If there is one thing the last year has shown me in my global travels and work, it’s this: now is the time for CDOs to determine their grand vision for artificial intelligence and machine learning and how it will shape their business strategy. Why? Because the robots are not coming. Right now, businesses are already leveraging the cognitive abilities of self-learning and self-correcting, software-driven robots. Their arrival is not a few years or months down the road. They are already in the workplace and in our lives. For businesses to compete, now is the critical moment to both explore how AI and machine learning can take their business to the next digital level and build a plan of action.
Last week, I had the pleasure of meeting with the Podium Data Executive Advisory Council (EAC). The EAC is comprised of a group of senior executives and advisors from TD Bank, Cigna, the International Institute for Analytics (IIA) and several others. These companies are on the leading edge of transforming their businesses with agile analytics and self-serve data marketplaces. One attendee described the group as “trailblazers”; I could not agree more.
Yet they are not trailblazers because they are leading their peers in accelerating analytics and democratizing data (which they are), or that they are successfully putting big data technology into production (which many firms are not.) They are trailblazers because they have developed a completely new way to turn their data into a high-value business asset, which is incredibly disruptive to the status quo. They are in truly unchartered territory – where traditional organizational boundaries are being breached, long-standing customs and practices are changing, and well-accepted roles and responsibilities are getting redefined.
In the 1930s, a combination of severe drought, over-plowing, wind erosion and unsustainable farming methods led to the massive dust storms—known as "black blizzards" or "dusters"—that defined the Dust Bowl. Congress authorized the Federal Crop Insurance Corporation (FCIC) as part of President Roosevelt’s New Deal to help sustain the agricultural sector by providing farmers with crop insurance.
Today, the FCIC is maintained under the auspices of the U.S. Department of Agriculture Risk Management Agency (USDA-RMA), and CIO Chad Sheridan acknowledges the historical legacy of the agency while working diligently to digitally transform for the future.
It’s safe to state that digital innovation has already paved the way for everyday technologies, such as the laptop computers, the Internet, mobile phones and tablets, social media and cloud computing, and it keeps bringing more new technologies, such as the most recent arrivals that include 3D printing, immersive computing, virtual reality (VR) and augmented reality (AR), while Artificial Intelligence (AI) and robotics are beginning to reach their long-promised and over-hyped potential. Whenever it is stated that, from a technological perspective, such innovations are quite disruptive, it must be understood that the way digital innovation has transformed the way companies do business and the way is influencing every aspect of human activity is a true and ongoing revolution. The new digital world is drastically changing how people live, communicate, manufacture, produce, learn, collaborate and make decisions, even the most critical ones, in a way that is breaking down traditional barriers to entering markets, such as access to capital for innovation, distribution, customers, information and talent.
Digital transformation is a key initiative for many CIOs these days, but how do you know if your digital efforts are working?
The idea of selling goods directly to consumers on television may seem quaint in the age of ecommerce, but not to QVC, which today provides televised home shopping to more than 350 million households in seven different countries. It may seem quaint, but it is in fact a sophisticated data-driven operation that leverages real-time streaming analytics to help it make decisions that it can act upon within moments.
Since 1986, QVC has been a fixture on American cable, broadcast TV and satellite TV networks, competing with rival Home Shopping Network to sell goods via television. It found its home on the internet with QVC.com in 1996, just two years after the launch of Amazon.com. The company has always been driven by real-time data.
On Sunday, April 9, 2017, passenger David Dao was forcibly removed from United Airlines flight 3411 after being randomly selected because the flight was overbooked. Videos of the incident went viral, causing immediate backlash. The public response was swift and voluminous: according to Google Trends, the number of Google searches for the term “United Airlines” spiked to a ten-fold level relative to normal.
In addition to the potential brand impact, this incident may have a substantial economic impact on United itself and on the entire airline industry: just one day after settling with the passenger for an undisclosed amount, on April 28 United CEO Oscar Munoz issued a letter to customers apologizing for the incident and indicating that United will increase incentives up to $10,000 for voluntary rebooking. Given that more than half a million passengers were bumped from U.S. airline flights in 2015, costs could skyrocket if the industry follows United’s example.
In “Big data – a road map for smarter data,” I describe a set of machine learning architectures that will provide advanced capabilities to include image, handwriting, video, and speech recognition, natural language processing and object recognition. There is no perfect deep learning network that will solve all your business problems. Hopefully, the below table with the accommodating descriptive outline will provide you insights towards the best fit for purpose framework for your business problem.
The below figure, Deep Learning Frameworks, summarizes most of the popular open source deep network repositories in GitHub. The ranking is based on the number of stars awarded by developers in GitHub. The numbers were compiled at the beginning of May of 2017.
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The modern enterprise is data-driven. The capability to quickly access and act upon information has become a key competitive advantage. But business data is often siloed and fragmented. To gain a competitive edge from your information, you need a single view of your data.
Most organizations today have a complicated process for managing their data, one that usually involves multiple data sources of variable structure, ingestion and transformation, loading into an operation database and supporting the business applications that need the data. Analytics, business intelligence (BI) and reporting tools require access to the data, which frequently requires a separate data warehouse or data lake. These layers all need to comply with security protocols, information governance standards and other operational requirements.