The underlying problem

When it comes to the implementation of Artificial Intelligence, a lot of people have started to acknowledge the impact and return it can have on their investments. But when companies actually decide to hop on the train of smart technology, one issue seems to always come up: data quality.

Data is the essence of Artificial Intelligence. In itself, AI has existed for some time now. However, the reason it has gained such momentum in the last couple of years is specifically because of the humongous amount of data that is now being collected. On an individual level, our social media, online research and spending habits give us away. But when it comes to organizations, most of them are working hard to create a monitoring system that would allow them to understand the activities happening internally, as well as externally.

Working in the AI industry in the Middle East, I have come to realize that the recurrent problem is not the existence of the data itself, but the collection of data. In other words, even if in appearance most companies gather data, some light should be shed on the ‘how’.

The quality of the data is compromised.

I have mostly been encountering two types of clients:

• The first one wants their employees to keep tabs on what is happening internally, only for safety reasons: gather this data as “proof” and be ready for the unfortunate event where their customers file a complaint or a lawsuit. Those clients end up having tons of data, mostly paperwork, dating back years or even decades. A lot of times, this physically translates into them having what they call “archive rooms” that are used to preserve such evidence.
• The other one imposes the collection of data on their employees, but does not provide them with the necessary education on the importance of data collection. As a consequence, people don’t bother inputting accurate data, which renders the insights generated irrelevant.

Naturally, what I am describing varies from one industry to another. However the problem with data quality remains the same across all.

Before getting excited about implementing smart technology and Artificial Intelligence, perhaps we should start by asking each other “How accurate is your data?”.