Building a Strong AI Foundation: The Critical Role of High-Quality Data

Whether it’s manufacturing and supply chain management or the healthcare industry, Artificial Intelligence (AI) has the power to revolutionize operations. AI holds the power to boost efficiency, personalize customer experiences and spark innovation. 

That said, getting reliable, actionable results from any AI process hinges on the quality of data it is fed. Let’s take a closer look at what’s needed to prepare your data for AI-driven success.

How Does Data Quality Impact AI Systems?

Using poor quality data can result in expensive, embarrassing mistakes like the time Air Canada‘s chatbot gave a grieving customer incorrect information. In areas like healthcare, using AI models with inaccurate data can result in a wrong diagnosis. 

Inconsistencies arising from the lack of standardized formatting can confuse the AI algorithm and result in flawed decisions. Similarly, relying on outdated data can result in decisions that do not suit the current trends and market conditions. 

Having duplicate records is an acute problem as it skews analytics and could lead to misallocated resources and overproduction. Hence, despite the many benefits AI has to offer, it would be unwise to rely on AI systems without first preparing your data. 

A recent study found that only 4% of companies consider their data ready for AI models. So, how do you address the issue?

Assessing Data Readiness for AI Processes

AI algorithms depend on patterns gleaned from the data they are fed to make decisions. If the data is incorrect or outdated, the conclusions derived are likely to be wrong. Hence, ensuring good quality data is the foundation for effective AI implementation. 

To begin with, data must be complete. For example, a street address must include an apartment number, building name, street name, city name and pin code. Secondly, the data must be accurate and formatted in a consistent structure. 

For example, all telephone numbers must include the area code. Data must also be valid and unique. Having duplicates in your database can skew analysis and affect the relevance of AI reports. 

Preparing Data for AI Algorithms

Even the most advanced AI models cannot correct underlying data quality issues. Here are a few things you can do to make your data ready for effective AI implementation.

Assess data sources 

The first step to preparing data is to identify and evaluate data sources. Data must be collected from reliable sources and handled with care to minimize the risk of collecting erroneous data. Profiling the data helps set parameters and identify outliers. It must also be structured to be consistent with data inputs for the AI model. 

Collect relevant data

More is not always better for data. Being selective of the data collected helps keep data secure and minimizes unnecessary complexities in the AI algorithms. It cuts through the clutter and makes AI systems more efficient. There are two facets to ensuring the AI models are fed only relevant information. Firstly, design intake forms carefully so they do not ask for any unnecessary information. Secondly, filters can be employed to select the data required and keep other data out of the AI system. 

Break down data silos

Surveys, onboarding forms, sales records, and so on, businesses collect data from many different sources. Holding this data in individual silos can limit its usability. To overcome this, data from various sources must be integrated into a central repository. 

The process may also include standardizing data formats. This makes it comparable and also minimizes the risk of having duplicates in the database. Above all, it delivers a comprehensive view of the data available. 

Verify and validate data

Data must be verified to be accurate before it can be added to an AI database. Today there are a number of automated verification tools that can help with this. Automated data verification tools compare the data collected from sources with data from trustworthy third-party databases to ensure that they are correct. Verification tools must also check data for formatting and consistency. 

In addition to verifying incoming data, all existing data must be validated before it is fed into an AI model. Such batch validation ensures that the database stays up to date. After all, data can decay over time. For example, when a customer changes their phone number, the old number in your records becomes invalid. 

Data enrichment 

Data may also need to be enriched to meet the standard for completeness and provide a more contextual basis for AI models. Data enrichment plays an important role in understanding demographics and customer segmentation. 

For example, street addresses can be enriched with location-based information to help insurance agencies make more accurate risk assessments. Many data verification tools are capable of enriching data with information extracted from reference databases. 

Implement stringent data governance practices 

Training AI models on proprietary data can put sensitive data at risk of being exposed. Hence the need for a strong data governance framework. This should ideally cover data security, user interface safeguards and testing standards. 

Defining roles and responsibilities of the data users makes it easier to keep data secure. Similarly, logging data access and transformation helps maintain control over data access and reduces discovery time for security issues. 

Powering AI Algorithms with Trusted Data

The era of AI is definitely here. But to fully leverage AI’s potential, organizations must pay close attention to data quality used to train AI algorithms. To ensure precise predictions, data fed into the system must meet high quality standards for accuracy, completeness, timeliness, uniqueness, validity and consistency. 

Selecting the right data sources and profiling all incoming data is a great starting point. Following this up by verifying and validating data before it is fed into the AI models keeps bad data out of the system. Automated verification tools can be further used to enrich data and give AI systems a more comprehensive dataset to work with. Taking these few simple steps to prioritize data quality builds robust and resilient AI systems capable of making decisions that take your business into a brighter future. 

The post Building a Strong AI Foundation: The Critical Role of High-Quality Data appeared first on Datafloq.

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