AI Market Forces, Data Strategy and Data Governance

With Artificial Intelligence, data strategy and data governance go hand in hand.

Artificial Intelligence is taking over the world, changing our lives and pushing boundaries. AI is indeed now smarter and embedded in more and more devices, such as toothbrushes, refrigerators, and thermostats, than ever before. But what’s next? How about a smart toilet that provides a rapid, daily health check and screening readout! Now, this may sound like an extreme example of TMI (too much information) but consider the positive implications.

In a way, it makes total sense, as using the toilet daily is something that we humans all have in common. And it happens to result in a great deal of available data that AI could use to improve healthcare outcomes while empowering us to make better, healthier choices in the way we live and the food we eat. A perfect and unique example of AI technology helping humanity at scale. So no, your toilet will not read the newspaper to you, but it may become an even more integral part of your daily healthy living regimens.

Data Strategy

In addition to new, enhanced compute, storage and networking technologies, many significant advances in AI are made possible by data. The more data created, the more needed, resulting in a virtuous loop of asset creation. Data just keeps coming and you need to act on it methodically with a data strategy.

Data strategies follow business strategies. If your business strategy is solid, then developing a data strategy is the next logical step in adopting AI. But how do you treat data as the strategic asset that really drives the business, that sets you on the path to capitalize on it?

First, don’t just start AI pilots in search of quick outcomes: think strategically of possible use cases. How do we use data to deliver new customer value propositions and new innovations? How do we use it to make smarter products and services? Next, consider using AI and Data Analytics to drive efficiencies in operations and business processes, thus streamlining your organization for the future. This dual-pronged approach is an effective way to get started.

Moving Beyond Pilots

Of course, businesses exist to create value and drive profits, so AI initiatives must move quickly and effectively beyond pilots to profitability. Therefore, it’s crucial to align business strategy with data strategy, not only to streamline the business, but also to set up for the delivery of smart products and services. At this point in the digital transformation of society, all businesses must ultimately deliver smarter offerings, or they will end up in the dustbin of history.

We’ve touched on how applying a data strategy can wield significant benefits, both in new, smarter products and services development, as well as in streamlining operations. However, it’s not an all or nothing proposition. AI can deliver incremental steps in the journey to your smarter products and/or services.

Think for instance of the future vision of self-driving cars. We’re not there yet, but the safety measures brought by work in the autonomous driving space are already here and standard on most new vehicles. Consider features such as lane keeping assist, or automatic braking for collision avoidance. These safety enhancements are significant and serve to prepare us all for the day that our cars will automatically and safely drive us anywhere without us touching a steering wheel!

The Importance of Data Governance: Doing Good

Competitiveness and delivering an enhanced customer experience using AI is just the tip of the iceberg. The emergence and broader adoption of AI is arguably the most important technological advance of this generation, thanks in large part to the increasing power of the compute, storage and networking systems underlying it, not to mention the billions of smart devices and IoT sensors proliferating around the globe, leading to amassing swells of data. But caution is in order.

Doing good for humanity is a key altruistic underpinning of historical technology advances. Therefore, data strategy goes hand in hand with data governance. All organizations need to adhere to numerous data privacy and security frameworks, so it’s critical to have a clear ethical stance when it comes to AI: the data used for, and generated, by it. Lack of clear standards here can result in penalties and fines, but also in the loss of customer trust.

And it can have serious ripple effects across entire industries. Retaining clear ethical standards in the use of AI, having ethics integrated into processes, is the best way to avoid issues such as bias.

We need to be as cognizant of these societal ramifications as we are of those for business. We will need more focus on ethics, reducing biases and making AI decisions more explainable. Therefore, keeping humans in the loop, via ethics panels, stronger regulatory frameworks, and real-time monitoring, will be critical for the continued positive adoption of AI. And the good of our society and planet.

Next Steps

We’ve discussed the importance of business and data strategy alignment, data governance and the requirements for successfully adopting AI. The next step is to update and modernize your IT infrastructure to meet the rigors of data science. This is where Dell Technologies can assist.

As one of the world’s essential providers of IT infrastructure, we have all the necessary AI solutions and building blocks designed for the rigors of data science. And we will stop at nothing to help you achieve success in deploying them.

We also work closely with our ecosystem partners such as Intel. In fact, just recently we jointly launched the next generation of PowerEdge Servers, our newest innovation engines, aimed squarely at allowing our customers to better innovate with data.

Mark Orth

About the Author: Mark Orth