Just like a roadmap or an architect’s blueprint, a data model acts as a conceptual representation to facilitate more profound understandings of the storage and relationships of datasets in databases and/or data warehouses. This crucial task of linking data portfolios, characteristics, and interrelationships becomes even more mission-critical when the target database is product-related that needs to be fed into a Product Information Management (PIM) solution.
A perfectly executed Product Data Modeling can ensure consistency in product naming conventions, semantics, default values and security, and above all the quality of product master data. By defining data associations and constraints with Product Data Modeling, decision-makers can reduce many performance inefficiencies like logging redundancies, export delays, multiple formats, and errors that need manual fixtures. Furthermore, since the availability of reliable and consistent foundational data is a key ingredient for conceptualizing, designing, and deploying all solutions, data modeling can act as a ready-made mix for businesses to free up resources and focus on other critical matters. It can increase the consistency in documentation, improve cross-team communications, speed up organization-level data mapping, and also streamline data-dependent process.
Businesses need to consider industry standards of timely, accurate, and stable exchange of foundational product data across their networks to rightly approach product data modeling. After all, standardizing foundational data and actualization of robust product data modeling is not a one-step solution.
To perfectly realize product data modeling and reap the immense benefits of PIM, businesses need to take cognizance of the following best practices:
- Prepare organization-specific use cases for product data: Product data models are living documents that evolve along with changing business needs. The modeling process should allow organizations to leverage data specific to operational needs and objectives. For instance, an eCommerce brand would focus on end-customer buying patterns to acquire product performance data to guide their campaign strategies. On the other hand, an aircraft engine OEM would focus on research and development data to launch industry-compliant products and stay market relevant. Knowing what data will (or can) be used for what purposes solves half the future problems of formatting, visualizing, and analyzing it.
- Analyze the current condition: A data model certifies actionable downstream results, data management best practices, and optimized utilization of PIM capabilities. A preliminary step for that is to simply evaluate and understand current data conditions. Vetting initial data conditions against organizational goals is extremely crucial because both product data modeling and PIM apply to the entire value chains and can drastically impact overall productivity. Data modeling should allow condition analysis and correlation of product data with external information for improved customer experiences. Only when you know the structures, standards, groupings, etc. of your repository can you go ahead with key data asset assessments and processing.
- Identify the primary and secondary data sources: Another advisable step for product data modeling is gathering and standardizing data from primary and secondary sources, irrespective of format. It begins with identifying cohesive and logically discrete things, events, or concepts that are represented in each data set. Upon merging incoming data into the model, opportunities open up for creating new feeds using the information received. The idea is to allow the unification of disorganized and siloed data within a standardized predefined structure. These business rules can then be translated into data structures to formulate a concrete database design.
- Create a high-level entity-relationship diagram: An entity-relationship diagram is a mandate for any type of data modeling. As the business grows, the number of entities (customers, geographies, products, versions, features, etc.) also increases proportionally, if not exceedingly. A high-level entity-relationship diagram can go a long way in such situations. Its schemas, symbols, and attribute connectors can help understand dependencies and process flows between two or more datasets. The data model usually specifies the nature of these relationships via unified modeling language (UML). This can provide a standard, consistent, and predictable way of defining and managing data resources across an organization or even beyond.
- Compare and analyze whether you need construct the data model from scratch: Once the purpose of different product datasets is defined, the entity relationships are visualized, and the present data conditions are analyzed, one gets a clearer picture of data needs. If that picture suffices the organizational goals, the existing product data model is good to go for PIM implementations. An improved, refined, or restructured data model is only needed if a business feels the need to represent and better understand the nature of data flow within an enterprise information system. A good point to remember here is that Data Models un-complicate data into useful information for decision-making and strategy.
- Be watchful of data latency and integrity issues while modeling the product data: Techniques of product data modeling should ensure the coherence and consistency of data while allowing updates to take place against the unique copy of master files. For instance, for marketing teams to derive customer insights, they need to have access to the latest data, where universal changes are reflected automatically. This is only feasible when product data modeling is watchful of data latency and integrity issues. This can be brought about by assigning keys (numerical identifiers) and normalizing the model to reduce redundancy with performance requirements. The normalization technique can also reduce the storage space, and the model can be used to visualize the information pipelines.
- Develop a clearer governance framework for data stewardship and quality controls: Rules and requirements should be defined upfront and incorporated into the product data model to avoid hidden discrepancies and inaccuracies in underlying data. Given the criticality of product data on customer experience, product development, and sales strategies, a governance framework for data stewardship and quality controls is necessary. Guidelines and benchmarks of data integration and processing can help maintain updated, qualitatively high, relevant, and accurate data for PIM. As a result, data governance, quality, and security standards can ensure the highest outcomes from product data and PIM.
- Adopt a collaborative approach to product data modeling: Data modeling makes it easier for developers, data architects, business analysts, and other stakeholders to view and understand. Given its scope of applications, each stakeholder needs to define the conventions and semantics of the modeling process. They also need to be well-versed with the schematics to quickly grasp the information, collaborate with each other seamlessly, and creatively add value through it. Be it the SKUs for a distributor or the customer ID for the sales team; the data sets need to have a single trusted view of data for operational efficiency and business development.
Model Product Data to Perfection
Product Data Models can help enterprises reap excellent rewards by aiding data-driven decisions and strategies. Data standardization initiatives benefit businesses of all shapes, sizes, and industry orientation. With the suitable model in place, Product information management (PIM) solutions can prove to be a game-changer in simplifying data handling, visualization, distribution, and analytics.
