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#99 Ten guidelines for success in the Data Economy

As the main editor of the 100 Days Data Economy series, I was given the task to collect the most important lessons from the journey in one post. To make my task a lot easier, Toni (post #96) and Jussi (post #97) wrote their own curated wisdom as takeaways in the previous posts just like me. This post collects all the most important learnings in one set.

We could have easily listed 100 things to consider, but instead, we decided to pick just the 10 most important based on our experiences from around 200+ data economy cases.

Here are the 10 guidelines for success in the data economy.

1. Start with low-hanging opportunities

The data utilization needs to start with the business use cases and the easiest ones that are almost immediately possible to implement. If your strategy or vision contains entities that are 100% data-based, make sure that top management knows and speaks for it naturally.

2. Select Tech based on business needs

Choose technologies according to your business needs, avoid technology vendor locks, and try to promote the ability to change at the business level by all means. Before you invite technicians to a discussion, make sure you have a business case and business needs defined.

3. DOn't become data hoarder

Don’t just collect data. If the data cannot be used, it is a major cost for you and the real value of the data remains just an imagination for your business. It is an indisputable fact that companies where management does not take part in or take responsibility for the design of data models, even at the highest design level, are left out of the tsunami of the data economy.

4. The customer defines the created value

Follow the old golden rule of keeping your focus on the customer's needs. It is easy to fall into a trap and "internally invent" features and benefits. It's easy: if you look inside, you get insider input. If you move your focus outside, you get fresh lucrative paying customer input. In the data economy the customer is driving the value creation which you enable.

5. Design first to avoid costly mistakes

Don't try to guess what the customer needs, interact constantly with them to nail it and improve your offering over time. Know your customer! You should always mock the solution first, then verify the value and fit with the customer. Only after that proceed to implementation!

6. Apply product thinking to your data

Data monetization requires hard work and a systematic approach. Current hot concepts like Data Mesh and Data Fabric both include data products in the core. One crucial element is to treat your data as products. Productize data internally to maximize speed and reuse capabilities. Servitize data to maximize partner network and customer value. Hire data product owner(s) and start refining a value-creating portfolio of data products and services.

7. Good enough data quality

Good data quality is the foundation of business but the responsibility for example determining good enough data quality lies primarily with the business operations, not so much with data management. Resources should not be wasted is a nominal quality work that does not have a value-creating business goal and impact.

8. Start Breaking down silos

The greatest obstacle is the same eternal even today; access to data. If data utilization has been identified as a necessary competitive advantage for today’s business, then why is it so darn difficult to access the data we need? Digital transformation should start by focusing on breaking down your data silos.

9. Transparency is the key pillar of Customer trust

Transparency is the key thing when building customer trust. People should be given control over what data is collected from them and told transparently how their data is being used and by who. Fostering trust is the new business imperative.

10. Assume personal data to be there

Currently, the data landscape is changing rapidly and we humans are involved in the process as data creators as well as consumers. Legal frameworks such as GDPR define boundaries for how to deal with personal data. More often a person can be identified from the data and thus privacy issues have arisen more and more. You should prepare processes and development in a way that you always assume personal data and privacy issues to be present next to IPR issues. Winners will have the capability to manage personal and industrial data with the same process without hiccups.


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