In today’s complex business world, data is the life elixir of every and any company. Data, however, does not equal data. A particular role is occupied by so-called master data. Such data forms the foundation for smooth processes and informed decisions. What’s behind the concept of master data management (MDM) and why is the well-defined master data administration so decisive for your success?
Master data encompasses the most important and foundational information of your business objects. This includes, for example, customer data, material master data, supplier information, and, of course, product data. These kinds of data usually have a long lifetime and are utilized by various systems and corporate departments. In short: Master data forms the backbone of numerous business processes – from offer creation, through logistics, to customer support. Inefficient management of this data is quick to lead to data inconsistencies and redundancies. The consequences range from wrong reports to slow-moving workflows. This costs a lot of time and money. There is no way around it except for efficient master data management.
Well-implemented master data management creates a central, consistent, and reliable data foundation. This enables you to make strategic decisions, optimize your processes, and improve collaboration between various departments. That is to say: Master data management guarantees order in foundational data processes.
An effective master data management ist much more than only a technical exercise – it directly influences the success of your company. Positive effects can be observed in almost all areas:
In short: Clean master data pays dividends – today and in the future.
A functioning master data strategy is not created overnight – it requires structure, clear objectives, and a system that matches your company. The key lies in small and pragmatic steps. “Quick wins” is the magic formula: Instead of realizing the whole in its entirety in one go, it is recommended to begin with concrete use cases – with product data in e-commerce, for example, or with supplier data in sales.
The important thing not to forget is: This is not just a pure IT project. Collaboration between IT, management, and experts in relevant fields is required. Define clear roles and responsibilities, establish a slim governance structure, and, finally, assure that the data quality is maintained at regular intervals. And: Choose a platform that does not only convince in terms of technology but can also be flexibly adjusted to your requirements – ideally, cloud-native, API-first, and scalable, like ATAMYA. This way, you stay in control, minimize risks, and are quick to see first benefits – and this is precisely what defines a good MDM strategy.
Once the strategic foundation is set up, it comes down to the actual implementation. Especially in the field of product management, the well-thought-out master data management plays a key role. Because it is here that daily data maintenance, marketing requirements, and sales successes come to meet. Clean, consistent product data makes the difference: For successful omni-channel sales, effective marketing campaigns, and satisfied customers.
Best practices for master data management of product data include:
With modern master data management, you create the foundation for a strong, future-proof product information management – independent of channel, language, or target group.
Author:
Robin Demeter
Senior Sales Manager
ATAMYA
Whitepaper Tip: This Is How You Use the Potential of Your Product Data
Do you want to dive deeper into the subject matter of digital product management and learn how to use your product information optimally? Then take a peek at our free whitepaper “Digital Product Management” packed with lots of tips, practical impulses, and strategies for your everyday work life.
Sooner or later, PIM users encounter a frequently underestimated topic: data onboarding. This term refers to importing external data into one’s own systems – for example, into an ATAMYA instance. Depending on the context, it’s also called “supplier data onboarding”.
Merchants and wholesalers typically receive product data from various sources such as manufacturers, suppliers, or data pools. Each data supplier manages their data differently — some use Excel spreadsheets, others use a PIM. In addition, there are many data exchange formats like CSV, XLSX, XML, JSON, and data standards such as BMEcat or ETIM xChange.
What works for the individual supplier often poses challenges for the data recipient. So, how can different datasets be consistently and accurately imported into ATAMYA serving as the central platform for managing structured product information? The answer lies in a clearly structured, efficient data onboarding process supported by middleware acting as a “translator” between data supplier and recipient.
This article explores why data onboarding is more just than a technical necessity, and how to optimize it within your product data management.
Admittedly, data onboarding isn’t exactly a hot topic for small talk. But why is this part of product data management often treated as secondary? Data onboarding is often dismissed as a minor preliminary step before PIM maintenance. However, considering the entire product data journey — from creation to presentation and marketing — data onboarding actually plays a crucial role throughout the content supply chain, from manufacturer to end user.
However, when you consider the entire journey that product data takes from its initial creation to the final product presentation and marketing, this assessment is put into perspective. It quickly becomes clear that data onboarding actually plays a key role along the entire “content supply chain”. This refers to the complete flow of data from the manufacturer to the retailer and finally to the customer or end user.
One reason is the quality assurance function of data onboarding: it ensures that low-quality external data can’t even be imported into internal systems and cause further issues. The reality is: product data management processes in a PIM are only as effective as the quality of the data they rely on.
The takeaway: Those who import high-quality, standardized, and clean data into their PIM system lay the foundation for automated workflows, omnichannel syndication, and sustainable data quality – within the PIM and beyond. Product visibility in search engines, online shops, and marketplaces depends heavily on the freshness, completeness, clarity, and overall presentation quality of products. Essentially, high quality product data helps reduce return rates and increase sales potential.
The need for high-quality product data in the context of data onboarding is closely tied to the very nature of PIM systems. Systems like ATAYMA serve as what is known as a “Single Point of Truth” (SPOT) – a central and consistent source of product information. A cross-departmental use of the PIM also helps prevent the formation of data silos – isolated, disconnected datasets that may contain inconsistent information about the same products. In short: where data silos exist, confusion follows. And PIM systems are designed to prevent exactly that.
Everyone accesses the same centralized data pool, ensuring a uniform and reliable data foundation across the company. Product manufacturers can create data for new items directly in their own manufacturer PIM system. Wholesalers then receive this data and import it into their own PIM environment. Once stored in the system, the data can be continuously updated, enriched, and published. Sales and marketing teams, for example, use PIM data to create compelling product descriptions and promotional campaigns. However, this requires active data management: all product data must be kept up to date, complete, and informative at all times.
The key question is: How can different external product data be consistently and efficiently imported into ATAMYA? To achieve this, companies often rely on intermediary software solutions – so-called “middleware”.
These specialized supplier management tools (e.g. in the form of a supplier portal) enable distributors to define clear specifications for how they accept product data deliveries. Each supplier can align their data with the required data structure (e.g. the field structure in ATAMYA). This can be achieved using a data mapping process that matches the supplier’s source fields to the defined target format.
Versatile data onboarding software also allows distributors to configure how supplier roles are managed – whether suppliers are permitted to manage their data themselves, whether the distributor takes over this task on behalf of the supplier, or whether a hybrid role is preferred.
Before supplier data is transferred into the ATAMYA system via interface integration, middleware users can set up and manage various data management processes, including the
A product data classification in the PIM — whether custom or standards like ETIM or ECLASS — can also be generated and automatically exported this way.
Those who regularly work with (external) product data are likely familiar with common obstacles: Excel files with freely named columns, incomplete mandatory fields, format inconsistencies, or media links leading nowhere. In some cases, data is not updated frequently enough – or the updates are not passed on to data recipients in a timely manner.
These practical examples highlight a typical situation: in addition to clearly defined rules for data enrichment, many companies simply lack the technical infrastructure needed for smooth product data management. Especially when handling large volumes of product data, interface integrations – for example via REST API – with middleware solutions, as well as external and internal systems, are indispensable.
In many data onboarding workflows, another issue becomes apparent: the lack of a clearly defined set of requirements. To address this, PIM users (being the data recipients) can reflect on questions such as:
Experience shows that data management processes tend to function reliably when they are both technically well-designed and clearly communicated.
Once these data structures are clearly defined, the foundation is in place for an automated data onboarding process. However, such a process relies not only on the right middleware but also on clear communication. Only if both sides understand what matters in terms of data delivery, data transformation, and data distribution can potential issues be avoided from the outset.
However, an automated data onboarding process depends not only on the right middleware, but also on clear communication. Only when both sides – the data supplier and the data recipient – know what is important when it comes to data delivery, data transformation, and data export can potential problems be avoided in advance.
An efficient data onboarding workflow typically begins with the appointment of clearly defined contact persons who are responsible for managing and supervising the process. The technical implementation is then based on the definition of user roles and access rights, the connection of data sources (such as a PIM, ERP, or individual files), the integration into the ATAMYA PIM as the target system, the creation of data mapping structures, and the setup of customizable or standardized data validation rules.
Experience from numerous data onboarding projects shows: yes, setting up such a workflow involves an initial effort. But this effort pays off quickly – through higher data quality, fewer follow-up questions, and a shorter time-to-market.
This article makes one thing clear: professional data onboarding is not magic – it’s the result of a well-aligned interplay between technology, processes, and people. In practice, the following five aspects have proven particularly valuable:
When all these elements come together, data onboarding becomes what it truly is: a foundational building block for commercial success in today’s digital business landscape.
Author:
Kevin Mattig
Sales and Business Development at nexoma
Error-free Product Data is No Coincidence
Our checklist demonstrates how you can systematically improve your product data quality in a sustainable manner.
In a world where up-to-date, complete, and consistent product information decides about success or failure when it comes to convincing customers and staying afloat in the face of ever-growing competition, a Product Information Management (PIM) system occupies the main role: It centralizes and structures product data so that it can be distributed to all sorts of channels. Not every PIM system, however, delivers the same performance – in particular when it comes to structuring the data. A real gamechanger in the world of product data is the so-called multi-domain data model. Now, what does this concept mean and what differentiates it and sets it apart from conventional, static data models?
Simple, static data models used to be the go-to standard for companies utilizing a PIM system in the past. More often then not, they are based on strictly defined attributes such as product names, descriptions, prices, or sizes. This usually one-dimensional concept is made to fit a specific set of data. This, in turn, means that every product has the same attributes and everything follows the same rigid definitions.
This works smoothly, as long as you have a clear overview of your product data and the requirements for your data structure are moderate. Wherever a company works with simple product information, such a model is sufficient. As soon as the products are more complex or serve different categories and use cases, however, static models are quick to hit a wall. The requirements concerning flexibility and adjustability grow – and here is where multi-domain comes into play.
A multi-domain data model follows a dynamic method which can be adjusted to a diverse range of data types and requirements. Instead of focusing on a single, set-in-stone structure, it allows for a flexible organization of product information across multiple “domains”.
A domain within a multi-domain model can, for example, be centered around specific product attributes such as technical specifications while others focus on information for customer analysis or supply chains. Each domain can be organized separately and adjusted on demand. This way, companies can manage different kinds of information side-by-side and put it to use in a strategic manner depending on target group or channel. Therefore, a multi-domain data model enables the parallel management of different worlds of data using an all-in-one approach.
The multi-domain data model sets new standards in product data management and offers clear benefits compared to static models:
The multi-domain data model provides enormous benefits for marketing since it is heavily reliant on up-to-date and precise product information. It allows you to structure product data in a more targeted manner and process it for different channels and target groups, in turn boosting customer success. Where static models are quick to hit the wall when it comes to both flexibility and data diversity, multi-domain models offer a dynamic and sustainable solution for modern product data management.
Companies who trust in multi-domain data models for PIM systems can meet the ever-increasing requirements of product data and are quick to respond to changes. This does not only render product information complete and consistent but – before anything else – flexible.
Author:
Marko Stuka
Senior Sales Manager
ATAMYA
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Have you grown curious about how to tap into the full potential of your product data? We show you how the multi-domain data model manages your data efficiently. Set an appointment with us now and get off to a flying start!
On the 13th of December 2024, the new EU regulation 2023/988 for the General Product Safety Regulation (GPSR) comes into effect. It replaces the former directive 2001/95/EG. This new regulation comes with higher requirements for manufacturers and retailers. This includes, among other things, a strict obligation to inform concerning products as well as a comprehensive risks evaluation as a guideline for manufacturers.¹
Manufacture, Retail, and Import
The legal determinations of the GPSR concerns manufacturers, retailers, and importing companies. In particular, it comes with the definition of new compulsory information in online retail, hereby requiring far-reaching adjustments.
Consumer Products
The GPSR applies to products for the end consumer. Goods not marketed for the consumer and not utilized by consumers do not fall under this category.
B2B and B2C
The GPSR does not differentiate between B2B and B2C businesses. Accordingly, it is relevant to both areas. Products that can be directly or indirectly purchased by end consumers such as advertising materials offered in the B2B field do also fall und this category.
Excluded from the Regulation are:
Products that are offered on EU markets prior to the deadline are also excluded. Accordingly, products currently available for purchase can still be marketed without the requirement to comply with the new obligatory information defined by the new regulation. These products, however, do still fall under the existing product safety law and must comply with corresponding requirements.
Therefore, all new products that have been marketed since the 13th of December 2024 fall under the new regulation – which are consequently required to comply with the demands of the GDPS.
The new legal determinations of the General Product Safety Regulation (GPSR) have the aim of guaranteeing the consumer’s safety and that all relevant information is easily accessible. In order to satisfy the higher security and transparency requirements for consumers, companies are required to provide the following information in a clear and distinct manner:
Manufacturers must specify the names, the registered trade name, or brand. Additionally, both the post address and electronic address (e.g., email address or links to contact forms) are required. Companies not headquartered in the EU are, additionally, required to specify the name, address, as well as electronic contact information of the EU-based person in response.
Warning and security-related information must be formulated in clear language pursuant to the regulation and applicable EU guidelines. Such notices must be printed either directly onto the product, its packaging, or accompanying documents.
For product identifiers, information must be provided that allows for the clear identification of an individual product, together with an illustration of the product that constitutes a central element of the new regulation.
In most cases, a simple image of the product will do the trick. Where the creation of an image would require disproportionally high effort, it is sufficient to provide an illustration or a pictogram, as long as it assures the clear identification of the product.
The information about manufacturer, importer, as well as warning and safety notifications must be visible clearly and distinctly in the offer. A mere link is insufficient. The products must be uniquely identifiable as established in the form of a product illustration and other identifiers. The information must be easy to find and must, for example, be set apart from running texts. Simple notifications as part of the legal information or an FAQ page do not meet this requirement. A special, highlighted tab in the online offer, for example, may be a meaningful option to present such information.
PIM (Product Information Management) is software for the purpose of centralizing, maintaining, and managing product information.
With the help of PIM, you can maintain the manufacturer information and, in the case of imported goods, even the contact information for the EU-based person in response – and link it to all products offered by this manufacturer. Warning and safety notices, for example, can be directly added to the product group or products as an attribute, depending on how specific they are. Even product identifiers and illustrations can easily be maintained in a PIM as a product attribute, be it GTIN, product type, image, or other kinds of illustrations.
All this information managed in the PIM can then by distributed to various channels depending on your requirements: as product labels, accompanying documentation, or as information in your online shop. A PIM facilitates the realization of legal requirements since you can simply store and maintain such information in your PIM system as the central datahub – from where you can consequently export or distribute it as necessary.
¹Note: This article offers basic information about the new General Product Safety Regulation and describes possible implementations of its Obligation to Inform using a PIM system. This text makes no claim to completeness and legal security. Nor does it replace individualized legal consulting. In case of legal questions, please consult your legal agency or advocate of choice. For the creation of this article, sources consulted include the following German institutions specializing in related matters: IHK Regensburg and Trusted Shops.
Author:
Sebastian Faber
Senior Digital Performance & Marketing Operations Manager
ATAMYA
Interested in an Exchange?
Our expert team is happy to receive your requests and explain in detail how you can manage your product data with ATAMYA Product Cloud, search and find all your information with it, and where it can be put to use – for example, the new obligation to inform of the GPSR.
The ever-increasing diversity of product variants poses a two-pronged challenge to companies. On the one hand, companies can fulfill the customer’s wish with customized products, while, on the other hand, the resulting diversity of products comes with difficulties for production. A continuously growing variety of variants is becoming more and more of a competitive advantage because it allows you to satisfy the customer’s demand for individualized products. However, this also increases complexity and costs in production in turn. Batch-sized production doesn’t allow for reducing costs because you suffer from so-called scale effects as a consequence. In order to get a firm grip on the complexity of variant management and all processes involved, companies should make use of two things: a methodological approach when selecting the variants you want to produce as well as suitable technologies that can support you in doing so.
In order to define what variant management means we should, before anything else, clarify what a variant even is.
Variants (as in product variants) are specific articles which differentiate your general product on the basis of defined properties. Let’s illustrate this with the help of an example:
Variant management, accordingly, is all about the efficient selection, creation, maintenance, and handling of variants. It is integral to the fields of production, marketing, and sales.
Concerning effective variant management, the first question is: Which products do even need variants to begin with? The answer may depend on the demand voiced by you customers and the actual implementation. For example, is it economically profitable to respond to individual customer wishes? Once these questions are out of the way, you can start with putting theory into practice and realize processes for variant management.
For the efficient management of product variants, consequently, it’s decisive to determine how complex the products which need to be managed are. To this end, you can divide variants into three different groups:
First Group (e.g., t-shirts): There are only a few variants for the product and they are made up of only a few properties without a particularly complex set of rules. In the case of t-shirts, for example, this simple rule set may define that there are three sizes, each of which can be combined with one of four colors.
Variant Management: The variants can be easily created and maintained in a PIM system. Since a simple rule set is in place which defines the exact number of variants, it would be comfortable if you would only have to define the rule set itself in the system rather than set up every single variant individually.
Second Group: While there may only be few properties out of which the variants are formed, each comes with a large range of values — resulting in a corresponding number of variants per product (circa > 100). Examples for this second group are customized products such as, say, planks from the building supplies store, given that they’re manufactured to match every required length and width. If an oak wood plank with a length ranging between one to five meters can be cut to shape centimeter by centimeter, then you’re looking at a total of 401 variants. Consequently, if the plank has a width ranging from one to two meters which can be potentially cut to shape at every ten centimeters, there would be a total of 4.411 variants.
Variant Management: In this case, we have a lot of variants. Here, it’s imperative not to create every single variant in the system but rather only the simple rule work which underlies all variants. This rule work can then be provided, for example, to your web shop. Customers then choose from the pre-defined properties and enter required values — in response to which they receive their relevant variant. The system dynamically generates an article with an article number and forwards the purchase to the Enterprise Resource Planning system. If the article numbers are continuous, the article can be clearly identified across all systems.
Third Group: There are variants for a product, but these variants are created on the basis of a complex set of rules. That is to say, contrary to the first group, you can’t simply form variants out of permutations (i.e., all possible combinations of defining properties). For example: blue pants might not be available in the size L, while red pants are available neither in the size S nor M, etc. Such rule sets can contain a lot of complexity, as you know it from the automotive industry. More often than not, a lot of product variants are required here.
Variant Management: The creation of variants of the third group is most efficiently realized in the form of a special product configurator. Such configurations can be self-developed or you can rely on a standard software. The configurator is based on a complex rule set. This requires a lot of effort and an in-depth understanding of the product. Usually, the configurator also comes with visual or 3D support. On the customer’s end, the configurator — which may be operated either by the customers themselves or trained sales personnel — is used to configure the variant. It is often times the case that the configurator directly creates an article number for the specific configuration which is later referenced by the ERP system.
Besides efficient and comfortable product and article data management, a PIM system can help you in making sure that the variant management for the first and second group discussed above won’t cost too much effort. In the case of both groups, as already mentioned, it’s usually not required to manage every single variant but only the set of rules for creating variants. So, whenever a third-party system connected to the PIM via an interface in real time such as, for example, a shop system or an ERP system requires the information of some variants of the first group, the interface simply applies this set of rules and transfers all data. To this end, it’s best practice to automatically generate a suitable and unique article number. Make sure that all selection fields which serve the purpose of individualizing the general product into the specific variant are clearly defined.
Following the previous example for the management revolving around such a rule set for the first group: When creating the product in your PIM system, you set up the selection fields for possible colors and possible sizes.
In the case of variants of the third group, you can at the very least transfer the text-based product data and images from your PIM to the product configurator used by the customer.
Author:
Matthias Gärtner
Project Manager
ATAMYA
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You’d be happy about receiving support in your variant management and you’re looking for suitable software for you company? Then how about making contact with us so that our experts can show you first-hand how you can utilize a PIM system to successfully manage your full range of variants.
The topic of Data Governance may seem complex at first glance. With two blogposts, we’ll bring light into the darkness. In this first article, we want to analyze the topics of essential aspects, challenges, both monitoring and measurement, as well as regulations and data protection. In the second article, “Data Governance in E-Commerce: Why PIM is the Key to More Well-Informed Decisions,” you’ll learn how a PIM can help you during the implementation of an efficient data governance.
Data governance encompasses the management, control, and security of all activity and processes within a company that revolve around data. Among other things, this includes
A solid data governance supports companies in successfully implementing their data strategy, guarantee data integrity and quality, and make data-driven business decisions. Central aspects of data governance are: data definitions, data standards, master data management, business sectors, and also data ownership.
In today’s corporate landscape, the management must handle large quantities of data while, at the same time, fulfilling not only data quality standards but also compliance requirements. Additionally, they are to gain valuable insights in order to stay streets ahead of the competition. Some resulting challenges are:
To guarantee the success of your data governance initiative, monitoring and measurement are indispensable. Under this heading, you’ll learn how to monitor the performance of your data governance and what kinds of metrics, indicators, and methods for continuous improvement you can apply.
The performance of a data governance initiative can be empirically measured by a variety of metrics and factors. It’s imperative to select all those that best correspond to your company’s objectives and the requirements of the involved management. Essential metrics include:
A useful tool supporting you in your challenges for metrics and indicators is, for example, Collibra, a software platform developed specifically for data governance use cases. It helps you in efficiently managing the data processes and guidelines.
The principle of continuous improvement is crucial in data governance when it comes to progressively enhancing the quality of your data as well as adapting to new challenges at regular intervals. Some measures for continuously improving are:
Data governance also comes with various potential pitfalls you need to avoid in order to maintain the performance of your initiatives. Here are some examples:
By identifying and preventing possible pitfalls, you make sure that the implementation of your data governance initiative can be carried out effectively. As a result, you can exhaust the full potential of all the data in your company.
Being able to adhere to legal regulations and data protection requirements is a crucial aspect of any data governance strategy. To gain a better understanding of this topic, let’s have a look at the role a data protection officer plays and, consequently, the relevant regulations.
An essential aspect for realizing data governance is the inclusion of a data protection offer. Their expertise lies in making sure that everything conforms to both data-related regulations and guidelines of the company. They make sure that the data organization, master data management, and data standards adhere to data protection law requirements and the company’s internal policies for handling specific data.
The data protection officer’s role encompasses, among other things:
The data-driven economy poses a great many challenges to companies in the field of compliance. Which is why both data management and governance ought to be adjusted to the relevant national and international regulations and directives.
Well-implemented Data Governance takes different aspects into consideration:
An effective incorporation of a data protection officer in your data governance combined with the implementation of legal regulations and directives will result in a strong compliance on top of better decision making in business. This is because data records will be more accurate, consistent, and secure – while also offering higher availability. It enables companies to make data-driven decisions and minimize risks in an effective manner.
Here are the most frequently asked questions and corresponding answers.
Data governance is indispensable to guarantee data quality, data security, and compliance to legal regulations. Companies can set up effective decision-making processes and protect their valuable data foundation. Practical tips include defining guidelines, assigning clear roles, and conducting regular control check-ups.
A data governance manager develops and implements strategies in order to secure the data quality, access, and protection in a company. They coordinate teams for data guidelines, establish compliance, and control data protection measures. Their work optimizes data-founded decision making and reduces risks.
A data governance framework is a system for managing and controlling corporate data. It defines strategies, processes, and technologies which can help maintain data quality, integrity, and access. Equipped with this, you can make well-informed decisions and apply risk management measures.
Introducing a data governance strategy across an entire company for data use is a crucial project. We’ve looked at two universally applicable key factors on how to realize this. Finally, there is a third topic to be added specifically to account for product data:
What this third point, “PIM systems in connection with data governance,” is all about, is what you can learn in our second of two articles: “Data Governance in E-Commerce: Why PIM is the Key to More Well-Informed Decisions.”
Author:
Steffen Grigori
Chief of Service at acquisa
In today’s business world, the efficient management and classification of your product data plays a decisive role. The right classification does not only facilitate your search for products and product information but also optimizes the data exchange with companies, customers, and partners.
This article analyzes the synergy between ECLASS standard (formerly “eCl@ss”) and PIM — and how it can increase the efficiency of your product management, reduce the error rate, and push your competitiveness. The smooth interplay of PIM system and classification standards supports your company in the digital age and guarantees that your product information is not only all-inclusive but also well-harmonized – aspects that are important if you want to stay successful in this data-driven world.
ECLASS is a cross-industry classification standard that enables you to consistently identify products and services on an international scale. The same-named non-profit organization was founded on November 14th, 2000, by twelve of the largest companies in German economy. By 2020, the association had already gathered well over 150 members worldwide. As a state-of-the-art ISO/IEC-compliant data standard utilized on a global scale, ECLASS is commonly referred to as the universal language of Industry 4.0 nowadays.
The standard is a hierarchical system and encompasses around 45,000 product classes and 19,000 unique features – allowing for a fine-grained and precise classification as well as a unique description of products and services. The result is standardized master data that can function as the enabling condition for an efficient Master Data Management. And it assures data integrity besides facilitating collaboration with global customers and suppliers. You can find an overview of the current ECLASS version on the official website of ECLASS e.V.
When it comes to formatting your products in accordance with the ECLASS standard, a so-called PIM system (Product Information Management system) can be of great help. Now, how exactly may a PIM system support you in putting your product classification into practice?
These are only some examples as to why a PIM system can support you in exhausting the full potential of a cross-industry standard.
While the classification makes sure that you can always pick out and categorize the correct product, BMEcat handles the smooth data exchange between business partners. In the E-Procurement field, this makes for a significant increase in efficiency while also reducing the error rate. Electronic catalogs that use a data standard are usually interoperable and can exchange data with both other systems and other platforms using the same classification. For more information on electronic catalogs in the BMEcat format, please refer to our article BMEcat: Exchange Format for Your Product Data.
Besides ECLASS, there are many more standards which serve other markets or sectors. One such example is ETIM. Here, once again, the versatility of PIM systems are brought to bear. Such systems do not only support ECLASS but offer possibilities to fully integrate it together with several other standards. Equipped with this, you’ll always correctly classify product information in a range of contexts.
Author:
Corinna Schneider
Project Manager
ATAMYA
In case you’re looking for a solution to efficiently integrate ECLASS and other standards, our PIM system could be just the right answer for you. Contact us and learn more how we can optimize your Product Information Management together.
An unwieldy word that holds great meaning: product data feed – or product data flow – refers to a well-structured data record that contains all essential information about your product. That is to say, the product data feed bundles all product data in a centralized manner: This includes article identifiers, colors, descriptions, prices, availability, image URLs, and shipping information. Such a product data feed, however, is more than just a mere list of data: If used efficiently, it enables you to transfer data to all target sales channels in a quick and secure way without any errors – while, at the same time, also guaranteeing that this data pool is always up to date across all marketing and sales platforms. This is because the product data feed draws directly from your database and can, with the help of a management system, be utilized flexibly in all sorts of manners. How exactly does this work? That’s what we’ll discuss in a moment.
We can already take notice of the following at the very least: You can’t imagine today’s e-commerce and multichannel marketing anymore without a product data feed. There are, in fact, even more benefits which speak for creating and maintaining your own product data feed:
Product data feeds are usually stored as a .csv or .xml files on a server. Your sales partners are granted access with a URL. Be it .csv or .xml, both formats allow you to structure a large quantity of data, distribute it, and import it into other systems. When setting up your own data feed, pay special attention to the following aspects:
You can already see: Even if your product data feed is well set up, occasional updates are indispensable. Whenever you increase prices, make adjustments to the product availability, add new products to your assortment, or launch promotions, you’re required to update your data.
Also, a complete and well-maintained product data feed can only come to be when all data from all sorts of sources are unified in a clean, clear-cut manner without any redundancies. The to-be-managed data, however, rarely comes from a single source; the following domains may contribute to generating a complete set of all your product data:
Further data may come from:
How, then, can you bring together all this data in the best possible manner so as to form a single, well-manageable, and flexible data record? How can you make updates in a quick and easy fashion? How can you make sure to prevent duplicate data records so that you team can rely on complete and correct data records that convince with high data quality at any given time?
In a PIM system (Product Information Management system), you can manage all your product information efficiently and smoothly distribute it to all sales channels – be it online shops, B2B or B2C marketplaces, apps, comparison portals, or online catalogs. A PIM takes your product data feed and elevates it to the next level of efficiency and applicability.
Think of PIM as your well-assorted shop next door that you would recommend to your friends: All products are neatly organized in intuitive categories so that you always have the full overview; you can search and sort relevant data according to your individual use case or, in the customer’s case, the current purchase decision; and you can easily compare products and articles. All this is established by a PIM – with the difference that PIM is like a digital rather than a physical store. With a PIM, you organize all your product data and media assets in user-defined hierarchies – e.g., by brands, types, or specific attributes –, you create relations between products, and you create the optimal basis for your product presentation on the web.
The product data model created hereby facilitates the management of your product data – and renders your data all the more flexible. Use your PIM system to personalize your product information, for example to strategically address your target groups – allowing you to appeal to a diverse range of regional markets. How is this possible, you may ask. You can do so, for example, by also maintaining your complete data records in relevant translation languages together with corresponding digital assets. This way, even a multi-lingual customer experience can be achieved in a smooth manner – while everything stays consistent even across multiple languages.
By providing further technical data (such as measurements, compatibilities, energy efficiency, operation requirements, and further specifications) as well as images and other kinds of media (optionally with the connection to a Digital Asset Management system), you perfect your product data. Accordingly, you can now look forward to…
In order to make sure that your products can be searched, found, ranked, and accessed in the most optimal manner possible, you should keep the following in mind:
We wish you a good run with your new, well-organized visibility in digital sale!
Author:
Eric Dreyer
Head of Product Management and Quality
ATAMYA
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Do you want to learn more about the topic of Product Information Management and the benefits that the implementation of a PIM system provides to your company? Then make contact with us as early as today.
ETIM stands for “Electrotechnical Information Model,” a classification system that unifies product descriptions from commerce, the industries, and construction. It’s a universal standard which can present product data in a neutral manner and independent of specific manufacturers, media formats, and languages. Equipped with this classification, retailer and manufacturer speak a common language, hereby facilitating and accelerating communication even on an international level. Initially, with the founding of “ETIM Deutschland e. V.” in Germany in the year of 1999, the open standard has exclusively been used in the field of electrical engineering up until 2015. By now, the classification system has also been adopted by many other sectors such as the “Heating, Ventilation, and Air Conditioning” industry (HVAC) and “Tools, Hardware, and Site Supplies” industry (commonly abbreviated as WEBA in German, but there is no direct equivalent used by the English-speaking engineering communities). With the founding of “ETIM International” in 2008, the classification has started to receive a very high acceptance rate and recognition across all of Europe. In general, it’s a free standard so that you can utilize its data model without any license fees; you can also opt for a paid ETIM membership if you want to actively participate in the continuous optimization and refinement of the model.
For the classification with ETIM, your products are assigned to corresponding article classes in a flat hierarchy. In order to achieve an exact mapping of all individual products, the differentiation of classes is very meticulous, resulting in a rich number of classes. Finally, all these classes are summarized into thematic groups, but these groups only serve the purpose of providing a more convenient overview and better internal organization. All in all, the current version 9.0 of ETIM consists of a total of 5,554 classes for the following 5 sectors: “electrotechnical” (E), “HVAC and plumbing” (W), “building materials” (B), “shipbuilding” (M), and “Tools, Hardware, and Site Supplies” (T).
Every class contains a well-defined set of technical attributes. You can use these attributes to describe all products assignable to this class. In order to also guarantee that the inserted product information is uniform and well-structured, the attributes come in the form of selection lists so that all you need to do is select from a list of pre-defined input values. In case of numeric values, you can select the metric unit. Thanks to the unified language, terminology, and structure, it’s very easy to identify, describe, and compare products. This reduces a lot of effort when generating offers or commissions and simplifies the selection process for compatible products, among other things.
In short, you can divide the process of classification and description for your products into three steps:
1. Assigning products to product classes
2. Assigning the attributes of the class to the product
3. Describing the product using the list of pre-defined values
In ETIM expert panels and working groups, members actively work on optimizing and developing the model further. Accordingly, this universal solution enjoys a high acceptance rate and international recognition, together with high data relevance and usability in your daily business.
BIM (Building Information Modeling) is the method which allows all companies active in construction to visualize a building across its entire life cycle using a 3D model. With it, they can achieve an optimal and efficient collaboration for all construction projects. The relevant information required to do so comes in a standardized terminology and has a uniform format. You can learn more about how this works in our blog entry “What is BIM? Building Information Modeling in Simple Terms.“
BIM is an integrative, digital planning method for construction of buildings and infrastructure. ETIM, on the other hand, offers a standardized classification and well-defined, uniform product attributes. Applying the classification and attribute structure of the ETIM standard to BIM models is what enables a simple identification of technical products and facilitates their integration into your planning process. Bringing together ETIM and BIM makes it possible to optimize, digitize, and carry out the efficient planning and realization of construction projects. This, in turn, translates into a higher customer satisfaction rate, lower costs, and faster project completions. A clear advantage for all parties involved, be it manufacturer, retailer, or planner.
While ETIM takes care of the classification as well as the attribute structure, thereby securing the uniform description of your products, BMEcat is a standardized format for the exchange of catalog data between electronic catalogs (commonly referred to as e-catalogs). The combination of both standards renders possible a simple and efficient exchange of product data between manufacturers, retailers, and even customers. Besides ETIM, BMEcat is also the go-to standard format for the transfer of product data for ECLASS, profiCl@ss, and the XML format ARGE Neue Medien. You can find further information about BMEcat and an overview of its benefits in our blogpost “BMEcat – A Universal Exchange Format for Your Product Data.”
The PIM system is the central system for managing your product data. And since it contains all your product data, it already contains everything you need for ETIM. By integrating the ETIM classes that are relevant to your industry and products, you can efficiently classify every article while also maintaining a good overview, i.e. you can handle the assignment of products to ETIM classes with PIM. You can also do so across different ETIM versions. Thanks to these relations, the corresponding ETIM descriptions are directly stored at the product and can be accessed and edited using the PIM system. Your user-generated and already well-maintained product attributes can be linked automatically to the respective ETIM attribute, so you can manage everything from a single source without any redundant data management.
We from ATAMYA support you with our knowhow and 30 years’ worth of experience in product data and classification systems when it comes to building your own ETIM structure, the mapping of your product data, as well as data distribution with our universal BMEcat export. A compact solution thanks to which you don’t need to rely on any specialized software for designing and creating e-catalogs. Does this topic resonate with you? We’re always happy to have an exchange over a virtual cup of coffee, without any commitments on your end. Make an appointment for a personal conversation with us.
Author:
Corinna Schneider
Project Manager
ATAMYA
Digitize Your Product Data
In our whitepaper, we let you in on how you can quickly make the shift from manual processes to smart workflows with a PIM system – intuitive, concrete, and easy to implement.
In our digital day and age, product data and information are of decisive importance for the success of companies. Through the optimization of product data, companies can reach out to their customers in a more efficient manner, boost their online presence, and improve sales. This blog entry goes in-depth on product data – what it is, why it is so important, and how you can optimize it in order to accomplish business goals in a much faster fashion. So, if you want to draw from the full potential of your product data, make sure to keep on reading!
What is Product Data?
Product data is all data and information which describes a product and its properties – it forms the foundation for the presentation of your product. At first glance, this seems to be pretty straight forward, doesn’t it? However, you shouldn’t forget that product data can encompass the full scope of product information: the spectrum ranges from simple data such as names, prices, or colors to complex technical data such as efficiency grades. In short: even a simple product can be described by a large number of product data attributes.
Now that we’ve defined what product data is and know just how complex it can be, there still remains the question of how crucial this is to the sales of products. One good answer to this question are the results delivered by an empirical study conducted by KPMG and Statista.
According to this study, 57 percent of the surveyed people wish for detailed product information to avoid personally returning products. 42 percent of the people do also wish for more detailed product images in this context. This means that the majority of people wish for an overall higher quality of product data which can serve as the basis for good purchase decisions without product returns. In short: the quality of data is decisive. This, in turn, is easy to comprehend if you consider the gigantic quantity of products comparable to one another in a vast market such as e-commerce. Customers here have the freedom – or agony – of choice and must, therefore, rely on correct, precise, and elaborate product descriptions and product information. The point being: high-quality data is necessary to stay streets ahead of the competition.
Only this way can you create target-group-oriented product experiences which inspire customers and motivate more purchases. To achieve the desired results, you ought to concentrate on the quality of your data. The following guides you in 5 steps through the best practices for getting the best out of your product data while also optimizing the quality of this data at the same time.
The rule of thumb in this context is: customers think in use, not in product properties. So, before your company gets down to business and starts filling product data with life, it’s imperative that you first step into your customers’ shoes and view things from their perspective. Grab pen and paper and write down your answers to the following questions:
This step is of utmost importance! After all, this is what constitutes the concentration point where all your target-group-specific information comes together. With it, you create a solid foundation from which all subsequent steps follow, allowing you to realize your objectives in a strategic manner.
After analyzing the needs of your customers, your second step should be to present the unique value your products bring to the table in a clear-cut manner. What makes your products special? How can they make your customers’ lives easier? Even seemingly simple standard attributes such as size, color, weight, or material can already leave a lasting impression.
An example may best illustrate this: you’re selling flowerpots in your online shop. Providing only a simple description or a single image with the headline “Flowerpot” to your customers may, with a very high possibility, be insufficient for motivating purchase decisions. What is unique to your flowerpot? Tell an authentic story which inspires your customers to buy exactly this flowerpot and no other.
There’s one thing in particular that you should keep in mind concerning this step: in the first step, you’ve already determined the concepts and keywords which customers type into search engines to look for products like yours. Make sure to integrate them into your description in meaningful ways. This way, you don’t only provide relevant information to your shop but also make sure that your product or service can be found across different channels.
One further golden rule for a successful online shop is the uniformity of your product information. The reason for this is self-evident: providing different currencies, measurement units, or color labels for related products will quickly make your shop appear to be unprofessional and may, consequently, result in consumers opting for the competition instead.
Conduct regular check-ups for validating the uniformity of your product data. For example, have you used the correct currency and the correct measurements in all target channels? Also take the coherence of your product images into account. Make sure that your customers are provided with high-quality images. Such images should clearly display both your product as well as its unique selling points. A uniform presentation of your data is a must-have you should establish across all your online marketplaces.
To make it so that customers can find their desired products when browsing you shop, you should sort your product data by specific product groups or categories. Here, too, the same principle applies: look at it through the customers’ eyes. To make this point as explicit as possible, let’s once more refer back to our flowerpot example: judging from your customer’s viewpoint, in which categories would you expect to find what you’re looking for? The answer to this question depends on the scope of your product portfolio. Assuming you’re a specialized flowerpot manufacturer, a categorization by use cases may be adequate. This is an entirely different story, however, if you offer an assortment of diverse products across several domains. In this case, you should neatly organize everything into parent categories and sub-categories to give your shop a simple structure to navigate through. The flowerpot, to stick to the example, would then be available under the category decorations or similar labels.
When it comes to the structuring of your data, here you should keep another rule of thumb in mind: Make things easy for your customer!
After all, if you customers can get straight to their desired article with little to no effort, you simplify the customer journey and make it more experienceable – all the while reducing the bounce rate to the minimum.
If you’ve carried out step 1 to 4 consequently to the end, then you’ve already done a lot of things right. The place where you store all this high-quality data and product information, however, is also a decisive success factor for the optimization of your product data. For example, if your product information is spread over various excel lists or multiple digital tools and file systems, you’ll be quick to find out that maintaining your data is a process prone to fatal errors which will not remain unnoticed by your customers.
This does not only cost time but also money. Shop operators who sell a large quantity of products across various online marketplaces should, therefore, invest into a PIM. In a PIM system (Product Information Management), you can structure data in a customized manner for target groups, manage large quantities of data centrally, and automate exports to target channels and all given touchpoints at specified time intervals. At the same time, automated processes accelerate your time-to-market. In short: with a PIM, you get the maximum out of your product data and secure your company’s competitive edge.
Author:
Melina Laws
Inside Sales Manager
Automating and Digitizing Product Data Processes
Set the foundation for fully exhausting the full potential of digital product data management. Are you ready to get off to a flying start? In our free whitepaper, we let you in on how you can successfully accomplish no less than this.
