Monday, February 24, 2020

Falling into Bad Data

By : Mike Davie


Why entrepreneurs must educate themselves on the risks of the data economy


In this new information-driven economy, a lack of appreciation for transparent data begins to look less like calculated risk and more like carelessness. With our systems and organizations becoming increasingly data-reliant due to breakthroughs in areas like AI, bad data is now a serious, albeit avoidable, problem in every industry.


Today, startups and large enterprises alike rely on data. Ride-hailing apps, for example, use location data for their maps. Marketers spend significant resources on various kinds of intelligence to generate insights about their consumers for advertising campaigns. The healthcare sector, from hospitals to pharmaceutical giants, buys data on health trends and behavior.


The problem is that all this data is sourced from a global marketplace that lacks transparency, increasing the likelihood that companies making critical business decisions are relying on questionable data. This issue impacts real people, both employees and customers. 


Financial loss due to bad data can reach up to 30% of revenues for the average business, and it costs the U.S. economy more than US$3 trillion annually (HBR)–scary, but real numbers.


The costs to startups


If the average company is losing significant revenues each year due to bad data, imagine what high-growth startups are losing. At 30% of revenues, this number could be in the millions–a severe consequence for a preventable issue. 


An entrepreneur may choose to market aggressively in a particular country based on faulty data, and millions in activation might be sunk with minimal returns. Hiring scientists to clean up bad data is costly, time-consuming, and frequently error-prone. Even if data is found to be falsified, which is difficult to do, little can be done aside from discarding it, which does not address the underlying problem. 


According to the 2018 State of Startup Spend Report, startups in Internet services, transportation, and data analytics typically burn through their funding fastest. If they can cut out financial losses from biased or falsified records, they could extend their runways, in some cases, by a third. 


What’s discouraging is that I don’t often hear investors talking about this issue when grilling founders on their downside risks, as they fail to realize that startups with data integrity should be valued more highly. 


Understanding the data economy


The data economy is the production, flow, purchase, and sale of information. This information can be created and sourced from all sorts of places, including transportation apps, social media networks, telecommunications companies, banks, and a range of other products and services that we use daily. Vast amounts of anonymous consumer data are created, stored, and sold in these marketplaces. The buyers, often large enterprises (and increasingly, early-stage startups), then use the data for various purposes. 


Intermediaries and aggregators are involved in most of these transactions. Large volumes of data are bought and sold daily, eventually changing hands so many times that it can be difficult to discern what is original and what has been tampered with along the way. This is compounded by the fact that there is no industry transparency, as middlemen do not reveal their sources, which can allow for bad actors to flourish.


In a big-picture sense, there’s no one specific way that bad data hurts entrepreneurs and slows down innovation. It can affect different startups in different ways, from botched app launches to a series of small efficiency losses over months or years that don’t seem too big of a concern on an individual basis, but can be rampant and costly when added together.


Data authentication technology exists to track information from its source, and uses blockchain to stamp an indelible signature onto it. This process guarantees that, from the time of stamping, any change in the data will result in a misalignment with the unique signature, signaling to the buyer that it has been altered at some point.


Applying blockchain is a simple yet effective way to guarantee data provenance. What needs to catch up is awareness among entrepreneurs of the hidden costs of skewed or incorrect information. Education can push them to ensure the transparency of all the underlying data in their business processes and assumptions, saving themselves and their investors millions, and making the company more viable in the long-term.


As with any global challenge, this problem can’t be fixed overnight. But I’m optimistic that over the next decade, we can start to make serious progress in the right direction and begin delivering transparency to what has become a murky data economy.


About the Author




Mike is the founder and CEO of Quadrant. Based in Singapore, he has been leading the commercialization of disruptive mobile technology and Information and Communications Technology (ICT) infrastructure for over a decade with leading global technology firms in Asia, Middle East and North America. He parlayed his vision and knowledge of the evolution of ICT into the creation of DataStreamX, a pioneering data analysis platform, in 2014. DataStreamX evolved into Quadrant, a blockchain-based platform that allows organizations to verify and interpret complex datafeeds, with a primary focus on location data.

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