Building a Product Data Management platform business case

A product data management (PDM) platform is a centralised software system to store all information related to a set of products. This platform supplies all other systems across the company (such as ERP, logistics and warehousing or an eCommerce platform) with the information they need about products. The product data can be deployed for multiple business functions, from design to marketing and can include computer-aided design (CAD) data, models, parts information, manufacturing instructions, requirements, notes and documents.

High-quality product data is critical to the success of businesses operating in an eCommerce environment which is expanding, diversifying, and becoming ever more sophisticated. But its usefulness can degrade by more than 15% in terms of accuracy and value, as it gets shared and updated. Consequently, when those bad data sets are used for operational and strategic decisions, results of frequently inefficient at best.

As one of the key stakeholders in pushing for the deployment of a product data management platform, you might think it’s a project which sells itself. The online research on the quality of product data management across many sectors seems to indicate otherwise.

Essentially, the decisions for a master data management business case are made at a level where strong arguments are required to loosen purse-strings in very challenging times. That is why you need to develop a powerful product master data management business case to sell its importance in driving optimal business performance into the longer-term future.

What are your baseline requirements?

Formulate a vision and strategy for product data management, from which a business roadmap is created. This is foundational for the strategic aims of the initiative in terms of measuring value added to the business with measurable KPIs
Establish quantifiable business benefits for the PDM initiative (percentage increases, reductions, and so on)

A business case model

In designing a roadmap for an MDM business case, identifying the ‘as is’ and contrasting it with where you aim to be is a useful and compelling entry point. The major problems with product data management tend to be endemic across sectors and business models.

The first is having multiple sources of information, especially in large organizations. This means searching for the information and not being certain that what you find is the correct and definitive version. The associated cost is high, directly, and indirectly. Directly, as it is consuming the time where highly paid staff could be carrying our more profitable tasks. Indirectly, because the impact of using incomplete, outdated, and plain incorrect product information will inevitably have a noxious impact on sales, returns rates and abandoned product searches.

Data are not fixed and immutable entities. They change over time, can get out of sync, become fragmented, and end up as being unreliable and unusable. If staff from various departments no longer trust that degraded information, they lose trust in it and you run the risk of implicitly encouraging the use of siloed data sets, with various versions floating around the company.

Governance

Identifying the best and most suitable technology tool is just part of the overall PIM project framework. Only by clarifying and constructing a fit-for-purpose product data governance framework will you be able to truly align your data management architecture with your enterprise architecture. In turn, that will enable you to achieve measurable targets in terms of growth in sales revenue gains, internal effectiveness, and feasible scalability.

Developing a business case roadmap

There are two main areas where the benefits of a product data management platform can be measured: customer-facing and non-customer-facing operations.

In the first case, the aim is for measurable improvements in searchability, discoverability and conversion rates for your products. Putting a quantity on that is a necessary risk, despite other variables existing outside your control (such as pandemics).

In terms of non-customer-facing goals, capacity for growth, scaling, and speed to market are the key drivers:

Faster time to market for newly launched products – a tangible and easily quantifiable gain in terms of how it translates into lost revenue at present

Faster adoption of new routes to market – expandability to more channels, new markets, and new channel types

Measurability

Measuring added value and benefits to the business is highly effective. As far as possible, progress towards the objectives of a PDM project should be monitored and quantified, in terms of percentage rises or falls, volume in numbers, frequency, and more. Milestones during and towards the end of the process need to be clear and transparent, to indicate whether an objective has been achieved. Thus, the organisation and foundations of such a project must be a structured system of processes, where tracking is built in throughout its lifecycle.

What you are implementing and WHY

The desirable endpoints for a product data management platform will certainly offer the following attributes:

a single storage hub for all types of product data
tracking and governance of product data lineage, ownership, version control, and release status
creation of single-version technical specifications to manufacture, maintain, update or upgrade products
a full audit trail of all changes made to product information
collaboration among internal and external teams as well as partner organisations such as suppliers

When it comes to the ‘why’ of your business case, your business case should be outlined without the need to use “MDM” or “data” at all. For example;

The implementation of the product data management solution will

increase customer on-boarding rate by XX% while reducing error rates to X%
reduce slack by XX% in the product introduction cycle
increase asset up time and timely usability by XX%

Categorising the business benefits

One reason for unsuccessful product data management initiatives is the lack of a structured framework to qualify and quantify the value created by excellent data management. There is more than one way of developing such a framework but, as an example, let’s look at this four-step approach.

1. Focus & Strategic Alignment —clarifying the business value

According to Industry analyst, Gartner, over two thirds of product data management projects do not get much further than piloting and experimentation, as they do not address key questions in the right order: they focus on the ‘what system do we want?’ and ‘how will we implement it?’ rather than burrowing down to the origin story – ‘Why are we going to implement this PDM initiative?’ At the pre-project / discovery stage, this must be the first step.

All stakeholders, decision-makers and budget holders need to agree on the value assessment objectives, which means being aligned regarding the company’s strategy and understanding of the company’s strategy. Only then is it advisable to focus on what and how by further research into the potential (and probable) value drivers.

2. Stakeholder Interviews — prioritising resources

There is most likely a wide range of stakeholders from both business and technology teams, who are required to:

Capture the challenges and opportunities to reach cross-functional alignment on the goals of the initiative
Deep dive into current and future use cases from across the organisation – they are essential for mining potential PIM attributes to respond to needs.

3. Evaluating attainment of KPIs

The project team review the initial findings, using them to iterate the case based on achievable solutions. This is ongoing, to ensure the findings are accurate and defensible when presenting to those stakeholder managers responsible for defining and/or approving a budget for this major new technology investment.

The project obviously needs representative users/stakeholders from both business and technology teams impacted by the planned enhancements in data management.

4. Reporting to the executive

Finally, a review of findings and proposals take place with the executive sponsor (at C-suite level, ideally). This will likely include a presentation to key personnel and executives to outline

the benefits of implementing a product data management platform
the projected cost-benefits analysis and ROI for the enterprise
the targeted business outcomes, value map and time frame

Final words

Sometimes, we can’t see the wood for the trees. In the fast-moving, frenetic, and AI-driven eCommerce landscape, it would be easy to be reactive to operational imperatives rather than being able to step back and adopt a considered perspective on solutions for the company’s broader and longer-term strategy. Expert third-party input is often more capable of assessing future goals in a timely fashion (and where the gap exists in terms of reaching them) than can be achieved by simply canvassing internal stakeholders.

At Start with Data, we’ve supported many clients in developing robust business cases for a PIM solution implementation. Reach out to us and we can have a more detailed discussion of how we can support you with your PIM business case and implementation project.