How Important is Data Accuracy as AI Evolves?

Spreadsheets are a relic of the past. There is no longer a need for data to be stored in these archaic contraptions. Instead, we have moved onto a more fluid method of gathering, storing, and analyzing data. Google searches, shopping history and even social media posts can now be collected, analyzed, and stored automatically. Artificial intelligence will then take it a step further by automatically analyzing this data and funneling the useful information into the right systems. Read on to know why data accuracy is so important.  

Big data continues to expand and hit companies with the velocity of a hurricane. It is hence no surprise that businesses are asking themselves the most critical question – why is data accuracy so important? Well, the answer is obvious. Having access to accurate data ensures that your marketing efforts are not wasted in targeting the wrong demographic. It also makes sure that your resources are not wasted in creating a product that will not be in demand. Artificial intelligence makes sure that the data you are using is accurate. 

That’s the reason why artificial intelligence skills have been on a constant rise since the last three to four years.

Data Quality and Intelligence Must Go Hand-in-Hand

The two need to have a symbiotic relationship. For example, customers are starting to take a keen interest in how their favorite products are being made. You could be left out of the loop if your business does not have access to the right information. 

Companies that incorporate automation should also pay attention to the quality of their applications. They have a certain expectation of how these tools must be produced. Some concerns include:

• Stolen or duplicate information
• Incomplete information
• Corrupted information
• Inconsistent data

In short, you cannot rely on artificial platforms that are built using subpar standards because they will provide you with inaccurate results.

“Business analytics is dependent on the delivery of actionable and accurate insights, which is why artificial intelligence has become essential.”

The sheer volume of data that companies receive has started to overwhelm their current systems, which is why they are turning to automated processes to help collect and store only valuable information. 

Let’s take a closer look at the different types of data that companies are collecting right now. 

• Location and condition of physical assets.
• Sensor data from production floors in factories.
• Past and present sales data.
• Customer demographics and social trends.
• Geographical data from surveys and studies.
• Order tracking and monitoring inventory.

Businesses in today’s world accumulate an insane amount of data. If they have any hopes of being competitive in the future, then it’s essential to get a hand on it. That means mobilizing artificial intelligence systems to help derive meaning from this data. 

That brings us back to data quality. Since data is used to provide direction and help make important business decisions, it is very essential for it to be accurate. But data quality will also directly impact analytics, automation, and artificial intelligence tools. 

Where Data Science and Machine Learning Cross Paths

Machine learning is a method of artificial intelligence that uses algorithms to predict outcomes automatically. The goal is for these systems to receive data and then compare those against statistical models to be able to make predictions. While the output data looks relatively clean and simple to users, there is a lot of data mining and predictive modeling going on behind the scenes.

So why is data accuracy so important? Look no further than Amazon’s powerful e-commerce system.   

Amazon has mastered this craft. They gather data continuously at every second of every day. If you shop on their platform, then they are collecting data on every product you’re viewing. They have systems in place that compare this browsing history to your account, factor in your purchase history, and then automatically compile a series of emails to send you product recommendations. It’s a very complex system that is completely reliant on data. 

However, imagine if this data were inaccurate. They would be sending the wrong product recommendations to the wrong users. Their pages would be sorted in the wrong order. In short, their automated system only works because it’s collecting accurate data. 

The 5 R’s of Data Quality

Relevance

Data that is collected must be relevant to the audience or overall goals of a business. This requires consistent reviews to reflect newer needs as they arise. Turning back to our previous example, Amazon uses a combination of social media engagement and its powerful review system to determine what is considered relevant. 

Recency

Certain developments in business have a short life span, and so companies must be providing their teams with a steady stream of new data. It must also set older data at a lower priority. Having AI systems in place does little good if it’s not being updated with new, relevant information. 

Range

Data must come from a wide range of sources. If a business is depending on data from only social media sources, then their quality of data will suffer. One source of data is not enough. We need a wide range of sources to ensure the quality of data. 

Robustness

High-quality data can be used in several different scenarios. It can be updated seamlessly as new, relevant information is discovered. Robust data can help AI systems fill in certain gaps left by missing values – all while maintaining accuracy. 

Reliability

This is also referred to as data integrity. Accuracy, validity, and consistency are the three key characteristics that make data reliable. This is not done through repetition. It’s done through carefully designed collection systems. Data must be reliable if it’s to be used in planning and decision-making. 

Data Accuracy is Critical

Data is quickly becoming the heart of how a business operates. While AI systems can be used to collect and organize most data, there will still be times when manual data entry is required. A couple of examples would include corporate meetings and teleconferences. Businesses must ensure that this information is put into the system accurately. One way to guarantee this is to have the data entry outsourced by professionals. 

Business is going to continue to evolve, and as it does, and the importance of data accuracy is only going to increase.