Approximately 250 years ago the industrial revolution immutably remoulded our society and our views on how to create a better world worth living in. Today, another revolution is underway with potentially even further reaching consequences. It’s our shared responsibility to answer hard questions about how this data and algorithm revolution can add real value and drive positive change.
Every company is a tech-company
Now that we’ve entered the 2020’s it seems fairly safe to say this will be the decade in which it becomes common sense that every company is a tech-company. Consisting of its data, algorithms and all related activities, every company will have its own corporate digital twin. As the roaring 2020’s proceed every company will learn that its corporate digital twin turns out to become its leading driver of innovation and value creation.
The inevitability of every company becoming a tech-company brings two urgent problems to the surface which, when not addressed properly, can become blocking issues:
Problem 1: Most corporate digital twins are in fact disorganized digital strangers
In the 2010’s we witnessed increasing differences between the real world personality of a company and its corporate digital twin. Whereas the real world behavior of most companies is rooted in values and business principles, these appear to be non-existent in how data and algorithms are applied within a corporate digital twin.
This schizophrenic character of the digital self becomes even more apparent when we look at commitments made to quality, transparency and governance, designing strategies for long-term impact oriented value creation and making contributions to for instance diversity, circularity or climate change. The way most tech-companies use data and algorithms show none of the above. It is as if everything that matters in real life doesn’t matter for a tech-company’s corporate digital twin.
‘A corporate digital twin is more than just the sum of a tech-company’s data and algorithms. It also has a personality with values that define its behaviour.’
Problem 2: Data and algorithms are facing a serious trust crisis
Partly caused by the schizophrenic character of first round corporate digital twins, but primarily as the result of the complete failure in data and algorithm related ‘sense making’ in the 2010’s, data and algorithms are facing a serious trust crisis. For most companies the majority of their stakeholders, including clients, employees and shareholders, are clueless when it comes to their role and involvement with its data and algorithm related activities. Let alone, how they themselves, the company or society at large benefit(s) from these activities.
As the number of alarming headlines about the down-side of data and algorithms keeps growing every tech-company will face the same problems: talents are becoming reluctant to work with them, customer and partners are looking for better alternatives, shareholders who are genuine about investing for a better world will invest their money elsewhere and politicians will continue to make their demand for regulatory interventions louder and louder.
Not acting on the underlying dynamics will deepen the trust crisis, increase data and algorithm resentment and eventually trigger more curtailing restrictions. This will undoubtedly limit the extent to which a tech-company can benefit from its corporate digital twin, but above all it will decrease the potential value and impact that data and algorithms have to contribute to solving the key transitions our society is currently facing.
‘It’s a simple conclusion; the current trust crisis means we are witnessing data and algorithms growing from childhood to early adolescence.’
In search of a new paradigm
If we’re serious about capturing the tremendous potential that data and algorithms hold to help solve society’s key challenges, such as climate change, the circular transition or exploring lifesaving medical breakthroughs, corporate digital twins need to become more mature.
In the 2020’s the key challenge for tech-companies is to make their corporate digital twins more balanced so they are much better aligned with the real world personality of the company and the stuff that really matters in society. Ultimately this means to make sure that when it comes to designing, applying or using data and algorithms, seizing opportunities and taking responsibilities go hand in hand.
‘Becoming a tech-company and having a corporate digital twin is not something that just happens to you. It should be the result of thoughtful design and making deliberate decisions.’
This manifest explores how to reset the way we see, design and use data and algorithms. By also including how they function within our economy and society, tech-companies and their corporate digital twins become fit for purpose. The manifest identifies three priorities:
1. Rethink data: from Big to Relevant
2. Redefine algorithms: from Statistics to Engeneering
3. Refocus key priorities: from Tech to Eco-system