Master Data Management

Ensuring data integrity: Master data practices for AI-driven future

Research shows that the number of companies choosing artificial intelligence (AI) for their key processes has jumped 270%. To date, 37% of these companies have implemented AI technology. According to this research, the AI market at $51 bn in 2020 will reach a staggering $641.3 bn. However, many companies have jumped into AI adoption without ensuring that their master data is ready for it.

Before businesses start adopting AI data integration and benefiting from it, they must ensure master data integrity so that it is ready to handle the leap.

This article explains the current challenges and problems with master data and preparing yours for an AI-driven future in business.

What are the current problems with master data?

Businesses embark on the AI journey thinking that technology alone will solve their problem. However, they forget to check their current master data integrity.

They miss out on transforming raw and unlabelled data into usable information. Without an effective data processing system, data scientists spend 80% of their time, on average, collecting, cleaning, normalising, and organising data. This time could be better spent on contextualising and enriching this data.

This causes the AI initiative to fail or lack its full potential due to the absence of operational readiness and gaps in the data pool.

Why is data integrity important for AI success?

Machine learning models ingest huge amounts of data to learn. Inaccurate and incomplete data can make the ML model learn incorrectly and the AI model to derive the wrong output. For example, if the model is trained to identify cars on a road using images. But some images are those of motorcycles. Then the model will not learn to accurately distinguish between the two. This could have negative consequences on an AI-powered traffic management system. It could also cause serious financial losses due to legal liability, regulatory fines, and loss of trust due to a damaged reputation.

What are the challenges of handling vast datasets?

The complexity of data increases exponentially with its volume. This makes it challenging to manage, validate, and synthesise data for AI models. The 4 challenges of vast datasets are –

Data volume

The sheer volume of data can make it challenging to manage and process it timely.

Data diversity

Different data sources and formats can make integration and analysis challenging.

Data quality

Data from different systems can be inaccurate, incomplete, or biased.

Data security

Careful handling and security are needed for sensitive data such as credit card numbers and social security numbers.

How to prepare master data for an AI-driven future?

Ensuring data quality and integrity is essential for the success of your business’s AI transformation. Here are some ways to prepare your master data for successful AI implementation.

Data integration

Start with data warehousing to solve the issues of speed and volume of data generation. Group similar data arriving from several locations into a standardised format and ingest it into a common pool or data lake. Create your standardised cloud databases and prepare the data for big data analytics, artificial intelligence, and machine learning platforms.

You now know where the data is stored and in what format and volume. Since all the data is now in the same place, it is easy to access and optimise for accuracy and quality.

Accuracy and quality

Implement data quality and accuracy checks and alerts to ensure the viability and stability of AI algorithms. Reliable data is the backbone for trusted results from your AI implementation. Inaccurate results from your AI system do not let you reap its benefits, such as automated commercial decision-making indications.

Despite the need for accurate and high-quality data, 84% of CEOs still lack confidence in their data integrity. However, most of the critical decision-making still happens on this data. According to a survey, businesses lose up to $15 million per year due to poor data quality.

Machine learning algorithms are highly vulnerable to unreliable data. They use large quantities of data to adjust their parameters, and a minor error can cause a big mistake in the system output. Therefore, the quality of a company’s master data directly impacts its results and credibility.

Contextualisation and enrichment

By using geospatial technology, you can geocode and enrich the data, thus establishing links and relationships between components. You can add geographic location, mobility, and demographic details to validate the data and put it into a context. This is critical before feeding the data into artificial intelligence and machine learning models. Doing so increases the effectiveness of the model’s predictive analysis capabilities.

How can Infosys BPM help?

Leverage master data management, discover opportunities for data quality improvement, and simplify and digitise business processes for better AI transformation. You can do so through the following steps –

1. MDM advisory
2. Master data maintenance
3. Data quality assurances

Read more about generative AI for business at Infosys BPM.