Reliable data – a core underpinning of Artificial Intelligence

Artificial Intelligence (AI) is possibly the hottest of hot topics at the moment, particularly when it comes to reliable data. ​

What makes headlines is AI’s combination of wonderful potential applications; everything from curing cancer to driver-less cars, and a potentially terrifying loss of human control in which doomsday scenarios proliferate.

While the upsides seem evident, many people in business are concerned about AI, as they don’t know how it works and in effect, can’t see inside the box.   This then leads to questions of trust and thereby to the ethics of the applications.

Reliable Data

Neural networks, algorithms, the ability of machines to learn are pretty complex and can be esoteric.

That said, one thing that is generally straightforward is the data that is being applied. And it seems just like in every other data-driven dimension of our world, reliable data is the key to successful AI.

At Data Agility we’ve long used the term ‘reliable data’ rather that ‘accurate data’, ‘trustworthy data’ or anything else to describe what data people are seeking to use.  When we spoke with our clients, our research showed that people wanted data they could rely on to make decisions.  Reliability took out absolute standards of accuracy and trustworthiness.

In our day-to-day lives, humans are generally very good at filtering out rubbish data, and the majority of us bring natural statistical skills to our lives which help us through the day.   Similarly, many AI solutions can behave in a comparable way IF there is an understanding of the quality of the data at the outset; that it is reliable data.  If this understanding exists, then training routines can be established that allow the AI identify valid patterns in the data.

From there, the AI can determine what to do, within its preset parameters, with the patterns it observes.   And from there it can continue to learn and deliver useful outcomes.

However, without this baseline data quality understanding there are very real risks of the old fashioned rubbish-in-rubbish-out model being repeated.

So, if you want to implement AI in your business or you are talking to an AI vendor, make sure that you get to the bottom of what data you/they will use and deeply understand the reliability of your data.


Submit a Comment

Your email address will not be published. Required fields are marked *

Most popular insights.

Effective use of technology and data in sports

Effective use of technology and data in sports

Sports across codes and the world expect decision makers, like referees, to make perfect decisions with the tech and data they have at their fingertips. The passion of the sports fan and the desire for the ref to make the correct ‘call’ is not going anywhere. So is technology to blame? Is the data inaccurate? Or is human error unavoidable? Let’s explore.

Data Lifecyle Management eBook

Data Lifecyle Management eBook

This eBook shows you the best practices on data lifecycle management; collect the data you need, store it securely while you need it, dispose of it when it is no longer needed.