Data economy: the oil of today and how to not get left behind
All the way back in 2017, The Economist called data the new oil. It wasn’t an unfair comparison since data also needs to be mined, extracted, refined, evaluated, and only then bought and sold. This also called for the rise of the data economy. It is a whole new concept, where the most valuable commodity is no longer a tangible asset but a byte of information, written in 1s and 0s.
What is data economy?
Continuing with the analogy, much like the oil-based economy is a conglomerate of various industries, contributing or benefiting from the oil, so is with data economy. Digital Realty — a giant of data centres — provides a clear-cut definition in their 2018 Data Economy Report: a financial or economic value created by retrieval, analysis and storage of large amounts of detailed business data at high speeds.
For example, the shipping industry earns from transporting crude or refined oil from one end of the world to another while also generating profit for the refinery by using the oil to power its ships. It forms a cycle that is almost precisely replicated in the data economy.
A trucking company that tracks the movement of its vehicles requires geolocation data. That data is then scrubbed and transformed into readable insights that data analysts can use to provide more efficient truck routing solutions. That is how a traditional transport company becomes part of the data economy. Data becomes valuable to them; data analysts become essential to their business. And in turn, a company specialising in data collection and processing gains a customer.
What makes data so valuable?
On its own, a set of 1s and 0s is not valuable. Instead, the value is created by companies holding and using data to earn higher profits. Like crude oil in itself is not usable or valuable, data needs to be processed and, most importantly — understood, interpreted and put to good use. It’s the process of understanding and using data correctly that determines its value.
Research done back in 2015 backs up this claim. Worldwide, over 40% of companies use data to back up their decision-making. This process is called DDDM — or data-driver decision making. Data usage is most prevalent in retail (to no one’s surprise), specifically in sales & marketing departments. Companies report that while using DDDM, they had an 8% increase in revenues and reduced operational costs by 10%. With the other half of companies planning to utilise data in their business, these numbers are bound to go up. Data is showing its real value in numbers.
Why data analysis is an important skill?
Data economy exists, and it’s all fine and dandy. The question is: what do we, as marketing specialists, do about it? As individuals, we must improve our skills in data analytics. That’s an excellent first step.
Marketing has already become a fully-fledged member of the data economy. Data on customer behaviour drive PPC. Our ad slots on social media and beyond are carefully analysed and placed to the right audience at the right time. While a slow-crawler compared to PPC, SEO is also based purely on curated content, pre-determined by keyword rankings — which are also based on data. Even this article is based on some data, indicating that you are going to find it interesting.
This is also where the analytics tools come in. As marketing specialists, we can all benefit from better insights into data. Our competitive edge now lies in graphs, charts and comparisons of historical and current data. With current speeds, by 2025, humanity will have generated 463 exabytes of data. With the growing amount, more sophisticated tools will be necessary to process the data. And the data economy will grow, requiring ever more skilled specialists that don’t just create content and advertising but position their creativity based on sound data reading.
Neil Hoyne, the chief measurement strategist at Google, said during his presentation at the summit on the use of analytics and AI in the post-pandemic world: “The companies that are going to win are the ones who are using data, not guessing.” This is true for individuals and specialists: the more we are adept that using data in our daily work to our advantage, the higher the chances of success in today’s data economy.