GenAI: Compete and Win with Data - Now More than Ever!

GenAI will require leaders to have a solid understanding of how data contributes to their competitive advantage. Companies who appreciate the centrality of data to their business model will outperform those who treat it as an operational expense. The reasons are twofold:

1) Data spend is investment: While some aspects of data management will become more efficient, overall data spend will go up as GenAI requires better data, faster. Thinking of data spend as investment means tracking the upside, not just the cost.

2) Investments need to be intentional: As GenAI plays a more mission-critical and eventually central role in business models, the opportunities are greater, and so are the risks if decisions are made on bad data. Knowing how data contributes to the business is foundational to intentional investment.

How do businesses compete with data?

They acquire data that their competitors do not. This can mean different things in different industries. A retailer may use its in-store video to track foot traffic patterns. A B2B sales organization may train its sales reps to ask certain questions of customers and capture the answers in a database. A SaaS platform may capture novel data about mouse movements.?

They mix data together in novel ways that their competitors do not. Correlation is not causation. That is, until someone shows that it is, or that there is at least some influence or signal. Only by mixing different datasets together can relationships between data be discovered. In the absence of hard keys to link data, data scientists will have ideas on how to substitute that with probabilistic methods.

They draw conclusions from data that their competitors do not. Different foot traffic patterns might lead to different total purchase value. B2B customers who have recently returned from vacation might tend to buy more. Platform users spend a lot of time shuffling back and forth between two particular tabs. Knowing what metric to impact is critical to making the leap from data to insight.?

They take action on data-driven conclusions that competitors do not. Knowing is not doing. A data strategy is only end-to-end when it moves from data to insight to action. In some cases the infrastructure is in place to do this. The algorithms that decide which ads to show us, or that populate our social media feeds, have value because the ad servers and platforms are able to alter the content they show based on the algorithm outputs. In other cases, there will be blockers: it isn’t easy to reorganize a manufacturing line, or get out of a long-term contract that made sense when it was signed and no longer does. Internal politics can be a blocker if an action taken doesn’t impact all constituents in the same way.?

They learn from their actions in ways that competitors do not. The list goes full-circle with this because the after-action review is another data input that some companies are good at and others not. Knowing what went well, and why, and what poorly, and why become another set of inputs to expand the analysis.

Here are some keywords that I did not use in the above list: data governance, data management, data science, analytics, reporting, data ops. That’s because this second list in an inventory of table-stakes capabilities: necessary but not sufficient for data to be a competitive advantage. Not all data can or should be governed, analyzed, and operationalized all the time. Intentional investment is as much about focusing foundational capabilities as it is about sophisticated models and GenAI.

GenAI means that data (structured and unstructured) will continue its move to the center of corporate business models: it is beyond being just an input. Knowing how data contributes to your competitive advantage will help you win in the marketplace!