Master Data Management

Data quality management: Solutions for a data-driven age

As businesses rush to leverage artificial intelligence (AI), they often overlook ‘data quality’. The performance of AI depends on the quality of the data you feed. Poor data quality causes 75% of the new initiatives to fail.

High-quality data is also essential for data-driven decision-making, business models, advanced analytics, personalised customer experiences, and privacy and compliance. We produce 2.5 quintillion bytes of data daily, and low-quality data costs American businesses $9.7 million per year through failed technology transformation initiatives.

This article explains the need for data quality management, trends that will govern its future, and best practices to ensure high quality.

Why do we need data quality management?

In a data-driven technological age, most businesses make strategic decisions based on data rather than intuition, thus making high-quality data essential. Here are some of the reasons to invest in it –

  1. Better decision-making – High-quality data impacts all processes within the organisation, such as supply chain, procurement, resource planning, and customer relationships. Form data houses to find trends, form strategies, and drive a higher ROI. According to a survey, 92% of leaders find high-quality big data useful for decision-making, and 89% say it has revolutionised operations.
  2. Time and budget optimisation – Bad quality data wastes time and effort and results in loss of money and resources. This is a strong reason for businesses to invest in defining and meeting the standards for high-quality data.
  3. Competitive advantage – From marketing to sales and finance, improve the performance of all processes within an organisation with high-quality data. In a competitive and fast-paced world, quick and informed decision-making is important. This is where high-quality organisational data gives you a competitive advantage.

How does data governance impact data quality?

Robust data governance frameworks ensure accountability, standards, and compliance for data management. They establish frameworks and quality metrics to identify and resolve quality issues quickly. Data governance in the organisational culture results in successful data-driven technology transformations.

Data governance is an ongoing process that requires monitoring and improvement through quality audits, compliance checks, and stakeholder feedback mechanisms. By having literacy among employees, businesses make data quality an essential part of day-to-day operations.

5 Data quality trends that will govern the future.

The following key data quality trends ensure that the technology transformation initiatives are accurate, consistent, reliable, and successful.

No-code/low-code approach

Even a small business can generate up to 1 GB of data daily, and it is impractical to expect the team to spend hours on data quality checks. No-code or low-code AI platforms for DQM offer a user-friendly data cleaning and matching ability with minimal effort.

AI entity resolution to revolutionise data management and analytics

AI integrates machine learning (ML) and natural language processing (NLP) to identify, link, and cleanse data in diverse datasets. As a result, AI in modern DQM solutions improves compliance by 30%.

Leveraging data-as-a-service

Small and medium businesses can make critical mistakes in decision-making due to poor data causing legal, financial, and brand value loss. They will leverage data-as-a-service (DaaS) to overcome the problem of limited in-house resources. DaaS is set to grow to $43 bn by 2028.

Data governance a key priority

Data governance is not just an IT function, but a strategic component that includes data quality, privacy, and compliance and 80% of businesses consider it their top priority. Its frameworks ensure businesses with big data comply with privacy and compliance requirements, such as the GDPR.

Real-time data quality monitoring

Modern DQM ingests data into the system in real time. It does not wait for aggregation and cleansing before checking for quality. This helps fix quality issues before they can cause any problems downstream. In 2024, 50% of businesses will adopt modern DQM solutions to elevate performance.

Ensuring high-quality data – What next?

Businesses starting with DQM transformation must have a governance model with clear roles and responsibilities, and involve all stakeholders with the highest level of transparency.

A data glossary is a good starting point before deep diving into the root cause of poor-quality data. By fixing the problems in existing data, you set the ground for preventing costly mistakes in the future.

Invest in automation to avoid manual data entry, which is the most common cause of poor quality. Manual data entry also requires a higher workforce, thus increasing your costs. Have security processes for quality control, which archiving, cleansing, recovering, and deleting data and define KPIs to measure the result.

How can Infosys BPM help with Data Quality Management Solutions?

Infosys BPM has data quality management (DQM) as a part of master data management (MDM). It provides data quality assessment, categorisation, profiling and cleansing, validation, and quality framework definition.

Read more about data quality management at Infosys BPM.

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