5 Reasons Why AI Needs Good Quality Data

March 21, 2023
5 Reasons Why AI Needs Good Quality Data

AI is everywhere. Everything you see is in some way driven by AI. Due to its ubiquitous nature, the meaning of AI is very much a discussion within itself. AI started as an abbreviation for Artificial Intelligence, however as the use of AI has developed, so the term has evolved as well. Now, many prefer the term Augmented Intelligence as that is here now, where-as artificial intelligence (depending on your definition) is a long way away.

What Is Augmented Intelligence?

Augmented Intelligence (AI) balances the power that computing can provide in machine learning, data processing, natural language visualization with the freedom of the human brain. AI is doing tasks consistently and at a scale that manual processes could never achieve, augmenting a process to make it more effective and efficient.

The Importance of Data for AI

AI requires data to be effective to give the outcomes that make it a crucial part of your business. That means the value of AI is only as good as its data. Bad data can corrupt the outcomes and reduce AI effectiveness.

But why?  If your phone can recognise your face from a million others, why can’t your AI simply filter out bad data from your enterprise data environment on the fly? Why does bad data create such a problem?

Bad Data vs. Trusted Data

Well, AI could filter out data that doesn’t fit certain criteria, but it has to build up the view on what that data constitutes. Your phone can recognize your face, however, it needs to calibrate what your face looks like as a first step. Imagine using a friend’s face to calibrate the phone security, ridiculous! But this is the same as building an AI model using poor quality data and expecting it to give you the right results. Starting with bad data works like a false positive or a false negative and skews your results.

A false positive or false negative can then lead your business down the wrong path. In medicine, false results can lead a doctor to serious consequences, even causing patient harm. The same is true for your business. Bad data can lead you to product studies, changing business strategies, and new business solutions that won’t help, or do more harm than good.

To optimise your AI, you want to feed it trusted data. Trusted data comes from a trusted source. Typically, a trusted source will be:

  • Regularly Updated: the data is regularly refreshed to avoid data decay
  • Thoughtfully Formed: the definition of “good” is carefully defined with setting data parameters
  • Carefully Reviewed: the data is reviewed to catch duplicate data, incorrectly migrated data, etc.
  • Operationalised: the data source is active and ongoing

It’s easy to see how a trusted source can provide the best data to optimise your AI. After all, your AI is only as good as the data it receives. But let’s go into more detail than that. Let’s take a look at exactly why trusted data is essential to AI performance.

Related post: How Big Data Is Being Used to Tackle COVID-19

5 Reasons Trusted Data is Essential to AI Performance

The reasons are easy to understand, but most don’t take the time to consider them. Too many projects rush to deliver AI, spitting out facts and figures, and do not focus on the data. Don’t make the same mistake. Take a few minutes to read the following and then take the time to consider the issues that can arise if you do not use trusted data.

1. Trust

If you leave a process to run and it throws out results that can be easily discounted, then trust in that process will evaporate and be difficult to regain. Imagine if you used the facial recognition security for your phone, and you tried it on a friend and it accepted their face. You would stop using it, and it would be a long time before you tried it again. AI is the same; if it lets you down based on dirty data, it won’t be the data that people stop trusting, it’ll be the AI process.

2. Productivity

ML/AI processes that are being created by data scientists should be all about designing models. It is estimated that 60% of a data scientist’s time is spent data wrangling, which is not getting the most out of your resources.

3. Governance

Good governance can be compromised by dirty data unless it is identified and then resolving, it forms part of a governed process. It isn’t always obvious, but the need to clean up data in a structured and governed way can fall by the wayside when a business requires results. The net of this is that AI, reporting, and data flows start to have logic built into them to account for poor quality data: a transformation here, a filter there. Your outcome may end up looking more like you want it to, but the business has lost clarity on how it arrived at the outcome. Lineage is lost, confidence in the data improves, but the confidence is misplaced.

4. Speed

Dirty data will stop your AI project going live. Trying to accommodate poor quality data when you are trying to deploy is challenging. Accommodating data quality as a process in your overall project may extend your original estimate by a small amount, but it increases your chances of making that date and provides business value to boot. There is no use in having a quicker time to value if you don’t achieve it.

5. Morale

It is infuriating to work on an AI project, to find the right model that works for you, to test and refine that model, have it produce great results, and then go back to the drawing board when you discover that one of your key fields was populated inconsistently. Data related setbacks can cause shoulders to sag and affect a projects morale. Not to mention the wasted time and money spent working with dirty data. Employees will feel deflated, and the boss’s wallet may feel the same.

A Strong Foundation for AI

The foundation for AI is trusted data. The foundation for a successful business is trusted data. And, the foundation of any business strategy is a trusted team. Prolifics has a team of end-to-end specialists who help businesses utilise AI and manage, analyse, and get insights from data by using IBM Cloud Pak for Data alongside Watson for AI.  Talk to our Prolifics data experts to learn more about how our range of Data & Analytics solutions can take your business to the next level of efficiency and effectiveness.