We should talk about Data Lifetime Value.
You’re probably familiar with Customer Lifetime Value (CLV), which focuses on maximizing the total worth of a customer over the long term. CLV shifted business thinking away from one-off transactions and toward fostering deeper, more profitable relationships.
I believe we need a similar approach with data.
Too many organizations still view data through a short-term, use-case-specific lens: collect it, clean it, and use it for an immediate need. It’s the data equivalent of a one-time sale. But like customers, data’s value should be considered in a long term context, actively managed and grown over time.
Adopting Data Lifetime Value (DLV) would treat data as a strategic asset by: 1️⃣ Quantifying data’s long-term value to the business
2️⃣ Identifying the levers to maximize its potential—historical depth, input diversity, strategic relevance, broad applicability, top- and bottom-line impact
3️⃣ Using data to build sustainable long-term advantages
DLV would also force us to prioritize: it identifies which data is valuable for a broad range of high-impact use cases and which data is maybe nice to have, but overall more expensive than it’s worth.
Adopting a DLV view isn’t just a tactical adjustment. It requires serious investment in governance, infrastructure, and cross-functional collaboration to ensure data is constantly nurtured and aligned with business strategy.
Like CLV, companies that embrace DLV will be the ones that constantly evolve and adapt, outpacing the competitors that are stuck in a transactional mindset.