The Big Data era has pushed marketers trained in the 4 P’s to now be conversant in the 4 V’s. Yet it’s clear we’re still in early days when it comes to the full impact that data-driven marketing will have on companies and the economy. While only fools and keynote speakers claim to know the future, short term trends are already drawing some clear pointers towards what’s coming next.
Paying for the Good Stuff
Currently, a lot of data floats around essentially free for the harvesting. That’s led to the false assumption that data is cheap. The cost of obtaining good data is likely to rise in response to multiple factors currently at play. For one, privacy issues continue to lurk around the issue of data ownership.
Decisions outside the US show that the world is moving towards a more restrictive environment for the gathering and storing of personal data, which also relates to the growing epidemic of security problems around data. The global public and their governments are growing less tolerant of data breaches treated as just the price of doing business.
Because the cost of identity theft now exceeds the cost of property theft, there’s likely to be more regulations and legal liabilities covering its storage and use.
Finally, the advances of the future depend as much on the quality of the data as the quantity. More reliable and accurate data will command a premium. Combined together, these factors will make data more expensive from either a direct financial point of view or from the indirect costs to comply with higher regulatory standards. Data budgets will come to rival media budgets for CMOs.
The advances of the future depend as much on the quality of the data as the quantity.
Shifting from Input to Output
So much of the infrastructure around data is focused on how to bring it together. Data warehouses and DMPs rely on the value of having everything in one virtual place. The “single source of truth” imperative rightfully focuses on minimizing the inefficiency of duplicate or contradictory data stored across multiple sources.
While that will continue to be important, it’s of growing importance to understand how that consolidated data feeds all the potential applications it can impact. The tough job of collecting, cleaning, and combining the data is still a job half done. The other half is figuring out how the data gets out to product development, marketing communications, sales, and other teams who have different needs from that shared data resource. The different types of questions they strive to answer impose different demands on the shape, timing, and relationships built into the data. Marketers will have to map how data goes out as fully as they currently map how it comes in.
Real Learning the Artificial Way
The early stages of competitive advantage came from simply having the data. The first party data exhaust thrown off by Google, Amazon, Facebook, and others provided proprietary information not otherwise readily available. The second stage was having a robust tech stack to manage the data. The challenge now is dealing with the sheer amount of data available.
The continued exponential growth of what we’re all throwing into the data cafeteria makes it impossible to digest by established means. Artificial intelligence and machine learning are the only ways we’ll be able to make full use of the data to operate on a truly individual level. We’ll move to the meta state of competing over who’s best at learning new ways to learn. And while the character of a brand used to be most visibly reflected in the type of language it used, soon that brand character will be better demonstrated in the type of algorithms we use.
Together these indicators represent the move from the Big Data Era to the Deep Intelligence Era. If the main components of winning in Big Data were harvest and storage, the competition in Deep Intelligence will be around processing and distribution. And with it, a new set of banners along the beach.
DIGITAS GLOBAL BRAND PRESIDENT