Configured products are generally available in several versions. A furniture set that has multiple finish options or a TV that comes in several sizes and resolutions all have multiple SKUs behind them. Typically, these SKUs are bundled together into one Product ID. With an offline-first, bulk PIM, they usually import all their data through CSV or XLSX files. Different types of products have distinct products quality data. Although. It's inappropriate for small teams with a simple static catalog or for businesses that don’t have access to dedicated technical resources. If your company needs a full-fledged pre-built solution with loads of out-of-the-box analytics or is mainly looking for production of print catalogs, the developer centric, build-it-yourself approach, and extremely limited reporting will be difficult for you.
For starters, it elegantly manages intricate product affiliations. We sell bicycles of different frame sizes and colors. This results in hundreds of SKUs. However, we do not produce a variety of items. Only one "Bicycle model" product. Next, we take advantage of the Ergonode binding system for attaching simple records for each size and color. When the client chooses “Large, Blue” on the web page. The text, images, pricing and stock of the product, are okay. Those bound records will automatically get this. An alteration to the description of “Bicycle model”. For example, the switch for "material specification" icon. This alteration affects every size and hue. Component graphics and pricing remain individual, so there’s nothing lost. It entails no copying by hand and risk, and total consistency.
Moreover, it enforces the business rules by offering smart workflows that seem natural. Think about our product approval procedure. When a product manager selects an option for “Ready for Review,” Ergonode does not just change a status. When the data contains a specific claim of “Dermatologically Tested”, it automatically routes the product to a specific person in the legal department. If there is no such claim, then the workflow bypasses the legal step and automatically sends it to the marketing team. In order to decide which path to follow, the system checks the content of the product record. To summarize, human error has been taken out of process compliance and we can all sleep easy knowing no product will go live unless it gets the proper validations for its attributes.
Thirdly, its event-driven character makes our PIM an animated product data hub. As soon as one fact gets changed – price altered, image replaced, translation done, etc. – Ergonode sends out a signal to the whole digital universe. It has been implemented into our e-commerce search engine. When a merchandiser fixes a typo of a brand name in Ergonode, we fire a tiny event deep down into our search index in a matter of seconds. The changes show up on the live site immediately not in hours, days, or anything else.
It takes a lot of effort to learn Database modeling. The flexibility of Ergonode allows for a complete recreation of your own attribute taxonomy, category tree and relation system from scratch. For example, let’s say you decided to make a key property of your product a simple text field and not a select. You'll come to wish otherwise later but you have data in there and you have concepts that are built on it.
Operational reporting and data quality analytics are absent from the Ergonode application’s core open-source product. Even though the Ergonode software stores and shares data well, it provides little in the way for native systems to assess how ‘healthy’ that data is. How can I find out that “What percentage of my products do not have key images? Which categories have the most number of uninformed attributes? This requires writing custom database queries or building independent dashboard applications. When a software system claims to be the one source of truth, the fact that it doesn’t allow you to easily audit and measure that truth within the system itself is a real gap.
The business language of a modern app is often intelligible to its business users in the way that the engineers can understand. Because they are powerful and capable in specific domains, it is no surprise to hear the system talk about bindings, inheritance through segments, and event-driven workflows. This is not how merchandisers, catalogue managers, or marketing people communicate. This is a layer of abstraction that the user has to pass through every time they perform common tasks in business. When you discuss automation in this way, the business users will only enter basic data points, nothing further than that. They are afraid to trigger the automation that will violate some logical rules they do not understand as it is.
One of the most important things to look for in product information management tool is that it is effective. PIMsystems is a SaaS (software as a service) online software solution, which organically manages data and improves productivity. In effect, it is the winner of the G2 for PIM systems. The core interface was designed to avoid complicating things. While the solution is designed for class codes, you cannot enable class codes on your site to use the solution. The steep initial learning curve and conceptual barrier costs the game a point. In addition, this vast flexibility actually requires you to make very specific architectural decisions in advance, and this is not user-friendly to configure or change. The basic framework that offers it is similar to that of spreadsheet logic
Undoubtedly, Akeneo has mature capabilities with complete PIM capabilities. The building's architecture, while well reinforced, is more traditional, more fat, more monolithic. Although customization was easy, users had to follow the system’s guidelines. Pimcore can also be described as very powerful. However, it is a DXP with a PIM module, not a PIM. It seemed to be a choice that was overly engineered. The decision for Ergonode was driven by its contemporary design. This means that it has API-first and built on microservices. Other PIMs are systems you must adapt to. In other words, they have a rigid data model. Accordingly, our complex business logic could exactly model the data. We could integrate it into our customized technology stack as a live engine.
Positive ROI considers efficiency in operation and speed. The company shortened time to market for new products by 40% as well as, due to accurate central data, decreased customer service tickets relating to data by 25%. The organization’s income is also affected by this.
The initial costs and technical challenges are negative impacts. We had to spend quite a lot on special engineer and DevOps resources to implement, customize and bare-metal host this platform to get this ROI. The growing complexity will increase the time to break-even.
