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PIM 2033: A New Era of Product Information Management

14 July 2023
Michael Weiß, Michael Fieg

Product Information Management: A Look in the Rearview Mirror

More than 20 years ago, parallel to the first e-commerce wave, the importance of electronic data exchange for product information grew as rapidly as the desire for easy and convenient internet shopping. Establishing data standards such as UNSPSC, GPC, ETIM, and eCl@ss, and exchange formats such as xCBL, cXML, BMECat, Datanorm, and EDIFACT created the basis for linking data processes along value chains across company boundaries.

Managing product data beyond the ERP systems in marketing and e-commerce departments was soon defined as a core data process. It filled existing and new sales channels: PIM established itself as the home of these work steps.

A market for IT systems emerged, which today endures as an independent discipline in the context of PCM, MAM/DAM, CPQ, and PXM.

Often started as a less integrated departmental solution with the business goal of supplying new sales channels (as quickly as possible) with the necessary product data (PIM 1.0), the maturity of PIM solutions increased after only a few years: from a simple Excel tool replacement to an integral process component of the product data lifecycle, which now addressed data quality, role governance, and business intelligence in equal measure. 

In recent years, the PIM discipline has shown to be flexible, integrating itself into emerging holistic data strategies. The mutability of PIM is still remarkable today:

  • As part of a multidomain MDM strategy, PIM is the gateway for supplier data and, in interaction with ERP, maps the golden record and central characteristic and hierarchy structures
  • As an authoring application, PIM bundles the maintenance of product texts, associated images, and documents, translates and localizes product information, and prepares it for specific channels
  • As a data acquisition and distribution platform, PIM is a component of enterprise-wide data integration components

The specific business cases in each case are still decisive for the process focus with which PIM is integrated into existing process and application landscapes. MDM-related implementations (= Product MDM) are on an equal footing with customer experience-oriented, target group-specific product distribution scenarios (= focus PXM). 

Today's mature software market has consolidated in recent years and oriented itself to these process focal points. Nevertheless, all too often (we can tell some incredible stories here), users and product data managers worldwide make do with primarily Excel-based workflows - what we call the PIM maturity paradox. More on this later.

Our Forecast: PIM 2033

Where is the path leading in the coming years, and what prospects does PIM offer as a data discipline in the future? We have designed a future projection for PIM based on our experience and insights over the past 20 years. We used the following guiding questions to formulate our hypotheses to describe the next decade of PIM:

  • Business: Which use cases are gaining importance? Which business cases will be essential? 
  • Technology: What are the technological drivers of the next ten years? What are the PIM technology concepts of the future based on?
  • Organization: How do PIM process authorities change in the context of centralized and decentralized data platform concepts? What is the significance of the users of data and their needs?

In addition to these key issues, we expect the executive-level agenda to influence PIM increasingly. Risk management and compliance requirements, as well as the urgency for sustainability and climate neutrality (Environmental, Social, and Governance ESG), undoubtedly set essential framework conditions and targets for handling products and their digital images.

The Following Five Hypotheses Present Our Projection of Product Data Management Over the Next Ten Years:

1. PIM Strengthens the Bond Between ERP and CX

Flexible product assortments, complex product structures, and a constantly growing need for digital product services determine product information management capabilities. While process automation (= augmented PIM) is compensating (or rather must compensate) for dwindling margins, especially in B2C retail, the industry is visibly benefiting from the maturity of the PIM discipline, investing in data architecture and organization as well as in data quality and performance. The matured concepts of Industry 4.0 and the increasing relevance of digital products and services with simultaneous diversification of the customer journey strengthen the proportional importance of PIM along the End2End processes. 

In addition, we will increasingly see deeper integration in MDM strategies (=backend focus PIM), completed by a defined data distribution architecture for the broad use of data as a product (= syndication focus PIM).  

2. AI Fuels Process Automation

Not exclusively, but especially retailers will use the management of their heterogeneous assortments and specific customer approaches with high support of AI to automate and accelerate data management processes. Augmented PIM is our answer to today's time-consuming and, thus, expensive manual data maintenance processes in PIM. In addition to process automation, AI-supported use cases for quality control are also part of future PIM solutions:

  • Process efficiency through AI: AI-supported matching services accelerate the onboarding process of product data, become a flexible component of the master data processes in the ERP network, and thus make supply chains and purchasing relationships significantly more flexible.
  • Compliance by Quality through AI: product classification, AI-supported mapping of attributes, values, and unit systems will improve data quality, as will pattern-based comparisons by self-learning systems - for example, for the automatic product assignment of images and documents. On the one hand, this will reduce the risks posed by erroneous product data, while additional service offerings will be created through improved content quality. 
3. The Author’s Share Will Dwindle  And That’s a Good Thing!

In addition to the technical integration of product data, PIM systems have been developed to date as genuine business applications that comprehensively map the authoring processes, i.e., the (creative) writing and classification of product content in the data management department. However, if one takes a closer look, highly qualified employees maintain the simplest data and fill in product attributes, which to a large extent, are undoubtedly fixed due to the product family characteristics. This is truly a waste of human decision-making ability!

The artificial generation and combination of content, texts, attributes, and images will be one of the essential drivers for PIM in the coming years. ChatGPT gives us a first glimpse of the potential that lies in automated content generation. AI automates data management use cases on the one hand and generates additional customer benefits on the other: by enabling target group-oriented text and language translation and visual personalization in almost any channel. 

Customers will benefit from this individualized product communication, learn to appreciate it, and pay into a stable customer relationship. 

4. PXM Increasingly Links Product Data With Customer Data

We see the importance of the product experience, i.e., the entire product experience of a customer across the various phases of the customer journey, as another key business driver in the coming years. This means linking product data with customer data and actively managing the resulting individual product offering: starting with the customer's product research, interest, and purchase intention in after-sales and complaint management. Product communication is more bidirectional than ever, and the outside-in perspective has virtually real-time relevance. Customer and product, individual interests, and selected product sub-ranges will become the critical PIM factor for success in the coming years. 

In this PXM process context, PIM should split into a data layer that essentially consolidates product data, ensures quality, and provides high-quality core data. In addition, a second PXM layer will be responsible for the individualization of product and prospect and actively manage the customer-product relationship as a concrete offer with architectural elements such as CRM and CDP. The insights gained from these 1:1 relationships become part of a company's insight management. They provide valuable data for business intelligence and impact supplier and assortment management, purchasing, and sales processes. Combined with external sources such as competitive data, socio-demographic information, or simple weather forecasts as seasonal parameters, the general forecasting capability of a company is noticeably supported by the involvement of PIM. 

5. PIM Becomes Part of an Overarching Data Platform Strategy

We expect PIM to become increasingly part of an enterprise-wide data platform strategy: as part of a holistic management and use of data in the enterprise to make better business decisions. As part of a platform strategy, product information is set up as a service product, and overarching architecture concepts become a key competence. In this way, the product data domain can be used flexibly, for example, within the strategic framework of a data fabric as a uniform layer over existing data sources. PIM will contribute to this with other architecture components such as ERP and MDM, CPQ, and DAM. 

By linking PIM with other data sources, companies will perform complex analytics to gain insights into product performance, customer behavior, and market trends. In the process, data will be available in an interoperable and application-specific way. This is an essential component of a company's data strategy: we predict that non-proprietary, sovereign sovereignty over data storage and, thus, the general usability of the company's information will be considered a critical success factor.

The PIM vendor market will follow these five hypotheses. Monolithically oriented all-in-one applications will continue to lose importance while new relevant market players emerge. Following the Composable / MACH architecture approaches, especially for the PXM layer, these integrate as part of the cloud-based data platform and score functionally with a clear process focus and the native integration of AI services. 

In addition to a consolidated first- and second-generation PIM market, these emerging PIM specialists are "allowed" alternatives, are quick and lightweight to implement, and thus expand the vendor market in the coming years.

Expert Talk: PIM 2033 – Where is the Journey Heading?

26 October 2023 | Live Webinar | Language: German 

We would like to compare our consultant and analyst view with practical experience. For this purpose, we have invited experts from Stihl, Förch and Dr. Oetker to a panel discussion.  Register and get ideas for your own product information management from market leaders!

Register now

The Gartner Trends for Data & Analytics 

Referring to our Parsionate Future Forecast PIM 2033, the analysts of Gartner name noteworthy theses and expectations, which we set as a general framework for our PIM projection of the next ten years. 

"Think like a Business" is the powerful perspective for future data management to focus on value creation and benefit through (product) data - and to accept the conditions this requires, especially for IT.

IT departments will increasingly build new technology capabilities in the future and ask enterprise architects and D&A functions to collaborate. This is because platform-centric ecosystem thinking requires, in particular, D&A capabilities focused on a consistent and aligned broad technical environment. This allows new capabilities to be built seamlessly and (cost)efficiently and deployed across the organization.

And last but not least, it is the people - in addition to the training of data specialists, especially the users and consumers of product data - on whom we will focus: by developing their data-related skills and accompanying necessary change processes in daily workflows. Organizational concepts such as data democratization, consistent transparency, critical understanding of data (=data literacy), and their responsibility (=data governance) are the basis for making data literacy and data leadership a vision. And ultimately solve the PIM maturity paradox.

PIM Provides Efficiency and Effectiveness for Multichannel Organizations
© Gartner: Market Guide for Product Information Management Solutions

We discussed our view on PIM and multichannel organizations a few weeks ago with Helen Grimster, "Sr Principal Analyst at Gartner and #QueenofPIM," at the Gartner D&A Summit in London a few weeks ago. We agree with Helen that with Customer Experience and Digital Shelf activities, PIM "thinking from the customer" is and remains critical. Every current and future PIM use case is directly related to the customer's expectations - and thus the business cases of companies that drive results. 

The flexibility of platform architectures expected in the future and newly emerging PIM market providers make it possible to set the appropriate process focus between MDM, PDM, PIM, DAM, and PXM and to achieve rapid and measurable implementation successes. It is crucial to find the right balance between process efficiency and automation potentials through AI on the one hand and effectiveness in multichannel addressing and communication on the other. 

A big thank you to Helen for the valuable exchange of information.

Michael Weiß
Michael Fieg
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