Big Data & the Data Economy for Mobile Apps

Five companies we all know are deeply embedded in the Big Data Economy – Apple, Amazon, Alphabet, Microsoft, and Facebook. By market valuation, these are among the largest companies in the world. Almost everyone “interacts” with three of them every day, maybe all five. And all five of them take advantage of the data they collect to create revenue. The question now is whether your business can expand your revenue channels by monetizing your data as these giants do? You may find your business can profit more from monetizing your data than from your traditional product or service.

What is Big Data?

Big Data, to put it simply, involves collecting and managing huge data sets, then using computer processing to identify trends and predict behavior. Done right, it can provide actionable information almost in real-time. Big data involves profiting from the capture, storage, analysis, search, update, transfer and sharing of it while maintaining the privacy of the data source.

The value of data is increasing as the world captures greater amounts of data in real-time via mobile, wearable and IOT devices. The data these devices can collect is limited only to what their sensors and software can detect. All are capable of generating continuous streams of data. Cognitive systems using machine learning or AI sift through all that data exponentially faster than your business analyst can do it. The data can then be organized for meaningful use, actionable in real-time.

For some perspective, the following projects the number of connected devices and total daily global data production, as reflected by the International Telecommunications Union:

• 2012: 2.5 Zettabytes
• 2016: 18 billion connected devices with 16 Zettabytes generated daily.
• 2020: 31 billion connected devices with 44 Zettabytes generated daily.
• 2024: 62 billion connected devices with 160 Zettabytes

1 zettabyte = 1,000,000,000,000,000,000 bytes or 1,000,000 Terabytes.
The Internet as we knew it in 2009 amounted to half of one Zettabyte.

What is the Data Economy and What Does it Mean for Business?

The Data Economy is chiefly about the acquisition and sharing, the buying and selling, of data – to augment or even replace revenue from traditional products and services. Tech media talks about Big Data like it’s been around forever, underscoring the impact it has had in just a few years. In 2013, the value of someone’s data amounted to maybe $2.00. But, a 2017 assessment by Trustwave estimated the average value of personally identifiable information (PII) at over $1,000 per person. (Imagine if that doubling rate continues!)

However, the Data Economy is not limited to information about people. Cargill, the largest privately held company in the United States and an international food conglomerate, is an active investor and innovator in the realm of Big Data. They’ve invested in Big Data programs for shrimp farmers, livestock, and agriculture. Their systems incorporate drones, IOT, and geo-located social media to provide actionable information to farmers.

To put it another way, what you know about your customers, industry, and area of operations can be monetized. In some cases, this data can have even greater value and generate more revenue than your current products and services. It is very important to note that nearly all data must be anonymized, but we will get to that shortly.

What data is relevant?

In the Data Economy, virtually any data that might be valuable to a business is relevant. The more relevant the data is to a particular business, the more they are likely to pay for it. If you can find a business using a certain set of data, the data has a value. It is worth pointing out that there are really two types of data involved:

Case 1: Detection: An app detects a different “user behavior or condition” and communicates that to a third party. They can seek permissions from that user to market a “solution” (product or service) to them.

Case 2: Collection: An app collects and sells aggregated data to interested parties. This runs from the obvious, like user purchasing patterns and health or medical data, to the growth rates of vegetation in specific agricultural conditions (soil, weather, fertilizer, etc.).

Data segmentation is also a key relevancy issue. Data collected from 50,000 in a specific profession or industry could have greater value than the data for a million people with unknown jobs. Data for people who travel frequently will have greater value than those who don’t, especially to travel and tourism agencies, rental car services, hotels and companies like Airbnb or even Live Nickson.

Big Data from Mobile vs IOT

What’s the BIG Difference between Mobile and IOT devices? Many US consumers spend 5 hours on their mobile devices per day, checking it up to 150 times. Most aren’t checking in on their smart thermostat 150 times a day. As much as 95% of the raw data collected by connected (typically IOT and wearable) devices is useless by itself. The value of this data comes from refining it to identify trends and patterns.

That’s as good a basis as any in defining mobile as an active tracking system – for the collection of data, but also detecting changes in user behavior and conditions. Mobile versions of your product can be used to attract more attention from users translating to more actionable data, more frequently. Users carry their mobile devices with them nearly all of the time.  If you have a travel or weather-related app, your anonymized and aggregated GPS data can be monetized on its own.

Who is Interested in Buying Data?

At the enterprise level, almost everyone across every imaginable industry buys data. For starters, there are several Big Data Exchanges which enable businesses to buy, sell or auction, data directly online. Of these, the China-based Global Big Data Exchange (GBDEx) is particularly noteworthy. In 2015, Foxconn of Taiwan, a company with 1.3 million employees, invested $8 million for a 21% share in GBDEx. Last year, GBDEx reached a partnership agreement with the Chicago Market Exchange ($3 billion in annual revenue). While obscure, major players are finding buyers and sellers in these Big Data Exchanges.

We can start with advertising companies. The more relevant an ad is, the greater it costs. Ever notice how after you get one credit card you get offers for others? Banks, credit card companies, consumer credit score companies, insurance companies are in the market for people’s financial data and activities.

Equifax, Experian, and TransUnion are required to give everyone a free credit report once each year. Beyond that, however, they charge a $15.95 to $19.95 monthly subscription for their credit reporting and tracking services to individual consumers. CreditKarma, however, provides those services for free to 80 million smart consumers. CreditKarma’s is paid for lead generation by making use of Big Data to match consumers with their most suitable financial advertisers.

But, the market for Big Data extends much, much further. Our client, Swiftmile, an on-demand eBike sharing system deployed at some of the most innovative companies in the world, is a good case in point. They’ve partnered with local restaurants to provide highly targeted in-app advertising to their eBikers – based upon the routes they take. In a somewhat similar case, Toyota having produced millions of cars, all with GPS systems, sells traffic data to urban planners and corporate delivery departments.

How Much Data Does a Business Need to Make Money?

Unfortunately, there’s no clear-cut answer to this question, but we can identify some benchmarks. It requires defining the value of your data and that’s a complex exercise beyond the scope of this article. However, you can start by comparing the rates for data similar to your own on the different Data Exchanges. This provides a baseline for the value of your raw data. Then, the question is whether you can package your data into a product or service of its own for more targeted B2B purposes.

In general, if your data concerns individuals, you’ll need to aggregate more data for monetization purposes. If it concerns businesses, you’ll need less.

And you don’t need to create your business using a data business model. Upmetrics started as a traditional business.  Upmetrics converted to the data economy by working with a few thousand schools, afterschool programs, and foundations by helping match them with funding, especially as involves sports.

Why Should I Convert to a Data Business Model?

Adding Data to your business model is not an “all or nothing” affair. For mobile applications, there are at least 13 distinct monetization options that you can put to use – in almost any combination.

Many business service applications charge subscriptions but they could significantly increase their user base by going freemium. This does not preclude maintaining an in-app store, selling vanity items, virtual currencies, newsletter marketing, business gamification, partnership arrangements, customer loyalty programs or white label options. Adding Data to your business monetization doesn’t preclude using all of them running in conjunction with an on-demand business model.

Many businesses and even some enterprises are not making the effort to monetize their available data. The relatively low amount of effort compared to the long-term commercial value of the knowledge and insights you can generate deserves serious examination.

When should I convert?

The first thing to do is find other businesses interested in buying your data and to check out the data exchanges. This is your due diligence to do and no one else can do it for you. Your likely transition point will come at some point after you are generating as much revenue from data as you are from subscriptions. Again, there’s no preclusion from charging a subscription and selling data. You are simply likely to substantially expand your user base by eliminating or reducing your dependency on subscription-based revenue.

Observation and Compliance with GDPR

The European Union implemented the General Data Protection Regulation (GDPR) in May of 2018. It requires that all companies who do business with citizens of the European Union to protect people’s personal data. GDPR applies to you if you engage in any economic activity that may involve the personal data of EU residents.  And GDPR applies regardless of where you live or do business. The full text of GDPR can be found at GDPR-Info.eu and provides easy navigation and search utility. We address GDPR in greater detail in 5 Steps to GDPR Compliance and Ongoing GDPR Compliance: Tools, Training, and Automation.

Apple and Google Data Collection Limitations

The recent Facebook and Cambridge Analytics scandal, years of hacking and new laws like the EU’s General Data Protection Regulation (GDPR) have brought renewed scrutiny on how businesses handle user data. Google Play and the App Store have limitations and restrictions on the data that can be collected to be allowed in their stores.

• Google Play User Data Policies and Best Practices to Avoid Selling PII
• Apple’s Store Guidelines – See: Section 5: Legal

Referring to Apple’s Guidelines:

5.1.5 Location Services
Use Location services in your app only when it is directly relevant to the features and services provided by the app. Location-based APIs shouldn’t be used to provide emergency services or autonomous control over vehicles, aircraft, and other devices, except for small devices such as lightweight drones and toys, or remote control car alarm systems, etc. Ensure that you notify and obtain consent before collecting, transmitting, or using location data. If your app uses location services, be sure to explain the purpose of your app; refer to the Human Interface Guidelines for best practices in doing so.

While collecting GPS data requires a good explanation to be allowed on the App Store, it is natural and inherent to the function of a travel or weather application. And many apps have been banned for what in Apple’s eyes constitutes improper use of PII.  For starters, have a good reason for the collection and/or sharing of data.  The data collected must be relevant to your app’s function.  The reason for collecting data should provide obvious, indisputable value to the user.   Apple’s likely to be reasonable if you meet these points.

Anonymity of Data

In further support of the basis of the GDPR and app store data collection limitations is the need for all data that is to be gathered (and sold) to be anonymized. This typically involves removing any personally identifiable information from any data sets that may be used and/or encrypting the data so it cannot be used to identify individuals. The GDPR articles referenced previously address this requirement in greater detail.

If better monetization of your data looks appealing to you, you will want to automate the means of anonymizing your data, as well as giving users the means to control how their data is used, including the “right to be forgotten.” In this regard, we offer an SaaS solution, GDPR by Design through our parent company, Provectus. It is based on distributed key management that encrypts Personally Identifiable Information.  GDPR by Design provides users with complete control over their data for purposes going well beyond GDPR compliance.