The average tenure of a data leader in an organization is only 17 months. There are few organizational functions where the survivability is so low. The obvious question: why is it so difficult for global data leaders to succeed?
In his keynote address at the IRM UK last year, Roberto Maranca, now data excellence VP at Schneider Electric and previously chief data officer at Lloyds Banking Group and GE, very aptly explained that there are three main reasons why data teams fail.
First, data is tribal. Within every organization, there are “tribes” of people who share beliefs, work practices, and, crucially, a dialect. This dialect inevitably colors the data they produce or manipulate. When this data exits the tribe, it is often seen as unfit for consumption by the people who do not belong to that same tribe.
Second, data is an afterthought. Many projects rely on it in a very fundamental sense, yet by the time people realize that the underlying data is not good enough, the budget is almost over, the deadline is looming, and there is no time to go back and fix things anymore. So the project goes ahead with data descoped.
Last (but not least!), data benefits are still very intangible. It’s difficult to quantify the outcomes produced by the work you put in. While everyone agrees that “data is the new oil”, many data teams struggle to produce a strong business case.
The business case for product data
Let’s focus on this last point in the product data domain specifically. How can a PIM executive create a bulletproof business case for their project and ensure buy-in and successful execution?
The conventional wisdom dictates that the closer you are to the customer, the easier it is to quantify the benefits. Perhaps more accurate would be to say “the closer you are to the transaction”.
Product data is very close to the transaction indeed. 81% of buyers research products online before making the decision, regardless of whether the product is later bought online or offline. A vast majority of purchases, and thus – most of the organization’s revenue is heavily influenced by the digital product content.
This tells us that there is a strong business case for better product data. Now, how do we calculate it?
It is helpful to think of two buckets of benefits.
First, let’s quantify the benefits associated with the market reach. Here, we can include:
- Faster time to market for products your organization launches
This is a very tangible and easily quantifiable benefit. All you need to get to the $$$ number is to understand how much faster you can start selling a new product and what that translates into in terms of lost revenue at the moment.
- Faster adoption of new routes to market
These can be new distribution or retail partners, new countries, new marketplaces, new advertising opportunities. All of these require your product data, each one – in their preferred way and format, with specific customization requirements. Again, by figuring out how much faster you can go to a new sales channel, for example, you can calculate how much faster you can start generating revenue from that channel and what that means in absolute numbers.
- Broader market reach
What would it mean for your organization if you could support more sales and marketing channels at once (and keep introducing new ones) without creating a bottleneck in your data teams? Put a $$$ amount on this one, too.
Second, let’s look at the benefits associated with improved product data quality. The rationale goes like this:
- Excellence in PIM allows you to create and maintain up-to-date, rich, and complete content for every product.
- Excellence in product data syndication allows you to distribute that product content to all of your trade partners and clients in their preferred format and method.
- As a result, your product listings are well described, up-to-date, and attractive to buyers – and win a larger share of organizational budgets and consumer wallets compared to competing products.
Now quantifying this is a bit trickier than market reach benefits.
What you are looking for is an increase in both discoverability and conversions for your products. What counts as a good increase varies across sales channels and product categories, of course.
You want to make some industry-specific hypotheses and then watch the results. Be aware that results will come with a delay, as it takes time for the product content you distribute to trickle down the value chain and show up for the buyers looking to make purchasing decisions.
Good hypotheses will likely require segmentation by sales channel, as those will create a different context for your products from the buyers’ point of view. Thus, a different approach is required to achieve a good fit with the context of each channel.
Hopefully, this gives you a good start into connecting data excellence with tangible value for your organization.
Author: Alexandra Maximova : Sasha has been working on enterprise digital transformation initiatives for the last 5 years. At Productsup, she helps develop the company vision and best practices for the Product Content Syndication solution. She is excited about all things manufacturing, and will show you how to deploy your digital product data in the most impactful way possible.