The phrase "data is an asset" is something of a corporate cliché. However, it is increasingly true as companies in industry after industry undergo programs to digitize their businesses. It is obvious that Netflix is a highly digital business with data at its heart, but what about more down-to-earth enterprises like manufacturing or energy? Even in the oil industry, the talk these days is of the digital oilfield where vast amounts of sensor data about the operation of an oil platform is captured and analyzed so field production can be tweaked and tuned in real time. In order to extract value from your data, though, you first have to know what you have and where it is, and this seemingly obvious starting point is a major hurdle in a large corporation.

You can't do anything with data – let alone use it for machine learning – if you don't know where it is. In the age of big data, this is not a trivial matter. It is also the main driver that's propelling the rise of machine learning data catalogs, which the analysts at Forrester recently ranked and sorted. Just a word of warning: the name at the top of the list might surprise you. According to Michelle Goetz's June 21 Forrester Wave report, the percentage of analytic decision makers managing more than 1 petabyte of data (either structured, semi-structured, or unstructured) has essentially tripled from 2016 to 2017.

The promise of AI is that it will deliver digital transformation and improve productivity and efficiency across businesses. For many of our customers, IBM Watson has already helped deliver on this promise – by enriching customer interactions, accelerating research and discovery, empowering employees, and mitigating risk. The next step for businesses is to make AI ubiquitous by operationalizing their workflows across the full AI lifecycle. IBM is committed to delivering these fundamental, end-to-end AI capabilities and giving enterprises everything they need. For example, consider the critical step of understanding and preparing data for productive and speedy use in analytical tools, machine learning and deep learning.