Whatever your organization’s motivation or need to use data science is, it is clear that data science, if done right and with correct context, has the power to deliver impact regardless of the industry. But, the story does not end here. There is lot more to be thought about as the industry matures. If you are a business leader of an industrial company who regularly relies upon driving outcomes and decision making through data science, you may be thinking, “How do I go beyond analysis and insights? How do I get continuous value?” Even if you are new to adopting data science in your enterprise, you should consider these aspects.
But, you can’t rely on data science alone to get continuous value, optimize processes, improve productivity, generate new revenue streams, and spur growth. You have to go from doing data science to building data products. And if you thought that building data products is hard in the consumer world, it is even harder for industrial companies. Even though the end result is a software product, there is a distinction between building a software product and a data product, which is delivered as software. Misunderstanding of this critical distinction leads to failed products before even they are developed.
There is a growing trend in democratizing data science, adopting machine-learning techniques to drive data products, and cultural change to find new innovative ways to develop products in a cross-disciplinary team.
In the next part of our data science blog series, I will describe these trends, define data products, identify unique challenges of developing data products for industrial companies, and share lessons that can be applied to develop industrial data products.