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Data Cleaning and Preparation for AI Implementation


Artificial Intelligence and allied technologies such as Machine Learning, Neural Networks, Natural Language Processing, etc. can influence businesses across industries. By 2030, AI is believed to have the potential to contribute about $13 trillion to global economic activity. And yet, the rate at which businesses are adopting AI is not as high as one would expect. The challenges are multifold- it’s a combination of the unavailability of data to train AI models, governance issues, a lack of integration and understanding and most importantly, data quality issues. Unless data is clean and fit to be used with AI-powered systems, the systems cannot function to their full potential. Let’s take a closer look at some of the main challenges and strategies that can improve data quality for successful AI implementation. 

Barriers to AI Implementation

A recent study showed that while 76% of the responding businesses aimed at leveraging data technologies to boost profits, only about 15% have access to the kind of data required to achieve this goal. The key challenges to managing data quality for AI implementation are:

Heterogenous datasets

Entering prices in different currencies and expecting an AI model to analyze and compare them may not give you accurate results. AI models rely on homogenous data sets with information structured according to a common format. However, businesses capture data in different forms. For example, a business office in Germany may gather data in German while the office in Paris collects data in French. Given the large variety of data that may be collected, it can be challenging for businesses to standardize datasets and AI learning mechanisms. 

According to Jane Smith, a data scientist, “Entering disparate data in different formats and expecting AI models to analyze and compare them accurately is a significant challenge. Homogeneous datasets structured according to a common format are essential for successful AI implementation.

Incomplete representation

Take the example of a hospital that uses AI to interpret blood test results. If the AI model does not consider all the blood groups, the results could be inaccurate and life-threatening. As the amount and types of data being handled increase, the risk of missing information increases too. 

Many datasets have missing information fields. It may also include inaccurate data and duplicate records. This makes the data an incomplete representation of the whole dataset. It affects the company’s faith in data-driven decision-making and reduces the value provided by IT investments. 

Research by Data Analytics Today suggests, “Many datasets have missing information fields, inaccuracies, and duplicate records, rendering them incomplete representations of the entire dataset. This undermines data-driven decision-making and diminishes the value of IT investments.

Government regulatory compliance

Any business gathering data must comply with data privacy and other government regulations. The regulations may differ from state to state or country to country. This can make it challenging for using an AI model that extracts data from global datasets. 

John Anderson, a legal expert, highlights, “Navigating the complexities of government regulations is a critical barrier to AI implementation. Businesses must carefully consider and comply with data privacy laws to avoid legal and reputational risks.

High cost of preparing data

80% of the work involved with AI projects centers around data preparation. Data collected from multiple sources must be brought together instead of being siloed and issues related to data quality need to be addressed. All of this takes time and a certain cost that businesses may not be prepared or willing to invest in the initial stages of AI implementation.

Best Strategies to Improve Data Quality

When it comes to implementing AI models, as listed above, the challenges are largely to do with improving data quality. The poorer the quality of data available, the more advanced the AI models will need to be. Some of the strategies that can be adopted to improve data quality are:

Data profiling

Data profiling is an essential step that gives AI professionals a better view of the data and creates a baseline that can be used for further data validation. Based on the type of data being profiled, this involves identifying key entities such as product, customer, etc., events such as time frame, purchase, etc. and other key data dimensions, selecting a typical time frame and analyzing data. Identification of trends, peaks and lows, seasonality, min-max range, standard deviation, etc. are also part of data profiling. Inaccuracies and inconsistencies must also be addressed and fixed as far as possible. 

Establish data quality references

Establishing data quality references will help standardize validity rules and maintain metadata that helps assess the quality of incoming data. This could be a set of dynamic rules that are manually maintained, rules that are derived automatically based on the validity of incoming data or a hybrid system. Irrespective of the setup, the data quality references must be such that all incoming data can be assessed against the validity rules and issues can be fixed accordingly.  These references should ideally be accessible for process owners and data analysts so that they can have a better understanding of the data, trends and issues. 

Data verification and validation

Once the data quality references have been defined, they can be used as a baseline to verify and validate all data. As per data quality rules, data must be verified to be accurate, complete, timely, unique and formatted as per a standardized structure. Data verification and validation is a required step at the time of entering new data. All data existing in the database must also be regularly validated to maintain a high-quality database. In addition to checking the data entered, validation should also include enrichment where missing information is added, duplicates are merged or removed, formats are corrected, etc. 

In Conclusion

The impact of AI on global businesses is likely to grow at an accelerating pace in the years to come.  From agriculture and manufacturing to healthcare and logistics, AI benefits are spread across all industries. That said, businesses that fail to adopt and implement AI technology will not only lose out on the potential profits to be made but could also see a decline in cash flow. Given the influence of data quality on the adoption and use of AI technologies, this is an issue that must be addressed with urgency. 

The good news is that there are a number of tools that simplify data quality assessment and management. Rather than rely on manual verification, data verification tools can automatically compare data entered against reliable third-party datasets to authenticate and enrich the same. The results are quicker and more reliable. It’s a small step that brings you miles closer to adopting AI systems. 

The post Data Cleaning and Preparation for AI Implementation appeared first on Datafloq.



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