A few words at the beginning about data strategy and its relation to business strategy. Developing data products and services (DaaS) is always part of a bigger game called your business strategy. It is guided with data strategy, but still, it is a subset of your overall business strategy. How to make sustainable data-driven business? I'll give you 4 cornerstones to think about and build upon.
Data literacy
Let's begin with the item which is going to be the hardest part. In a recent Harvard Business Review article data literacy is "the ability to parse and organize complex data, interpret and summarize information, develop predictions, or appreciate the ethical implications of algorithms."
According to Gartner by 2023, data literacy will become an explicit and necessary driver of business value. Data literacy is included in over 80% of data and analytics strategies and change management programs.
Needless to say that it goes beyond just being able to do traditional statistics. This is the mistake I hear even experienced C-level professionals make. Don't think the ability to do statistics and data literacy are equal. Data literacy goes far beyond that.
This is human resources related and often requires months if not years to train your staff to be data literate. It is said that data literacy is one of the key skills for the majority of your staff in the future. Should I then train all my staff to be experts in data literacy? Probably not. But make sure that business developers, salespeople, and the rest of the management at least have the necessary skill set in data processing.
Data Business & management talent
The second most difficult thing to solve is to find business talent for data monetization. It requires productisement skills since some of the data value chain commodities resemble products. We all know that it's hard to find really talented product managers.
A couple of seasoned AI and data economy professionals wrote a marvelous article about the rising need for data product managers. According to them "one major reason that we observe with increasing frequency is the lack of an AI product mindset". Traditional product managers handling digital products and services need to understand the unique requirements of data products and how for example machine learning features are developed as they will to some extent be part of any digital product in the future. The data product managers must also understand the data value chains and data supply chain connected data management. It's a completely different ball game.
In the relation between data and ML, the authors mentioned above are referring to the rising form of Data commodity labeled as Data as a Service. This is key concept of data economy and will be discussed multiple times in the series.
Fitting technology
Some people falsely start their thinking from technology and wonder if things can be done; does technology offer solutions for our needs. So far I have never found technology to be the limiting thing. It's always the business models or ideas that suck.
Of course, you can make bad judgment calls in technology selection and end up refactoring everything again. Don't start from technology point of view. Don't let the preferences of some developer or hype cycle define which technology to use. Technology is selected for the purpose - how do we reach our goal, provide value for the customers, validate our ideas efficiently, and make solution sustainable. Business needs drive the selection.
Technology is the least of your problems.
Ethical & legal framework
The fourth element is hardly ever considered by traditional business directors. When you enter data business, the legal framework is more complex than in product business. Same kind of data in different contexts is sometimes considered personal data and in another context not. The rising data nationalism in dozens of countries is erecting new borders and limitations on data usage and export. The ownership is less relevant on data economy than in goods driven economy. Instead we are more interested about controlling rights to define restrictions to reuse and reselling among other things. My advice is to build your data product portfolio and development s that it can handle personal data by default. That is the corner of data economy you just need to cope with.
Another aspect to take into account is ethical aspect. In data economy old school greedy approaches like we have witnessed in the platform economy are now challenged with requirement of ethical use of our data.
