What is the most valuable resource?
A 2014 article and podcast in the Harvard Business Review (HBR) includes the statement that “Time Is a Company’s Most Valuable Resource”. Time can only be spent once, and hence you need to think strategically on what you want to spend your time, to achieve your goals.
Thus time may be the a very valuable resource when considering how to spend your resources. Yet when it comes to value creation, there is another resource; one that you do not “spend”, but rather utilize without it disappearing: Data. Information.
Three years later, in 2017, an article at The Economist made the strong statement that “The world’s most valuable resource is no longer oil, but data. Exaggerated? I don’t think so.
Throughout my career I’ve been working with a large number of clients in many countries across the world. All of these engagements had something in common: they aimed to improve matters (whatever the organization was doing) by deploying information and information technology (IT) solutions. In fact, when talking about technology, it goes without saying that there is underlying data, because technology automates data processing logic, to support some business process in the organization. There is no IT without data.
Already when I was working on my PhD thesis on information management, the focus was on capturing business logic with the aim to automate decision-making processes, to provide some business benefits (e.g. I worked on an initiative for online offerings of care services for dementia patients and their caretakers). Why capture data, or information? In order to make decisions.
Success and failure of projects
I’ve seen projects succeed, and other projects fail. A key reason for success or failure has been the correct deployment of data and business logic automation to process this data for making the right decisions, for initiating the right actions and for triggering user intervention (a “human actor”) when the business logic determines that human intervention is required.
Some success cases were:
- The deployment of analytics techniques by a law enforcement agency to detect risks in border management. Being able to structure, interpret, process and analyze large amounts of data was the critical success factor.
- The facilitation of collaboration schemes among employees of a large nation-wide organization. The focus was sharing information (knowledge) among employees across all (dozens of) locations in the country, as well as employees working mobile.
- Tracking events in a supply chain of a manufacturer with global reach, to detect supply chain anomalies and risks. The focus was on capturing supply chain event data, analyzing this data and triggering the correct follow-up action.
But some projects fail, and I’ve seen projects fail too. I remember well a project where the functional requirements that have been defined at the beginning of the project were so limited, that when the project was delivered the users did not see the added value of the solution. Yes, they had the right data, and the specified functionality (for collecting, processing and presenting insights from the data to users) was implemented well by the IT team. But the analysis of the data was so limited, that the users felt it had not introduced any added value. Hmmm… in hindsight, this project was doomed to fail from the start.
The most valuable brands are data companies
Let’s have a look at Forbes’ list of the World’s Most Valuable Brands: https://www.forbes.com/powerful-brands/list/
As per the moment of writing this post, the top brands are (2020 ranking):
- Louis Vuitton
I would argue that the top 5 companies are all data companies (read my opinion about Apple’s CEO’s statement “we’ve never been in the data business” in my earlier blog here).
The Data Economy: How Technology Companies Monetize User Data
All these companies collect vast amounts of data about their users, and subsequently analyze this data, generate insights, and use these insights as a resource from which they can make money, most typically by using it in selling advertisements. For example, if Facebook knows that you love gardening, it will show you ads of plant shops, and you wouldn’t mind it. Facebook sells you (as audience) to the advertiser. But what if – based on analyzing your behavior and comparing it to the behavior of many other users – the algorithms present to you content that you find insulting? Or what if the algorithms decide that your opinion can easily be influenced (i.e. it is easy to manipulate you), and therefore your profile is presented with political campaigns of an advertiser who targets people who can easily be influenced?
These scenarios demonstrate how companies turn data into value, i.e. how they monetize data. They sell you as an audience to advertisers who wish to target certain audiences. Targeting is done based on analysis of all the data that the technology company has collected about you, and by comparing your profile to the profiles of many other users.
Users pay for “free” Apps with their data
Very often data is collected when users use “free services”, for example mobile Apps. Consider that Facebook collects so much information about people, that the Facebook algorithms probably understand the behavior of many Facebook users better than these people understand themselves. Facebook’s biggest asset is a massive amount of data about people worldwide. Facebook users provided all this data to Facebook voluntarily, in return for a free-of-cost service. At least, “free of cost” in the traditional, monetary sense. But Facebook, like its peers, monetizes the data about its users, and makes huge profits from this data. Users pay with their data, not with money.
As it has been said in the Netflix movie The Social Dilemma: “If you’re not paying for the product, then you are the product”. I use the term “the myth of free Apps” for the phenomenon of people using unpaid mobile Apps and thinking that they have received something for free. They are paying with their data. Of course, this phenomenon is broader than mobile Apps (it applies to other products and services), but it has reached an unprecedented magnitude in mobile technology, as billions of users daily download such “free” apps with permissions to collect user data, thereby giving access to their data to often unknown third parties who create these apps.
Monetizing Data (Information): Who Reads the T&Cs of Technology Products and Services?
Bear in mind though that user data collection happens also when you use paid services, not just “free” services. Whenever you buy an expensive iPhone, do you really read the device’s Terms & Conditions that govern which information about you Apple (the manufacturer of the iPhone, for which you paid a lot of money) can collect, and what Apple may do with this data? Don’t bother answering; the answer is NO (Apple serves here just as an example; the same rationale applies to other companies).
To sum, all these companies collect vast amounts of user data, and monetize this data.
Do you still have any doubt about data being the most valuable resource?
Don’t Accuse Tech Giants for Using Your Data
Technology companies (which can be referred to as data companies too) are most known for using data. But the phenomenon of using user data has existed at least as long as the concept of Marketing has existed. Recently these Tech giants have been subject to much scrutiny and criticism for using user data. Should they really be blamed? Doesn’t every user have the responsibility to read the Terms & Conditions of services that they consume, and make an informed decision on whether they agree with the terms of service before using the service? Probably the truth is somewhere in between. Users have their own responsibility, and companies using data have their – moral, or ethical – responsibility to be transparent about data usage. I expect that the field of ethics in IT will become more and more dominant in the upcoming years.
There is so much more to gain from using Data
The last few paragraphs focused primarily on technology companies, because they are in the spotlight for successfully monetizing data. But the discussion on the value of data is broader. First, because other industries use data too. Second, because one can create value from data in other ways than monetizing data. You can also create value from data by using it to speed up your business processes, to avoid errors (in production processes, in decision making), to detect and mitigate risks before they occur, etc. And therefore, turning data into value is applicable to any industry.
In certain industries data is being deployed extensively for these and other purposes. These are industries where traditionally IT plays a key role. Makes sense, because with IT one can collect, process and analyze data, and drive decisions based in insights created from this data. Examples are eCommerce / eBusiness (B2B, B2C and C2C) and financial services (money has been more of a digital asset than a physical one for a long time). Other industries have a lower pace of adoption. Take for examples industries where much is still done on paper (e.g. the logistics industry) or industries which traditionally have been more conservative (e.g. legal services, the construction industry or Government). There is so much more to gain, and especially in these industries that are lagging behind. I strongly believe that data is still one of most underutilized assets, for most organizations.
Action: Start creating value from data
So what are you waiting for? Go explore the opportunity to benefit from data within your organization. Be open minded, think out of the box, and do not limit your imagination to the data that you currently have. If you need certain insights which cannot be obtained from existing data, consider how to obtain this data, either from within your organization or externally. There are many external sources that can provide you the insights that you need at the time that you need them.
In a few of my earlier blogs, I provided concrete insights into how one can benefit from data. Read these articles to obtain concrete ideas for your own organization:
- Compliance in the financial sector; law enforcement (preventing money laundering and terrorist funding): https://insightsunboxed.com/science-and-magic-how-to-achieve-compliance-in-an-ever-changing-world-ziv-baida/
- How to fight modern slavery in your supply chains: https://insightsunboxed.com/using-data-to-fight-modern-slavery-in-your-supply-chains-ziv-baida/
- Risk identification in international trade: https://insightsunboxed.com/risk-identification-in-international-trade-from-data-to-science-to-insights-ziv-baida/
- Why data is the lifeline of global trade: https://insightsunboxed.com/why-data-is-the-lifeline-of-global-trade-ziv-baida/
- Big data for customs at the borders: start small, think big! https://insightsunboxed.com/big-data-for-customs-at-the-borders-start-small-think-big-ziv-baida/
Go for it. Data!
Further suggested reading:
- How to create a data-driven organization? Lessons learned.
- Data quality vs. data quantity
- Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage, by Douglas B. Laney.
Are you still doubting how to create value from data in your organization? Do not hesitate to send me a message with your question!
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