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Data, data everywhere - and plenty of drops to drink

Interview

Data, data everywhere - and plenty of drops to drink

Doug Laney, Innovation Fellow at West Monroe, joined CTech to discuss Big Data, Infonomics, and the value of our online information

James Spiro | 09:13  08.12.2021
As businesses collect more information on their consumers or users, questions are being asked about how exactly that data can be managed, measured, and ultimately monetized. In the next few decades, it has become clear that companies will continue to make a profit from the information we give them, and yet concerns are rising among a general population that recognize the risks to privacy violations, online freedoms, and dignity to operate online.

CTech spoke with Doug Laney, Innovation Fellow at West Monroe, to discuss some of the biggest topics facing the industry today. Laney has spent his career analyzing data and working on ways that companies can best use it to their benefit. His book, “Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage” was released in 2017 and helps companies use their data assets to achieve success.

Doug Laney. Photo: N/A Doug Laney. Photo: N/A Doug Laney. Photo: N/A

This interview has been lightly edited for brevity and clarity.

Doug, can we start please with a brief into yourself and your career, and what led you here today?

I’m currently an Innovation Fellow with the consultancy firm West Monroe, a Chicago-based firm… Prior to that, I was a senior analyst with the IT and research and advisory firm Gartner, where I was focusing on the role of the CDO and data strategy-related topics. I started at Gartner focusing on Big Data.

My first time with Gartner was during the 9/11 attacks, and some clients of ours in the Twin Towers lamented not only the loss of life but the loss of their data. We helped them quantify the value of that loss, even though data is not a balance sheet asset. Interestingly, those claims for the data they lost were denied by insurance companies because the insurance companies said that data was not considered property and therefore wasn’t covered by the property policies, which was quite an eye-opener to me: I always assumed data was an asset or was property.

I started to research it, and the reason data is not an asset is because the asset classes were defined coming out of the Great Depression in the 1930s. When we decided to standardize financial statements, we defined the asset classes. One of those asset classes was not data because at that time data was physical: it was books, manuals, manuscripts, papers, and so there was no reason to think about data or information as a separate kind of asset.

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There are still no insurers today that I know of that offer data insurance. They offer cyber insurance which will pay you a blanket fee for any kind of hacking-related incident, but they don’t pay based on the value of the data that is exposed, lost, or damaged.

Meanwhile, the rest of the world is trying to move forward and really treat data as the 5th factor of production in addition to land, labor, capital, and entrepreneurship. That got my attention and having helped those in the Twin Towers value their data, I started thinking about data as an asset: what do we do with assets? We measure them. Why do we measure them? To manage them better. We measure anything so we can manage it better. Why do we manage things? So we can get value from them and monetize them.

And so those are the three components of Infonomics: measuring, managing, and monetizing data as an actual asset. I did some research into how other assets are measured and managed and monetized and why don’t we apply those same disciplines and processes to the way we’re managing data. The other component of Infonomics is the ‘mics’ part. That is classic economic models like supply and demand, utility, pricing, elasticity, and productivity. All those models were designed with goods and services in mind and no one has ever thought about how they apply to data.

Let's go back to measuring and managing data. They say data is the new oil, and the big companies like the Facebooks, Googles, and Amazons have the data which they can monitor and manage quite easily. How can small companies compete with them in the search to monetize their data?

Amazon and Google are monetizing consumer data: searches, purchases, and that kind of thing. But within any given niche, most companies have data on the behavior of those within and throughout their extended business ecosystem. I’m working with a pharmaceutical supplier right now who understands the supply chain around the manufacturing and distribution consumption of pharmaceuticals. Google doesn’t have that, Amazon doesn’t have that - but it’s data that is particularly powerful to all those participants who are working with them to develop data products.

Back to the concept of data being the new oil: I appreciate the analogy because data is certainly driving the economy today in the way that oil grew the economy in the last century, but I think it misses the point that data has unique economic properties that oil doesn’t have. When you consume a cup of oil, it’s gone. It dissipates. You can only consume a drop of oil one way at a time, not multiple ways simultaneously.

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Data is very different: you can consume a unit of data simultaneously in a multitude of ways. When you consume a drop of oil, it doesn’t create more oil. Well, using data typically creates more data. Data is a non-rivalrous, non-depleting, and progenitive asset. And the companies winning in the economy today are the ones who are taking full advantage of those economic properties.

The Googles, the Amazons, the Facebooks, they’re finding multitudes of ways to leverage that data. By leveraging that data it’s creating more data, and that data is not going away, it’s not depleting. So they can continue to use it in multiple ways until that data becomes non-relevant anymore.

When we're working with this pharmaceutical distributor, they realize they’re making maybe a slim margin on pharmaceuticals but they can make a huge margin on the data they collect. And make that available to the stakeholders throughout their business ecosystem.

A great example and a company that has done that well is ADP - the payroll processing company. Their applications do all sorts of workforce management and payroll management. They have loads of data on workforce payroll and employment and they realized they could out-predict governments on employment numbers: sooner and faster and more accurately. They realized they could form a data business and make all these data available to customers.

For most companies, that is a vicious cycle of not measuring, so not managing effectively, and so not monetizing their data. The idea behind Infonomics is to reverse the curve, or flip the script, to get companies to start measuring and monetizing their data better.

I want to touch more on governments and how private companies can be better than the government at predicting things. These companies are huge and known to innovate - something that governments aren’t known to do. What is that relationship looking like?

I don’t really see it as competitive. The government will continue to produce authoritative open-source data and insights. Typically that data is not very predictive - it’s very hindsight oriented. And I think in today’s world, particularly with the pandemic which has broken a lot of companies' forecasting models, companies need to shift quickly from trend-based analytics to driver-based analytics. Drivers being one of the leading indicators of our businesses. What are the things that happen that predict our sales or supply chains? And the government is not typically very good at that prediction, other than maybe at a macro level. So companies are very quickly having to shift to more driver-based models than trend-based models.

Should there be some sort of legislation to maintain our privacy with this data collection?

Europe is very concerned about individual and consumer privacy. GDPR was the benchmark of data privacy legislation, and in the U.S, we’re a bit more challenged because we do things on a state-by-state basis. So California has introduced the California Consumer Privacy Act, which has been replicated in some other states but it’s one of those situations where the U.S acts as a conglomerate of entities rather than a whole. Most states have some kind of regulation, but how you enforce that in a global economy is obviously a challenge. There are 50 different states.

Privacy has done one thing - these privacy regulations have reduced the ability of organizations to monetize their consumer data and force them to be a little bit more creative. I have collected hundreds of examples of how organizations are leveraging data and analytics in innovative and high-value ways, and one of the new ones which have come up in light of the GDPR is that I can no longer sell you my customer data, but I can sell your stuff to my customers. So flipping that model is a way that you can monetize your customer data, but a lot of companies haven’t quite figured out how to do that creatively.

Can you provide an example?

Let’s say we're working at a hospital, and the hospital knows who its diabetes patients are but due to privacy regulations it cant sell that data to anyone. But, if you have a gym franchise or an exercise franchise, or healthy meal plans, or if your company offers at-home glucose monitoring test kits, I can sell your stuff to my patients and take a cut, or a referral fee, for that. So I can still monetize my customer data without ever exposing who those customers are.

Is the value in those exchanges the same?

It probably is diminished somewhat, and it requires me to establish a marketplace or a platform for products and services that I don't offer.

So, what’s the future? We seem to be living more online than ever, and yet in the last few years people are more aware of what they're tweeting, the photos they’re sharing, and suddenly seeing the consequences of some of their actions.

From a consumer perspective, the quid pro quo benefits that are received for sharing need to more significantly outweigh the expense or risks of sharing. That's something that social media companies need to be very concerned about.

But there are other scenarios where we are just quite comfortable and have been for decades. Consider the grocery store loyalty program. You go into the grocery store and you scan your loyalty card and you get a discount on your groceries. Most people don't think about what's really happening, which is that it is a barter transaction. You're exchanging information about you and your purchase for free food… It’s a transaction involving our information, information about what I am purchasing, who I am, when I’m purchasing it, and where.

On Facebook, I did an analysis about the value of Facebook's data before they went public. I analyzed the number of monthly active users they had and looked at their financials and determined that the $75 billion gap between their assets and their market value was primarily due to the value of the data that they have - or investors' expectation of Facebook's ability to monetize that data. And when I looked at that I determined that the value of a Facebook account was about $80. Now it's over $400 per account. Facebook has been able to monetize that data better.

What right do we have as the users of somewhere like Facebook to say "Hang on, that's my $80! That's me!"

There was a study done by some Austrian researchers and they asked what would you pay to retain your Facebook account. If they were going to delete it and you actually had to pay for Facebook. On average, they said they would pay $12. Facebook is generating a lot more value from our data than we feel we're giving. That's why the model works - because Facebook is generating more value than we believe we are providing.

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