Data science: 3 scenarios CIOs could see in 2030

How will businesses use data to solve business problems a decade from now – and beyond? Consider these bold data science scenarios and suggestions on how to prepare for them
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data science in 2030

Executives in the future might look back at today as the dark ages of data science - showing promise, yet very primitive.

Today, we have zettabytes of data, the power of AI for insights, and access to visual storytelling. Yet technology innovation is on an exponential trajectory. Bill Gates famously said that we always overestimate the change that will occur in the next two years and underestimate the change in the next ten.

Let’s look at some bold (perhaps even outlandish sounding) predictions to explore what the world might look like in the 2030s. This article will transport you a decade into the future to reimagine how your organization could use data to make decisions.

[ How can decision intelligence improve business decision-making? Read The missing link in many data science projects: Decision intelligence. ]

Three steps to solving business challenges

There are three standard steps to navigate from business problems to actionable decisions using data. Let’s use a hypothetical example to compare how these steps are done today versus the 2030s and explore how you can prepare for this future.

Suppose you’re the technology head of an American retail conglomerate with a chain of stores and a robust online presence. You’re facing the problem of customer churn.

How would you enable your business leaders to arrive at the right decisions to retain customers?

1. Gather the data

The first step is to collect data to understand the business problem and contextualize it.

Today: Data discovery tools

There’s a lot of data about your retail customers, both within the enterprise and from public sources. Teams pull them into central data warehouses or data lakes. Then they use tools to discover data and prepare them for analysis. This works, but it is cumbersome and time-consuming.

The 2030s: Brain-computer interfaces

In the future, a data fabric will emerge to bring together enterprise and public data seamlessly. Data will be highly decentralized, automated, real-time, and trivial to discover. It will be something like looking up your colleague’s email address.

In the future, you won’t need any tools to dip into your data. Thanks to brain-computer interfaces, your brain can be plugged into the network at will.

Importantly, you won’t need any tools to dip into your data. Thanks to brain-computer interfaces, your brain can be plugged into the network at will. Data harvested from human memories will enrich signals. Remember, your retail customers are a part of this neural collective too – you can tap into deeper signals about your churning customers.

How CIOs/CDOs can prepare 

Data governance will continue to be the foundation of data-driven decision-making. CIOs must plan for an enterprise data fabric that builds upon their existing data footprint. They must future-proof it to support the need for small, distributed, real-time data.

CIOs must enable easier discovery and use of data by experimenting with advances in upcoming trends such as wearables, mixed reality, and brain-machine interfaces. Privacy, ethics, and security will become even more critical, particularly when you are plugged into the brain with access to human memories.

2. Analyze for insights

The second step is to apply analytics and identify actionable insights from the gathered data.

Today: Artificial Intelligence

To find out what causes customer churn and how you can address it, there are many techniques at your disposal. Simple machine learning or AI-driven simulations can model hundreds of scenarios to tell you what could happen in each. You then roll out personalized discounts to this hyper-targeted set of customers.

[ Need to speak artificial intelligence? Download our Cheat sheet: AI glossary. ]

The 2030s: Collective augmented intelligence

In the next decade, computing will reach exponential heights at a quantum scale. However, the biggest game-changer will be the addition of human brainpower into the computing mix. An intermeshed network of humans and intelligent machines will emerge to unleash the power of collective augmented intelligence.

This neural computing hive will make it a child’s play to blend the deepest model insights with the subtlest business context. The recommendations to control churn will be magically effective, though non-intuitive … and yes, still not very explainable!

How CIOs/CDOs can prepare

How can you implement collective intelligence in your enterprise? To build a supermind of humans and machines, Gianni Giacomelli of the MIT Center for Collective Intelligence recommends a four-step process:

  • Start by identifying the best minds and machines within your organization.
  • Improve this network by connecting, incentivizing, and engaging these nodes.
  • Supercharge the network by building information-feeding processes.
  • Finally, promote a collaborative environment for collective problem-solving on the platform.

As an early prototype, Takeda Pharma used this approach to design CareNet, a collective intelligence system that helped tackle depression in Japan.

3. Story-tell recommendations

With actionable insights identified, the third and final step is to validate the potential decisions and build consensus among stakeholders.

Today: Visual data stories

Visual storytelling of data insights not only promotes understanding among stakeholders but moves them to action. In our retail example, your story uses data visualizations to present the personalized discounts you plan to roll out. The narrative weaves in the ROI from retained customers and helps you secure your marketing budget.

The 2030s: Neural impulses

Visual stories work best when we scan using our eyes and transfer them to the brain. But when you tell stories inside the human brain, will they still be visual? Shouldn’t we use a more native mode of communication – say, neural impulses?

When you tell stories inside the human brain, will they still be visual?

Information design will evolve to adapt to the brain-computer interface. When you have a connected hive of human minds (remember the movie "Avatar"?), your recommendations to prevent churn will be presented as a form of neural signals, a new language we will decode. You think up decisions, and the group instantly understands them and nods in agreement!

How CIOs/CDOs can prepare

The market already has EEG-based devices (electroencephalography) that help us control apps and physical devices. While the early applications are in healthcare and games, enterprise use cases are emerging.

Neural implants could unlock two-way communication in the brain. CIOs must experiment with pilots to explore such neural interfaces and evaluate early integration with business applications.

Brace for impact

There is a strong undercurrent in the futuristic scenario we reviewed. What is this biggest shift you must prepare for?

The complete cycle from business problems-to-decisions may no longer play out in the physical world. All of this could shift (back) inside our brain – thanks to brain-computer interfaces, collective augmented intelligence, and neural impulses.

Not every organization will manage to leverage such radical advances in decision-making. However, those that do will enjoy an outsized competitive advantage. All it takes to get there is an open culture to study trends, innovation through small pilots, and a willingness to embrace change continuously.

What do you think – pure hallucination, a stretch of the imagination, or a real possibility?

Note: A version of this article first appeared in my newsletter, Our data-driven future, which covers the business application of the biggest data trends for executives.

[ Get exercises and approaches that make disparate teams stronger. Read the digital transformation ebook: Transformation Takes Practice. ]

Ganes Kesari is an entrepreneur, AI thought leader, author, and TEDx speaker. He co-founded Gramener, where he heads Data Science Advisory and Innovation. He advises executives of large organizations on data-driven decision making.