- 9th May 2019
- Posted by: Manolis
- Category: algorithms, Artificial intelligence, Big data, machine learning
In 2018, 32 companies that leverage artificial intelligence (AI) for their core business surpassed the $1 billion valuation mark, according to research firm CB Insights. These companies use machine learning or build solutions that support AI implementation in healthcare, autonomous vehicles, cybersecurity, robotics, and more.
These so-called unicorn AI companies have diverse business models and industries, but there’s a strong thread running through all of these rising stars. The same thread also runs through those organizations considered the world’s most innovative and valuable brands. Yes, we’re talking about famous tech giants like Amazon, Alphabet, Microsoft, Netflix, Salesforce, Samsung, and Tencent. What’s the thread?
The rising stars and the tech giants all have developed mastery at the intersection where big data meets AI.
This convergence of big data and Artificial Intelligence is what the MIT Sloan Management Review called “the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities.” These organizations understand how to combine data savvy and strong AI capabilities into strongly differentiated solutions with massive market value. Here are a few ways data and AI empower each other:
Big data, the massive data collections that we’re all contributing to every day, is only getting bigger. It’s estimated that by 2020, every person on earth will generate 1.7 MB of data every second, according to DOMO. Within that data, if we know how to unlock it, lies the potential to build amazing new businesses and solve some of the world’s greatest challenges.
Data is the fuel that powers AI, and large data sets make it possible for machine learning applications (machine learning is a branch of AI) to learn independently and rapidly. The abundance of data we collect supplies our AIs with the examples they need to identify differences, increase their pattern recognition capabilities, and see the fine details within the patterns.
AI enables us to make sense of massive data sets, as well as unstructured data that doesn’t fit neatly into database rows and columns. AI is helping organizations create new insights from data that was formerly locked away in emails, presentations, videos, and images.
Databases are becoming increasingly versatile and powerful. In addition to traditional relational databases, we now have powerful graph databases that are more capable of connecting data points and uncovering relationships, as well as databases that specialize in document management.
How to become a data-driven company
What does the journey from data newbie to data ninja look like? The types of data analytics are often divided into four categories, as shown below. Starting from the left, descriptive analytics is the most basic category, while prescriptive analytics on the far right is the most powerful and advanced.
While most organizations use a combination of these types of analytics, you can bet that the unicorns and tech giants we’ve discussed have a firm grasp of predictive and prescriptive analytics. As you add more advanced analytics capabilities, your organization also gains:
- A better understanding of current and future performance
- Deeper knowledge of customer behaviors
- Important key performance indicators (KPIs) for decision-making
- Greater capacity to experiment, learn, and improve
- An actionable advantage over less sophisticated competitors
Power up your data with AI and machine learning
The competitive advantage of advanced analytics can be multiplied by building a strong AI capability. There is far more data being generated today than humans can analyze in any meaningful way. Techniques like machine learning, predictive analytics, and data visualization can help us find meaning by digging deeper into large data sets and improving the speed and accuracy of decision-making.
How important is leveraging AI and advanced analytics in driving value creation and future growth? In your organization’s transformation journey, it can mean the difference between success and failure. Some practical applications of analytics and AI include:
- Tracking and forecasting relevant exponential technology trends. Doing this proactively will help you determine how and when to take action, to make better decisions, and stay ahead of competitors.
- Using predictive analytics to minimize decisions based on intuition or outdated models. This is what analytics sage Avinash Kaushik calls the HiPPO effect: relying on the Highest Paid Person’s Opinion instead of relevant data.
- The ability to benchmark and track the progress and speed of individual innovation projects through development phases, and predict future outcomes and revenues.
The importance of being able to convene around a single set of shared objectives and purpose—what we often call the North Star—cannot be overstated. We’re all on a journey to the future, and organizations that take that journey as a motivated group with a shared purpose will travel faster and further than their peers.