Data integrity and AI: why intuitive algorithms need both.
As businesses focus on amassing big data and then crunching the numbers for better insight, little emphasis is given to whether the data is valid, relevant and accurate. In a digital world where data-backed insights can practically make or break a business, this is a risk that shouldn’t be taken.
With AI and machine learning being deployed for emulating human thinking, data integrity further rises in importance. Although succinct, here are three reasons why data integrity is highly crucial from an AI standpoint — and what you may stand to lose in its absence.
Quality data is needed to successfully train algorithms.
No AI-infused algorithm is possible without more than its fair share of training. However, it requires high quality data — and massive amounts of it. To imitate the human way of thinking, large data sets are what will determine the accuracy level of the final result. But simply feeding vast chunks of unfiltered, unvalidated data isn’t going to cut it.
Which brings us to our next point…
Clean data helps reduce the chance of errors.
The saying ‘Garbage In Garbage Out (GIGO)’ rings very true in this scenario. What you feed your AI bot will reflect its level of competency in terms of providing expected results. Poor quality data will not only cause errors in output, but could possibly train your bot to counter-operate.
On the other hand, unvetted data lying in repositories can also hamper efforts to scale up, since there will be no workarounds and the data will have to be cleaned before proceeding — a Herculean mission in itself, especially if it needs to be done on a tight deadline.
Data hygiene protects algorithms from being manipulated.
Establishing data integrity is a job in itself — in fact, you might need to dedicate a team member or two solely for the same. In a fast-paced and competitive environment that is also experiencing rapid growth, a dedicated data team may be a good investment for maintaining squeaky clean data — but also for identifying any anomalies.
In the vast abyss of big data, it is very easy for malicious individuals to infiltrate such repositories by depositing chunks of counter-data, to manipulate the algorithm for delivering the exact opposite of what is expected. However, the stakes are higher in a sea of poor quality data — since there will be no distinguishing bad data from the good data.
A dedicated data management team will do its due diligence by incorporating validation checks for every data field, while also monitoring round-the-clock for any anomalies — and remediate them, lest they occur.
In a nutshell…
As business owners shift their focus towards building AI-powered digital applications, it is equally important to ensure enterprise data is being maintained and protected for optimum performance. As data integrity gets put on the backburner in the wake of AI-induced excitement, it is only bound to negatively impact algorithm outcomes — a powerful reason in itself to begin practicing good data hygiene today, if you still haven’t.
