Is your data valuable? Might be. Is all data valuable? Nope. How do you construct a data strategy that identifies what is valuable quickly so you can keep the good and dump the rest?

In the second step of the DataSmart Method, the Ward brothers walk through a valuation approach to data assets designed to be done quickly. This ensures you focus on the data assets and partnerships strategies early on in your evaluation.

PODCAST

TRANSCRIPT

Jay: “Are you Data Smart?” A weekly podcast on data security, information management, and all things related to the data you have, how to protect it and maximize its value. I’m Jay Ward.

Christian: And I’m Christian Ward. And today, we’re gonna talk about the second step of the data smart method, which is valuing data assets. If you recall from our podcast number five, we talked about the first step in the data smart method, which was identifying your data asset. So, it’s sort of this know thyself-approach to data, where you break down all of your internal and external data assets, so that you can understand what data is created by your business and what data is created about or around your business. And that’s been the first step which sort of leads us into the next one.

Jay: And I think one of the things we did was talk about doing an audit, in essence. Taking a step back and just mapping out with all of the tools at your disposal what you have. And I think doing this in an audit-style approach, for me it’s been helpful with clients because it is inevitable that your clients will say, “Well, you know, I think this is, you know, I have only these discrete pieces of information.” And then when you say, “All right, well, let’s actually look at this a little bit more methodologically,” people are gonna be astonished at the amount of information that’s coming in. Internally generated data, externally generated data, there’s tons of it. And you need to be rigorous.

Christian: Yeah, I don’t think people have any clue until we sit down. As you said, looking back, something to the effect of people were like, “Well, we really don’t have that much.” And then all of a sudden they’re like, “Well, wait, you’re gonna have to come back in two weeks. We didn’t even realize we had all of this.”

Jay: “Oh, the Internet. I forgot about that. Oh, we get some stuff from the Internet”

Christian: Yeah, yeah. A lot of people talk about it.

Jay: I’ve heard about it before the series of tubes. Now, the point for me is the audit style approach, and why we recommend this to clients, is great because it gives you sort of that outsider’s perspective on what’s going on in the company, and you’ll be amazed at the amount of enumeration that comes in. And it gives you sort of a built-in advantage when you’re mapping out how you want to proceed. And I know, Christian, you have a lot of ideas and thoughts on mapping.

Christian: Well, generally speaking, I start almost every engagement with clients and partners with mind mapping software. I know this isn’t everybody’s thing. You can also use a dry erase board for this. But basically what we do is we interview every head of every division where it is clear there are data assets to be unearthed. And using my mapping software is really great because you can really quickly build out different categories, attach data assets to it, what their field types are, what their lengths are, what their, you know, protection level or security risk level are, and then that automatically exports as these beautiful spreadsheets that really breaks down those fields very well. But that’s usually what we do before we ever dive deep.

Jay: So, once we’ve got this deep dive complete and we’ve, you know, we’ve gone through the audit, the next step, and this is you know, a slightly controversial one or at least maybe not as intuitive users might think. You’ve identified the assets and now it’s time to value them.

Christian: Yeah, exactly. And the reason why it’s a little controversial is most people tend to go all through this process of setting up their strategy and then they start talking about go-to-market. The problem with that is if you can’t look at a data asset and within 30 seconds identify some level of value to it, then you really need to rethink the entire strategy. It’s too late by the time you’ve done this audit and then decided, “Hey, we’re gonna build these new products and services leveraging these data sets.” You actually have to value them much sooner in the process. And it’s easier than you may think, but it’s a critical step to be done ahead of choosing what your strategy is gonna be.

Jay: Right, because you’re gonna run into confirmation bias if you don’t. I mean, in the end, you’ve built an entire product launch and you’re like, “All right. So what is this really worth? It’s not worth the last six months of work that we spent on it.”

Christian: Yeah, you’ve just described some of the greatest failures of all time in new data products, but that is it. It’s people get excited about it and they’re like, “Wow, I didn’t even know we had the, you know, pant sizes of 10,000 jean companies.” And we’re like, “Yeah, that’s not a product yet. You need to first identify how unique is that and what’s the value?” So, one of the things about valuation is we have worked with countless bankers, corporate finance individuals, valuation experts, valuation is always subjective. It is very difficult to come to any sort of objective understanding of the value of a dataset. However, knowing that, what you can do is approach it from a little bit of a different perspective. And it’s one that honestly, we’ve just honed over a few decades of working in the industry, which is you ultimately end up with sort of buckets of value. So, things that maybe aren’t as valuable as the next. You won’t know the exact value, but you kinda know it when you see it.

Jay: Yeah, and I think the interesting thing about breaking it up into buckets is it allows you to do what the lawyers would do, which is say, “All right, so we have what we would consider the absolute value of this information. It falls into some categories. Now let’s cross-reference it against the potential risk, right?

What is the risk to me of holding on to this information? Sure, I can hold on to, let’s say, 150 million individuals’ credit scores and not really secure it because it would cost me more money to hash and to encrypt. But what is the potential risk to me if that information is accessed?” For most of us, it would be catastrophic, and that’s the thing that you have to do when you’re engaged in this process. Don’t just look at absolute values, think of the relative value, the risk-benefit, the cost benefit, because that’s how you can arrive at a thoughtful conclusion about whether or not the information you have is worth keeping and putting into a bucket.

I mean, if the information is worth not very much and the risk is astronomical, you know what you probably need to do, and you’re gonna save yourself a big problem down the line with regulators whose new watchword is data minimization. If you can’t say I need it when they ask you why you have it, you’re gonna invite more risk, you’re going to invite more liability. So, do that relative valuation.

Christian: And we’ve talked about this, Jay. I mean, that data minimization is the polar opposite of… I don’t even know how to say it, more opposite than the polar opposite. It is insanely different than what almost every data strategy is today, which is keep everything, hoard everything, because someday you may need this piece of data. And I think it’s a mindset that by design must be put in at the highest level. The executives have to start to buy into this at every major business that has data assets. And really, look, we boil it down to four simple buckets. And there’s no real secret to this, it’s literally just what we’ve seen year after year of what are the value of assets. And the four buckets are zero, so basically, data assets that basically you could charge no one for, they’re zero dollars. And we’ll dive into each bucket and all the sort of descriptors and properties on them in later podcasts.

Then there’s data that’s kind of up to about $10,000. You could charge someone up to $10,000 a year for it. So the first one is zero, the second is about $10,000, the third one is about $100,000 a year, and the last one is a $1 million or more. Now, these seem highly unscientific, and they probably are. They’re really…

Jay: It’s amazing that they’re all in factors of 10. How did that happen?

Christian: It’s because I’m a geek. But it’s mostly because it’s what I’ve found the market gravitates towards. And please, don’t misunderstand. Zero isn’t of no value. If someone’s willing to pay you nothing for your app but in return they’re willing to give you all of their data about who they’re friends with and what they socially like and what books they read, then there is a value exchange. It’s just that it is at such a low increment. Now, the beauty of zero is, you know, ask Instagram, right? But for no money at all, you can post all of your photos and follow all of your friends’ photos. And by the way, they’re then worth a $1 billion. Like zero is a powerful bucket, but you have to understand why the data asset falls into that.

Then you start getting into the 10,000 and the 100,000. And really, 10,000 tends to revolve around data assets almost like they’re more of a lifestyle. They really don’t have…you could build a business on it but not a big one. These are often the exhaust pipes of other businesses. So, where people, let’s say, have additional data about certain sectors and industries as the mere exhaust of them doing something in that sector or industry. But as soon as you break that $100,000 a year mark and start pushing into that million dollar bucket, you’ve got something truly unique that can scale and has relevancy on a global basis.

Jay: You know, it’s interesting when you talk about, you know, the runoff and the exhaust. It’s a quintessentially American way to characterize data, because data is, it’s a raw material, right? It’s a commodity that we can use and fashion to create new products and to create wealth. And that’s one reason why, you know, big data and the use of data has been such a profitable industry in this country and can really be an engine for growth for you and for your business. The one thing that I would caution you, is that when you’re doing this valuation, when you go back to that relative valuation, remember that that’s not the way regulators think about data, okay? They think of data as a component, a pixel, that eventually put in the aggregate will create a picture of a person. And that person has fundamental rights to privacy if they’re Europeans, and they have a reasonable expectation of privacy if they are an American.

So, you always have to have, in your mind, the sort of two-factor approach to valuing so that you can actually have a comprehensive and integrated approach to valuing this data that doesn’t put you at risk of having a regulator be like, “Yeah, you’ve collected a lot of information, but 90% of it you shouldn’t have anyway. So, you know, we’re gonna talk.” And that’s an important thing to remember because if you don’t do both, you’re missing out on half of the equation.

Christian: And that’s really what the buckets are about because you’ve got to be able to work in some framework of relative value. And that means time both the risks and the reward potential. It also means in your initial audit of identifying your data assets, you really have to pay attention to, what are the things that are really B2B or objective and really don’t paint that pixel, don’t really give you something about an individual or a person? Because at some point, you’re going to end up with a value matrix. It’s where you’re taking each data asset against each data risk, and then the amount of information, and then also things like, how hard is it to replace the data? If you were to forego it now because it’s a responsible thing to minimize its access, could you come back to it and harvest it later or partner with someone who has proven to have superior data protection than perhaps what you yourself as a business can provide? So, all of those great things work together to build a holistic valuation that you can then start to build your data strategies around.

Jay: Exactly. And I think if you maintain data now, it doesn’t mean that you’ll have to use it now. And if you get rid of data now, it doesn’t mean that you can never use it again. So that’s, have a long-term approach. That’s when we talk about this data smart method, we’re talking about thinking not just the next two weeks, not just when, you know, the round A is over or when your angel investors are in town next month. We’re thinking about long-term, how to build success and wealth and legal responsibility with data.

Christian: Absolutely. And lastly, in upcoming podcasts, as I said, we’ll dive into each bucket of analysis, give you examples of those types as well as the other really critical things to valuation such as coverage. So, how broad is the coverage? What is the depth of coverage that you have of any given unique data set? The freshness, the uniqueness of it, and of course, probably the most important, the actual quality of the accuracy of the data. So, we’ll dive into each of those in upcoming podcasts. Thank you so much for listening to this episode of, “Are you Data Smart?” And we will see you next time.

Jay: Thanks again.