Why data (privacy) matters…
If you are a young startup or an SME, you cannot escape the daily headlines about AI, Intelligent Robotic Automation, Quantum Computing or Cybersecurity. But moreover, you hear the phrase “data is the new oil”, and you know oil has helped humankind to develop so much further in such a short period. So what does this mean and why does it matter?
Let’s start by getting to know some terms:
- Data subject: the entity/object you collect data of
- Data controller: controls procedures and purpose of data use
- Data processor: processing data given by data controller
There are many more terms that you’ll hear, but for now, these are enough. A simple process would be an online purchase: Customer A buys Object O at Vendor V. V collects personal information, purchase details and other relevant data from A , processes and archives them. That’s how it was until the 90s.
Today V wants more. V wants to give A an excellent experience. V stores more data to send emails about new products regularly, V wants to show on its website “what others liked too” or “bought too”. V wants to maximise its business. And that’s all good, but the data collection nowadays reaches levels beyond imagination. E-Commerce has turned into social commerce, and with the latest appeal of Walmart willing to buy TikTok, we enter the market of “algorithmic commerce” (term by Scott Galloway).
Nevertheless, B2C is only a minor subset of “data is the new oil”. And this is hard for most to understand:
Collecting as much data as possible is not an objective.
When working with data, nowadays “Big Data” is the term. But it is only the raw good. The real value is when we can generate information and get new knowledge. The reason why data volume increases is because we start tracking the environment and behaviour to predict outcomes and events better.
In IoT use cases, a massive amount of data has been created by machines transmitting their sensorial data to analyse for potential defaults or to communicate with other devices. Although it is useful to collect much data as possible here, the objectives are to predict the machines’ behaviour and improve predictive maintenance.
But why is consumer/customer information handling so much more difficult?
Well, as any enterprise collecting data from B2C, you often face the issue that your data subject is not a machine but a human being. And this human being has human right s— believe it or not.
GDPR and CCPA are just simple enhancements of these human rights in the digital world. They force enterprises to treat personal data as valuable as any other asset.
Germany digital association “Bitkom” recently announced that half of the enterprises say that GDPR has prevented innovative projects. And that shows how few people have an understanding of “data is the new oil”.
Imagine you collect as much data from your customers as possible: age, gender, shopping behaviour, locations, balance… but you are only selling a daily consumable. You don’t need to know your customer, you know them already. You need to know your distribution channels and trends in different areas.
If you know your objective, GDPR and CCPA are easy to follow.
So many big companies try to collect as much data as possible to enhance their products. But does everyone need to do so? Well, no!
Imagine Netflix. Successful company. Most of us have an active subscription. Let’s have a look the kind of data they collect from users:
- Personal data for billing purposes
- What series we watch and where we last stopped
That’s amazing because we always think Netflix is also one of these “big data” companies. And in fact, they are, but they are running so well because they bring data together: data of users and data of their assets.
Netflix recommendation engine works not by just showing A who watched show B and C also likes show D, so you like it too. It works by trying to understand why A like show B and C. B and C might be action thrillers or contain a specific actor/actress. You need to learn first to define your objective and then to find the right data you need to collect. Netflix could easily store raw personal-related (easy to delete) data and pseudonymize them for their recommender systems. The data controller simply knows what he does and has a clear objective. And knowing exactly what you need, enables you to expand vertically in your supply chain.
It is not important to process the information that Jen, 26, from New York likes watching Brooklyn Nine-Nine and Modern Family. Your input shall be that one young female from New York state likes American sitcoms with equal amount of main actors and main actresses.
Why startups and SME often fail…
It’s as simple as that most of them lack of IT knowledge and the understanding about the actual value of data. When companies grow, they start to digitalise more and more business processes, but they often don’t have a Data Governance or an overall picture of data. But it could be that simple if you follow these principals:
- Start collecting only data you need with the consent of your users
- Have an overall picture of which teams use which data and collect which data for which reason
- Choosing a software should always also depend on what information it does collect and how it processes it
- When you avoid data silos, you have not only lower costs and better quality data, but privacy is even easier to protect
- Bring your data with customer data together, not request more of your customer
- Invest in data governance
- Be aware who has access to which data and find a balance of restriction and ease of request accessing
- Design a concept where you can maximise your knowledge by the least amount of personal data
- Have a transparent flow of data
So why does data (privacy) matter?
In short, data allows you to get deeper insights and predictability, and data privacy is the missing link between IT, Cybersecurity and the operational business. In future, this will also have a much more significant impact on your image, if you ignore it.
