It was an icy morning at Cape Canaveral, Florida on January 28, 1986 and all eyes were on NASA’s historical space shuttle launch, the Challenger, promising the dream – Space for Everyone. The dream quickly turned into a tragedy when the space shuttle exploded just 73 seconds after lift off, killing all seven crew members aboard. This disaster shook the entire nation and the space shuttle program was grounded for nearly three years. It was soon learned that the explosion was caused by the failure of a tiny rubber part, commonly known as the O-ring. The O-ring’s function was to seal the joints and prevent hot combustion gasses from escaping from the inside of the motor.

What went wrong with the O-rings on the fatal morning? The record-low temperatures on the launch day stiffened the rubber O-rings, reducing their ability to expand and seal the joints. Ignoring the available data for decision making and owing to the extreme external pressures, where the space agency had to innovate and prove its space dominance, induced the disaster. The temperature on the launch day was 36 degrees. Investigation into the horrid incident revealed that the O-rings were consistently malfunctioning under 53 degrees 1 . Superficial look at the O-ring performance data from previous 23 launches would not reveal a pattern as the O-rings failed both in higher and lower temperatures. There was also the issue of lack of data as there were no prior launches where the ambient temperature was below 53 degrees. This was also a clear case of a sample selection problem. A statistical analysis on the available dataset would have clearly demonstrated a correlated probability of successful launches at higher temperatures. The challenger disaster gives us a great deal of insight into the critical importance of data-driven decision making.

This may sound like an extreme example caused by the failure of data-driven decision making. However, whether it is introducing a new product, launching a new marketing campaign, expanding to newer markets, data is extremely critical to decision making and can make or break the future of an enterprise. In a recent example, Zillow’s iBuyer – a machine learning driven home buying service – was shut down due to higher-than-anticipated conversion rates and unintentionally purchasing homes at higher prices. Rich Barton, the CEO and co-founder of Zillow 2 said in a letter to shareholders,

“Put simply, our observed error rate has been far more volatile than we ever expected possible and makes us look far more like leveraged housing traders than the market makers we set out to be. We could blame this outsized volatility on exogenous, black swan events, tweak our models based on what we have learned, and press on.”

Similarly, Tay – an AI chatbot released by Microsoft – was taken offline 3 after dashing out a slew of controversial tweets, a testimony to prove the importance of data and AI governance.

Just a decade ago, one of the common challenges faced by enterprises was the lack of availability of data, hampering business growth. Lack of data availability meant that crucial business decisions weren’t yielding the right impact. Fast forward to today, storing vast amounts of data and access to 3rd party data is no longer proving difficult or cost prohibitive, thanks to the exponential evolution of cloud computing and data democratization that alleviated the data availability concerns. However, having access to data is very different from making sense of the collected data. From edge devices, IoT sensors, mobile, social media and user events, enterprise data sources continue to expand exponentially. Owing to the exponential growth in data, IDC predicts that the global data growth will reach a massive 175 zetabytes by 2025. While the data availability is no longer a concern, the primary challenge lies in ensuring data integrity, consistency and unified data governance. It’s no doubt that data is the new gold. However, organizations can hit gold only after prospecting, mining, curating, extracting, enriching and refining the data mine, while also ensuring the right data is used in the right context at the right time for business decisions.

Traditional data warehouses predominantly operate on structured data, offering deterministic data analytics. In contrast, a modern enterprise data lake house encompasses both structured and unstructured data, serving as a foundation for predictive data analytics. This opens up innovation avenues that were once thought impossible. The modern discipline of Generative AI is a subset of deep learning. While a traditional discriminative AI is trained on a labeled dataset to help cluster, classify, or predict the next best action, a generative AI is trained on massive amounts of generally available data—text, image, audio, or video—also known as the foundational model to generate new data.

There are five key considerations as enterprises explore and experiment with Generative AI;

1. 5. Establish Generative AI KPIs : As with any technology investment, it is imperative to establish a set of key performance indicators (KPIs) to measure, track, and report on the value of Generative AI. Some key metrics may include productivity improvements, output quality and relevance, accuracy, and business impact.
2. 4. Picking the Right Data: The choice of data source is critical for training and fine-tuning a generative AI model. A good data source should be representative of the real world, free of bias, and large enough to provide the model with a variety of examples to learn from. This will help to ensure that the model produces accurate, reliable, and explainable results.
3. 3. Identify a Business Domain: : It is imperative to identify a specific business function that can be significantly improved by Generative AI. For example, Generative AI can be used to improve associate productivity by reducing repetitive tasks or unlocking and activating large corpus of data to glean insights.
4. 2. Responsible AI: It is of paramount importance to foster a culture of responsible innovation in order to ensure the long-term success of any AI initiative. While generative AI holds the potential to transform business functions, building AI applications that are transparent, fair, secure, and inclusive can reinforce customer trust while also mitigating unintended bias.
5. 1. Data and AI Governance: In most technology discussions, governance is often overlooked or treated as an afterthought. However, it is prudent for enterprise leaders to establish data and AI governance before embarking on any AI initiatives, including Generative AI. AI curiosity must be well-governed from the outset to ensure that AI applications continue to guarantee consumer data privacy, ethical use, eliminate bias, and provide control.

Some Generative AI use cases that enterprises across any industry vertical can leverage to realize quick value add include:

• Associate Productivity:Generative AI can serve as a collaborator for a number of everyday tasks and repetitive activities such as routine email management, organizing calendars, identifying trends in data sets, writing and even for learning and development.
• Customer Experience: Generative AI can augment traditional customer service capabilities such as chatbots to offer smoother self service and personalized customer experiences.
• Data set Generation: : Generative AI can help generate synthetic multi-modal data sets for various AI/ML use cases that can significantly improve the model quality.
• Personalized Marketing: Leveraging GenAI capabilities, unique media content can be generated from simple prompts for e-commerce, marketing campaigns or web design. It can also be used to create personalized marketing campaigns that are more likely to resonate with individual customers.
• Code Generation: IT teams can accelerate application development and code quality by automating code generation and recommendations

While the technology landscape surrounding Generative AI will continue to evolve and mature, it is only a matter of time before it becomes widely adopted. The right approach for each organization will ultimately depend on its unique needs and goals. By building a strong data foundation with the right governance strategy and a focus on responsible AI use, enterprises can strive to achieve consistent and long-term success.

1. 1. https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster
2. 2. https://www.linkedin.com/pulse/here-what-we-told-shareholders-employees-our-decision-ri ch-barton
3. 3. https://en.wikipedia.org/wiki/Tay_(chatbot)