What's Your AI Differentiator?
Explore AI, machine learning, and good data.
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Companies are scrambling to build their artificial intelligence (AI) arsenal, machine learning (ML) capabilities are evolving, and organizations are employing several robust third-party plug-and-play AI solutions (such as Microsoft’s Azure Machine Learning, Amazon’s SageMaker, and Google’s TensorFlow).
However, companies seem to overlook a significant differentiator — "Good Data." Whenever I interact with an infant AI team, I see behind-the-scenes data-panic. Even though businesses have tons of data, the question to ask yourself is: Do you have good data? For implementing or adopting ML into your products, good or useful data is your significant differentiator. Many organizations do not realize it because good data is uncommon.
Traditionally, businesses collected data about "things and money." Today, it is different. Most valuable businesses now sell software and networks, not just physical goods and capital assets. Even manufacturing companies such as Whirlpool selling washing machines and microwaves offer apps and platforms that connect people. With the advent of IoT, even bulb makers have app platforms. Such business transformations have changed the perspective on what we measure and what drives value.
My point is, machine learning solutions are not going to be smart about any topic until taught with good data. It is fair enough to say that ML reads faster than humans. However, they do need more "good data" to outsmart humans.
Another problem that AI teams do is choosing data known to everyone. Let's say your organization is working on a prediction using government-provided datasets. The problem here is that others have access to it. There is no gain in using ML to study the same datasets that everyone in the market is already probing — seeking predictions from the same data matter that everyone else is using is not going to help your company succeed. Instead, companies that want AI as their differentiator must discover and uncover relationships between new datasets. They should build smart-data teams as well to assist the new AI team.
Good data creation is far more complex than a mere data point aggregation such as customer information and loading them into a database. Most organizations mistakenly believe that efficient data-gathering consists of picking up every scrap of possible data and then meticulously consolidating them in hopes of discovering fragments of insights that predict or categorize something they care about. While it is true that ML can surprise us, the technology is still not fully capable of presenting these insights with consistency.
However, such ML variations don’t mean the AI tools are broken or incompetent, and it implies that we have to be prudent when applying ML techniques. In the end, it all depends on the good and right data that we provide for the algorithms to learn from;
To conclude, ML outshines humans in ingesting good data that we gather and offer insights. ML is a faster, better, more scalable, and less error-prone method for applying insights. To train and engage machine learning to its full potential, you don’t feed the system every known data point that you find; instead, you feed it with carefully selected, organized, and presented sets of knowledge, expecting it to learn and maybe extend the edge of knowledge that people already have.
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