The Machine Economy Is Here – The Digital Transformation Era Is Over

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Read more about author Heine Krog Iversen.

The age of digital transformation is over.

It’s too late to be debating whether you should digitally transform your organization. The world has already digitally transformed. Everything is already digital-first, totally connected, in the cloud, and powered by data, everywhere, all the time. 

Digital-first organizations won. Everyone else missed opportunities to innovate, made costly mistakes, and failed to survive. Many more organizations will fail to survive the next great shift that’s already happening in the world.

The Machine Economy Is Here 

If the last great shift was about “going digital,” the next great shift is about “getting smart.” 

Everything from light bulbs to driverless trucks to entire cities is becoming smart, autonomous, and interconnected. 

To understand the enormity of this shift, here are three statistics you need to be aware of: 

1. As stated in this article dated Aug. 10, 2021 in Forbes by Wind River and referencing a PwC estimate, “AI, robotics, automation, and autonomous machines will drive 70% of GDP growth between now and 2030 globally.”   

2. And by 2030, AI will contribute an estimated $15.7 trillion to the global economy, more than the current output of China and India combined. 

3. In addition, as mentioned in the aforementioned Forbes article, 62% of business leaders are putting plans in place to succeed in a world filled with smart automation and connected machines – 16% are already investing and performing strongly. 

It’s now clear that decision-making AI and machines will be the primary driver of economic growth over the next decade in what’s being referred to as the “Machine Economy.”

If you were late to the digital transformation game in the last decade, you will likely miss out again, unless you start taking immediate action to build a foundation for success. 

Data Is the Lifeblood of the Machine Economy

Organizations are already creating all kinds of intelligent machines, from AI-powered software and self-service assistants to smart IoT sensors and connected, autonomous vehicles. 

The smarter these machines get, the more they can do for us, especially when they start engaging with each other autonomously to carry out production and distribution, without the need for human intervention. 

However, none of this is possible without data.  

Not only will the Machine Economy depend on data to operate, but all these smart applications, machines, and connected devices will continue generating exponentially increasing amounts of data. The last decade was a fierce data arms race, and that will be even more true in the Machine Economy as data continues to rapidly increase in both volume and value. 

Data Stragglers Will Fall Even Farther Behind 

Data volumes are exploding, but expectations for how fast data should be curated, prepared, and delivered for analytics and AI/machine learning haven’t changed. 

Many data teams are struggling to keep up, which has led to some sobering statistics: 

1. Data scientists still report spending around 45% of their time just on data preparation tasks. 

2. Twenty percent of business experts have had to guess when making an important business decision because they couldn’t get the data they needed. 

Here’s the most sobering statistic of them all: 

3. Approximately 50% of today’s S&P 500 firms will be replaced over the next 10 years as the Machine Economy picks up steam. 

The newcomers that are taking their place all have one thing in common: They have fast access to reliable data that they use to drive innovation, efficiency, and growth. 

Data-Empowered Organizations Will Win (Again) 

Over the last decade, the most successful organizations were the ones with the best data. 

According to McKinsey Global Institute, data-empowered organizations were not only 23 times more likely to acquire customers, they were also 6.5 times more likely to retain customers, and 18.8 times more likely to be profitable. 

Data-empowered organizations gained huge advantages over their competitors, because they were able to quickly: 

  • Spot industry trends and new business opportunities 
  • Anticipate customer needs and create better products 
  • Optimize productivity, performance, and resource allocation 

Data analytics capabilities are now table stakes – the basic cost of doing business – regardless of your industry or company size. Every company is now a data company. Now, we must all start preparing to use data in entirely new ways to power intelligent machines, automate manual tasks, and multiply our capacity to produce value for our customers.  

Data Teams Face Daunting Challenges in the Machine Economy

Today, both humans and machines need fast access to reliable data in order to make informed decisions. The first step in the process of delivering reliable data is to extract it from a wide variety of sources (databases, CRM and ERP software, social media platforms, APIs, IoT devices, etc.). 

Once these data silos have been broken down, all that data must be consolidated into a central location, cleaned up, and prepared for analytics and AI/machine learning. 

This process of data consolidation, cleansing, transformation, and rationalization is typically accomplished using three primary components: 

  • Data lake: Where you ingest and store all your raw data. The data lake can be used by data scientists for advanced analytics purposes using AI and machine learning. 
  • Data warehouse: Used to store curated, cleansed, and transformed data for business analysis and intelligence purposes. 
  • Data marts/products: Provides business experts with a subset of data based on their specific domain or use case (customer analysis, sales analysis, finance, etc.), without overwhelming them with a huge data warehouse that contains all reportable data. 

We refer to this modern infrastructure as the “data estate.” 

Note: This is part one of my two-part series. In my next article, I will discuss more about data estates as they relate to the machine economy, how low code plays a significant part in helping companies move forward, and what this all means regarding traditional data management.

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