Monday's Musings: Decision Velocity Will Determine Winners and Losers In A Digital Age
Monday's Musings: Decision Velocity Will Determine Winners and Losers In A Digital Age
R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...
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Speed Provides Exponential Advantage
Speed has always been a critical success factor in winning wars on the battlefield. You need to move troops faster, reach targets more quickly, and strike with speed and precision. However, what is often not talked about is how the speed with which decisions are made plays a role in claiming victory. Alexander the Great’s success on the battlefield is often credited to the rapid decision-making capabilities of his armies. Enabled by trust and a decentralized command structure, his troops were able to beat their enemies by “out-decisioning” them. In most cases, his opponents had bureaucratic decision architectures, where minor decisions would travel up multiple levels of command before traveling back down to be executed. In the 330s BC, that could mean it took days to make a decision on the battlefield. Such a centralized control and detailed micro-management approach was no match for Alexander the Great’s nimble teams. British military strategist J. F. C. Fuller, writing on Alexander the Great, explained, “Time was his constant ally; he capitalized every moment, never pondered on it, and thereby achieved his ends before others had settled on their means.”1
The speed of decision making plays a similar role in the age of digital giants. Any organization that can make decisions twice as fast or one hundred times faster than its competitors will decimate them. Time is a friend to those who make can make faster, more accurate decisions. While the human brain may take minutes to make a decision and it takes hours for a decision to work through an internal organizational structure, in the digital world machines and artificial intelligence engines can make a decision in milliseconds. Whomever masters these automated decisions at high velocity will have an exponential advantage over those who don’t.
To succeed, businesses must achieve decision velocity: First you have to amass a huge number of users and collect rich data and insights about their interactions—what I call data supremacy. Then you must train artificial intelligence to recognize patterns in that data and automate decisions, processes, and tasks based on those patterns. The higher the number of users, the higher the number of interactions, the higher the amount of data, the higher the quality of insights that AI can learn from, the higher the level of automation of your decisions in your organization. The higher the level of automation of the organization’s decisions, the higher chances you’ll rule your market.
It All Starts With Quality Data - Lots of It
Data is the foundation and the first priority for every organization’s growth and development. You must find and harvest all relevant sources of data and control, if not own, the upstream raw data sources. On the downstream side, you must control access to how the data is shared, monetized, and controlled. This means identifying where the biggest pools of quality data reside and understanding how data is consumed inside the organization.
However, the battle for data is often misunderstood. Many think data supremacy is only about accumulating the greatest troves of data. But having the most data does not necessarily mean you win. This is a battle for the most insight from well- curated, highly contextual data. Quality trumps quantity. The real goal is to understand the relationships among data. You want to learn how the data interacts with each other and what patterns arise from these interactions.
Where does the raw data come from? Successful organizations mine their organizations top to bottom, harvesting data from enterprise transactional systems like their accounting systems, supply chain, operations, and performance data. Then they pair their baseline back office data with front office data that includes customer interactions from sales, marketing, service, and commerce. They also mine “machine-generated data”—log files from equipment—and external sources such as social media feeds and feedback surveys.
The next source of data organizations rely on is user-generated. Every organization gets excited whenever users provide data on their own, whether through an online resume, a social profile, a customer account for a website, payment information, location data when they “check in” to a restaurant or shop, or photos that can be used for facial recognition and image recognition. The more organizations drive engagement with their users, the richer the data sets they collect and the more opportunities they have to find insight in the data.
These insights come from correlations, associations, and relationships—their “interactions”—among all the data produced and captured. Successful organizations are masters at identifying “signal intelligence,” the meaningful patterns or trends that emerge from the cacophony of data interactions. And they use this signal intelligence to make all sorts of “precision decisions,” from how much to charge for a product, to what customers ought to be targeted for what marketing campaign, to what product should be recommended to what customers.
Thus, the combination of good analytics, automation, and AI will help organizations improve decision velocity and carry this forward the learnings throughout the enterprise
Your POV
Have you organized your enterprise to optimize for decision velocity? Ready to move from data to decisions?
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