The difference between AI and traditional software is AI must learn how to do its job on its own instead of having to wait for software coders to manually upgrade their creations once per year. An AI system can add new tools, create new features and otherwise alter itself to better satisfy user requirements in a moment’s notice. With access to the right data AI becomes self-learning and self-evolving.
The driving factor in any AI system is the data that AI-driven systems are exposed to. Good data, properly conditioned and placed in the right context, will allow AI services to make informed decisions and take appropriate actions, while bad data will lead to poor results and steadily diminishing performance.
What Does Data-Driven Mean?
Data-driven is an adjective used to refer to a process or activity that is spurred on by data, as opposed to being driven by mere intuition or personal experience. In other words, the decision is made with hard empirical evidence and not speculation or gut feel. The term is used in many fields, but most commonly in the field of technology and business.
Techopedia explains Data-Driven
Data-driven essentially means that data dictates the actions taken by the ones that execute an event or process. This is most evident in the field of big data, where data and information are the basis of all actions and gathering and analyzing of data is the core motivator. Because data is now easier to gather and inexpensive to store, big data analytics is gaining more ground as the best tool for decision making in the business world. Having so much data gives powerful insight into the world and it allows people to manage outcomes because of this.
Even knowing this and having access to more data most organizations have yet to make the changes needed to leverage what the data is saying needs changing.
Talking the Talk but Not Walking the Walk
In an HBR article dated February 9th titled “Companies Are Failing in Their Efforts to Become Data-Driven” Randy Bean and Thomas Davenport write “We knew that progress toward these data-oriented goals was painfully slow, but the situation now appears worse. Leading corporations seem to be failing in their efforts to become data-driven. This is a central and alarming finding of NewVantage Partners’ 2019 Big Data and AI Executive Survey,
Here are some of the alarming results from the survey:
72% of survey participants report that they have yet to forge a data culture
- 69% report that they have not created a data-driven organization
- 53% state that they are not yet treating data as a business asset
- 52% admit that they are not competing on data and analytics.
Further, the percentage of firms identifying themselves as being data-driven has declined in each of the past 3 years – from 37.1% in 2017 to 32.4% in 2018 to 31.0% this year.
These results as reported by this HBR article remind me of the days when Corporate America became obsessed with “Quality and Improving Processes through Teamwork and the Use of Statistical Tools”. Today billions are being spent chasing AI which just happens to be a very powerful technology, yet it is totally dependent on getting access to quality data.
There is an old saying, “Those who forget the past are condemned to repeat it.” So, the lesson is before we chase future opportunities let’s be sure to understand past mistakes and the barriers to change.