Product Portfolio Discipline Makes Data Retention A Measurable Decision

The volume of data a company generates, moves, and keeps has started to carry a cost measured in megawatts and delivery dates. The constraint on enterprise AI has moved from chips to electrical equipment. Data center operators are competing for factory slots on high-voltage transformers and substation gear, and the wait for those units now runs into years. Enterprises are finding that what they choose to retain has become a decision with a number attached.

Sahar Mirmahmoodi is a global product and business leader in instrumentation, sensors, and industrial and building automation, focused on the shift from hardware to SaaS and AI-enabled platforms. She has led product, P&L, and commercial strategy across the sector, including running a global business unit for water-quality instruments, software and services, with earlier roles spanning industrial components and building-automation/IoT platforms. Her career has run through the equipment layer that now constrains AI buildouts.

"We're at that level to think, 'Do we really need to create so much data?' The cost of creating data is no longer abstract. It's going through the whole physical layer, and it tangles into everything behind it: power, equipment, and lead times," says Mirmahmoodi. The electricity bill is the visible end of that physical layer, and equipment that takes years to build sits behind it. Her argument is that the amount of data driving both deserves the same scrutiny a company applies to anything else it funds.

Transformers set the pace

Lead times on high-capacity transformers have stretched to four years, and those dates now set the schedule for new grid capacity. Transformer production is capital-heavy and low-margin, and several manufacturers sold or wound down those lines over the last decade. Mirmahmoodi was inside that industry while it happened, and took part in one of the exits herself. "Within the last decade most of the players wanted to move more into the use case of the software, sensory intelligence, which makes sense," says Mirmahmoodi. "But the industry didn't think about the heavy duty elements of this supply chain. Transformers are one of them. It takes years and multi-hundred millions to build a capacity."

Enterprises that can't add capacity on demand have to decide which workloads justify the capacity they can get. Mirmahmoodi counts that as a benefit. "The physical limitation that is happening right now is going to slow down the expansion," she explains. "I'm not saying AI is going to slow down, but the bottleneck gives us a moment of pause as leaders, to think about how meaningful and insightful we can be about the choices we make regarding the data."

Physical cost and buildout budget

Enterprises budget each stage of their own buildout separately, and Mirmahmoodi's argument is that the compounding never appears in a single business case. "If you want to have more data centers, then you need more physical layers, and the physical layers need to be more intelligent," says Mirmahmoodi. "When you ask for more intelligent sensors and more chips to be integrated in your system, then you're collecting more data, and the more data you're collecting, the more data centers you need."

Capacity kept pace with production for most of the last decade, so the question of whether a given dataset should exist rarely came up. Mirmahmoodi describes teams reaching for more AI without pricing the request, and she believes the correction arrives through cost. "At ten euros a month, then fifty, you start thinking," she says.

The business case for data

The first question is whether a dataset can support AI work at all, and Mirmahmoodi wants people with the right expertise answering it. A dataset that can't be used consumes the same storage as one that earns its place. Migration adds a second bill on top of that, and in some industries the alternative is to generate the data again from scratch. "In life science they're using AI in drug discovery, and they already have a lot of patient information," she notes. "But it's much more costly for them to transfer them into a new AI ready form than to start a new experiment with new patients and new data."

Mirmahmoodi applies the discipline she used on product portfolios, where a line gets reviewed, repriced, and eventually retired when the business case stops holding. She spent years making those calls on instruments and sensors. "Product discipline isn't only about retiring what doesn't work, it's asking, up front, whether something's worth creating at all," she adds. "Data is no different: is it worth generating? Does it still earn its keep? It's a product, so apply the same principle end to end."

Catching up the KPIs

Every solid business case needs a metric behind it. Data lifecycle decisions pay back over years, while the numbers executives report on land every quarter. The same mismatch runs through hiring, where the skills a company rewards steer people away from work that later turns out to be scarce. "The technology has been advanced a lot, but in my opinion, the financial and HR KPIs relevant to these technologies still measure success according to the standards set 100 years ago, built for heavy, factory-era industry," she says.

Sustainability reporting gives Mirmahmoodi a precedent, and she poses the same scenario for data. "Think about how the CO2 footprint evolved. A decade ago almost no one measured it. Today it's a standard part of corporate reporting, with targets and disclosure," she concludes. "Data is on the same path. Its cost and footprint will move from something we ignore to something leadership has to measure and answer for. And once you have to report it, you think very differently about how much data you create."