Mike Davie talks about why must marketers be educated about the Data Economy
Tell us about your role and journey into Marketing Technology. How did you arrive at Quadrant?
While studying for my Master’s of Computer Information Systems at Boston University, I realized the potential for building a better system for data producers, vendors, and buyers. I was one of the first public beta users of Amazon Kinesis, an early project to collect, process and analyze real-time streaming data, and I have been focused on optimizing data ever since.
In 2014, I founded Quadrant (formerly DataStreamX until it rebranded in 2018), the world’s first online marketplace for real-time, high-value data sets across industries and borders. At the time we were transacting billions of data sets, working with some of the largest companies in the world. We saw that there was an issue of transparency in the industry, organizations were buying and selling data without any idea as to the source of that data opening themselves up to using poor quality data (replicated or false data). We saw the potential Blockchain has especially when it comes to authentication and noticed an opportunity to apply this technology to the data supply chain. Quadrant, which uses Blockchain technology to authenticate and transact data, currently processes over 50 billion records a month between organizations.
I had been working in data for more than a decade when I lit upon a solution to the biggest problem of all. How can we navigate a data universe made up of disparate and unreliable sources? How do we verify data’s authenticity and maximize its value and applicability? After working with hundreds of buyers and sellers of data, as well as processing billions of data records monthly, and assessing what worked and what did not and for whom, I realized the data industry needs something bigger: a definitive map for the data universe.
Last year I launched Quadrant Protocol to meet this need. I gathered a team of long-term collaborators and new reinforcements that share my belief that Quadrant Protocol will become the standard for decentralized data and will be the next game-changer in the Data Economy. Quadrant is designed to prevent data ecosystems from falling into the same traps they have in the past, with bad actors flourishing due to the industry’s lack of transparency.
What makes Quadrant so powerful is that has authenticity and provenance at its core. It first establishes trust in the delivered data and between the transacting parties before it starts to map disparate sources of data.
Why must marketers be educated about the Data Economy and how it works?
Marketers must be educated about the Data Economy because it is indirectly influencing their most important campaign decisions, such as where to place ads (online and offline) and whom to target (demographics and profiling). If the underlying data on which they are basing their Marketing decisions is not accurate, the campaign will not be effective, and it will be dollars down the drain. This is often due to bad actors and murky data that is rampant in the Data Economy as a result of middle-men who buy and sell data on data exchanges and marketplaces, often with little accountability in terms of where the data originated from and whether it’s been changed since the source.
Broadly speaking, the Data Economy is the production, analysis, selling and use of data, with the Data Supply Chain being the link between the end-data consumer and the original source. However, very little is known about it as much of the conversation around data has centered on issues of privacy, specifically when it comes to social media platforms such as Facebook.
Data producers (such as ride-sharing apps which emit data on the movements of passengers and drivers) create data, which is then stored and often purchased by a third party. The third parties who purchase this data then use it for their own, separate business purposes, which can include Marketing functions. Data sets can be passed through so many hands that its provenance, integrity, and value ultimately come into question. For marketers, this is an issue that needs to be addressed.
Which technologies form the foundation and pillars of the Data Economy?
Big Data stacks are the core technology group at the heart of the Data Economy, but many peripheral technologies play a role. For example, huge global networks are required to get the data from point A to point B, and online marketplaces are required to enable the buying and selling or vast data troves at scale. In our case, it’s Blockchain technology that we leverage to authenticate data. Artificial Intelligence (AI), too, is increasingly relying on the Data Economy to power its decision-making algorithms, which makes accurate data even more important. Some estimates estimate the global Big Data market value as US$103 billion by 2027.
How is technology able to authenticate location data for marketers, generating more trust and accuracy and making data more accessible?
We use Blockchain-enabled Data Authentication technology to stamp data with a unique signature (also known as a ‘Hash’) as it is created, placing it on Quadrant’s Blockchain. This guarantees that, from the time of stamping, any change in or corruption of the data will result in a misalignment to the unique signature, signaling to the buyer (or marketer, in this case) that the data has been changed.
By using Quadrant’s Blockchain-enabled Data Authentication technology, marketers can create an immutable ledger that can reference the data by hashing it and putting it on the Blockchain. So, whenever it is consumed and analyzed, they go back to the data source, knowing that the data is true to the moment that the data was stamped.
Quadrant enables the tracing of the bad data to the bad actor whenever the data is delivered in a bad state. This allows for trust and transparency in the Data Economy, as marketers can verify the source of their data. Importantly, it also allows them to map disparate sources of data together, organizing the data and making it easier to innovate and develop creative solutions and products.
What is the impact of all this on Location-based Marketing, Location Intelligence, and Consumer behavior?
On the location-based services side, private companies and marketers are using data at a much greater pace – especially location data – with the global market estimated to exceed US$13 billion by 2025. Organizations and their Marketing teams are relying on the analysis of this data to make serious business decisions on expansion and strategy, or to make purchases worth many millions of dollars.
Location data-emitting apps are becoming more accurate and usable, from ride-hailing to augmented reality, and are being sold to both consumers, governments, and enterprises. We are seeing a growing demand for accurate location data, too. This is caused by a number of factors but in particular, the consequences of poor-quality data are becoming more severe as bigger decisions are being made based on data.
However, we are also seeing more ROI from companies using location data and this will continue, especially as the quality of location data continues to improve. Lastly, as the quality of location data gets better, so is the quality of Machine Learning and AI, both of which rely on data to make decisions.
I believe that we will start to see some amazing innovation and growth in this space. Location data usage has grown significantly and more companies are seeing real results. As more companies (from start-ups to larger established firms) become data-driven we will, in turn, see more firms produce their own data. Once this is mapped and organized then we will start seeing innovations and solutions that are perhaps unimaginable today.
Of course, we do need to address the issue of data provenance and transparency within the Data Economy as a whole – which we are seeking to tackle through solutions such as those enabled with Blockchain. Lastly, we will see more regulation emerge, the effects of which are hard to gauge. However, if we develop regulations that protect the individual yet at the same time make it easy for firms to access and use (anonymous) data in order to innovate and create solutions, then our lives will be improved thanks to location data.
Can you share more about Quadrant’s own Data Authentication technology and how it is bringing Transparency and Data Provenance to a murky Data Economy?
Quadrant maps and authenticates data, making it easier to buy and sell quality, authentic data and spurring innovation and solutions for organizations. Quadrant consists of two parts, the Quadrant Protocol and the Quadrant Platform, which combined allows businesses and governments to solve their data challenges and optimize data to fit their needs.
Firstly, we stamp the data with a unique signature through the Quadrant Protocol. This ensures that data can be traced and verified back to its origin and allows us to verify that it is authentic as at the time of stamping. Following the stamping process, we link buyers and sellers together through Smart Data Contracts which makes the Quadrant Platform accessible to any company and organization. The Quadrant Platform allows organizations to trace the data and then map it into usable, targeted data sets that de-clutter the field of information for Marketing professionals and organizations, allowing them to use inputs relevant to their needs and to analyze them effectively and efficiently. By creating an ecosystem that enables access to data that is authentic, traceable and mapped, Quadrant solves data challenges and spurs innovation.
The Quadrant platform processes over 50 billion records a month, enabling organizations in every industry to purchase data that they can then use to make business and policy decisions based on physical world interactions.
We are developing new technology, making it easier for organizations and their Marketing functions to find solutions to their data problems. We launched Quadrant Protocol late last year and earlier this year we made it possible for organizations to purchase credits for our services using fiat currency. By allowing companies to use currency, we ensure that they remain in compliance with traditional book-keeping and accounting structures.
Of particular interest to marketers is the microservices layer that we have created that – among other things – allows advertisers to identify footfall in a specific area with a much higher degree of accuracy. We have developed a number of algorithms that help clients make better use of their location data and provide the first line of analysis for companies, rather than just the raw data.
Which key technologies in Marketing and Sales are you keenly following?
I am particularly interested in the AdTech space as this is the next step in the value chain. Quadrant provides the authenticated location data (audience segments etc) and this is then used to in targeted ad campaigns. Around US$100 Billion is spent a year on digital advertisements with much being wasted on scams and bots. Project Proton uses Blockchain to bring transparency to this sector and I am interested to see how that progresses.
How can Marketing teams better prepare for AI and Automation? How do you prepare for these technologies?
AI will only be as good as the underlying data that powers it, which means we must focus on ensuring the provenance and transparency of that data if we wish to benefit from superior AI in the future. AI technology is becoming very powerful and useful, but much like any software or set of rules-based algorithms, if you feed in bad data you will get bad outputs (‘garbage in, garbage out’, we often say).
For businesses relying on AI for Marketing and other key decisions, bad data could lead to disastrous and costly outcomes. Marketing teams can prepare for AI and Automation by understanding where their data comes from and the steps they can take to guarantee its provenance. For technology hubs like Singapore, for example, this is particularly important as the city-state has set itself the goal of becoming a world-leading AI hub and Smart Nation. Good data will be a key determiner of the success of that vision.
Which Marketing and Sales Automation tools and technologies do you currently use in your current roles?
We currently use HubSpot to automate many of our workflows.
What are your predictions on the most impactful disruptions in AI and Mobile Management technology on the Location Data business for 2019-2020?
From my perspective, it’s really the other way around: high-quality location data, which is transparent and verified, will lead to more impactful disruptions from AI and mobile management technologies that rely on it. For example, first-responders in an emergency might use AI and mobile management technology to figure out the best route to the scene of an accident. City planners may use complex AI algorithms to calculate catchment areas when planning a new hospital or medical facility, based on population movements and neighborhood profiles.
To use a third healthcare example – it was World Health Day earlier this month – during the spread of a virus governments may use AI to calculate contagion areas and the best way to stop the spread. All this relies on the provenance of the underlying location data, and so I think it will be of the utmost importance to the effectiveness of AI and mobile management technologies going forward.
My prediction for 2019-20 is that, with the right use of Data Authentication and Blockchain-based mapping solutions, we’ll continue to see exponential growth in the potential of these technologies and the extent to which they’re applied successfully in real-world scenarios.
Tell us about the Sales culture that you represent.
Our culture revolves around solving problems. A perfect example is that of provenance and authenticity in the Data Economy which we ware seeking to solve through Blockchain-enabled Data Authentication technology. Additionally, our Sales team seeks to educate potential clients on the solutions that we offer. So problem-solving and education are core parts of our Sales culture.
How would you describe the workplace culture of Quadrant?
As a team, we are always learning and we really strive to build a culture and environment where we can create things, experiment and learn. We are looking to solve real-world problems and so we need to create as permissible a culture as possible. We are also a very diverse team with multiple nationalities, cultures, and backgrounds which really helps.
Do you have any hobbies outside of work that you’d like to highlight?
I have a long-time passion for triathlons. Growing up, however, I was never particularly known for my sporting ability, more of a ‘last picked on the team’ or ‘lucky to be picked on the team’ type of guy. I preferred computer coding and playing grunge and punk rock. But by the time I was a teenager, I started developing an interest in extreme sports such as Snowboarding and by the university, I got into downhill Mountain biking and Rock climbing.
I started running as a means to quit smoking ‘cold turkey’ over a decade later. From doing 10k runs I then progressed to half marathons across the Korean demilitarized zone, which then turned into my first triathlon before graduating to Xterra (off-road triathlon). From then on, I have upped my game after every race. Xterra races force me to push my boundaries and put myself into uncomfortable positions. I need to solve difficult and unexpected problems under time pressure and when very tired both physically and emotionally.
I need to find new and different avenues for self-improvement which also gives me a new perspective on life, which translates into my professional life also. Of all the sports out there, off-road triathlon best reflects the ups and downs of running a company, merging extreme obstacles and adrenaline-fuelled bike routes with energy-sapping long-distance running. I have participated in Xterra triathlons in places around the world including Malaysia, Thailand, and the APAC Championship in the Philippines. I also ran the DMZ Half Marathon along the border between North and South Korea. Most recently I competed in XTERRA Asia-Pacific Championship in Taiwan in March.
How often do you measure your technology stack and how do you relate it to your Sales performance?
We measure our technology stack performance monthly, we take the numbers from the platform and use them for our KPI calculations.
What startups in the technology industry are you watching keenly right now?
There are two main types of companies we are looking at. Firstly, we are interested in location data companies. Many of these firms are our clients and we are always on the lookout for new start-ups and firms and any new innovations and technologies that they may bring. Secondly, Blockchain-data companies are of interest, especially companies that are building new tech to solve data supply chain issues.
How do you prepare for an AI-centric world as a Business leader?
Firstly, I think it’s become widely accepted now that AI is going to play an important role in terms of the new responsibilities placed on business executives and the C-suite. By leveraging deep data and Big Data, AI will offer business leaders new insights and help them solve problems whether they’re a small start-up or a Fortune 500 company. To take advantage of this, they will have to be prepared to boost spending on AI and data-related initiatives – to become more data-driven themselves – perhaps more so than in any other area of their business.
Digital assistants, as we have seen from tech giants like Google and Amazon, are gathering huge amounts of anonymous data and providing automated services to millions of consumers around the world though clever use of AI technology, whether that’s in targeted advertising, online shopping, or hailing a cab from the sofa. What many business leaders may not realize is the edge this is giving these companies over the rest – not so much in terms of AI per se, but on the data production side.
By getting consumers to willingly hand over their first-party data by engaging with AI-powered services, these companies are becoming data powerhouses and self-sufficient in terms of their data needs, unlike most businesses who must spend millions to buy that data. My advice to business leaders would be to get to grips with the relationship between data and AI because the two are increasingly related, and be prepared to invest in both exponentially. Luckily, there is still time – just 3 percent of executives surveyed by Forbes said they feel AI’s been “fully deployed” in their business. Aside from AI, Blockchain is also a big push for many businesses, a technology I’m proud to have been an early adopter of at Quadrant.
Thank you, Mike! That was fun and hope to see you back on MarTech Series soon.