flexible Data Modeling
. Manage product data with uniformity— Reduce time-to-market and simplify all editorial processes. Edit data one time in one place and the changes reflect across the value chain.
. Classify standards— Handle text, media, static attributes, and relationships in a single view. Classify and structure product data based on industry-specific classification systems such as eCl@ss, ETIM, or GS1.
. Meet organizational structure— Ranging from small and centralized to global and distributed teams, every organization has a different structure. The usage and focus of product master data, ranging across use cases for design (information architecture), implementation (building the system), operations (running the business), and analytics (reporting on the business) demands agility and compatibility in data modeling capability. This can be easily done via a model that is flexible enough.
. Manage multi-lingual product data— Address the growing concern of product data in native/colloquial languages as the business grows beyond geographic boundaries. Translate and edit product data interfaces with flexible modeling to boost productivity.
. Resolve complexity— From simple to elaborate use cases – deploy PIM effectively in different scenarios with better levels of data governance, risk management, and control.
Though, a PIM system presents a trusted, uniform version of product master data that optimizes business value chains and drives the economic activity of product-centric organizations. However, a reliable PIM system inherently needs the support of a flexible data model that allows seamless updating and governance of master data. It must have a flexible workflow that can be easily fine-tuned with the business process while maintaining data governance. This means that the adaptive user interface (UI) must adjust to underlying data models automatically without any additional coding or implementation efforts.
To sum it up, diving head-first in the PIM implementation is not the answer. Businesses must carefully evaluate how they can amplify the benefits of PIM with flexible data modeling.
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