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Data Quality Management–An Absolute Must to Drive Business Success

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Data Quality Management – An Absolute Must to Drive Business Success

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Data quality is crucial for the effectiveness of operations today. Any deficiency in data quality can adversely affect business outcomes. Innover’s AI/ML-driven Data Quality Management Framework guarantees high-quality data throughout its lifecycle and across various sources, instilling confidence in every business decision.

Enough has been said and stressed about the importance of data. Still, not much has been spoken about its quality. After all, not all data is gold. As per Statista, the world today is generating, consuming, copying, and storing mammoth amounts of data expected to touch 180 zettabytes by 2025. In this era of massive data growth, ensuring data quality is paramount for businesses to make informed decisions. Data quality benchmarking, profiling, and cleansing are crucial practices that help maintain accurate and reliable data. Benchmarking compares data against standards to ensure reliability, profiling analyzes data for inconsistencies, and cleansing corrects errors, enhancing data integrity. These practices enable businesses to maximize the value of their data assets and stay competitive in a data-driven world.

Drive Sustained Success Through Quality Data

Ever since the explosion of data, businesses have shown keenness in making data-driven decisions rather than relying on gut instincts. It’s an approach that has paid impressive dividends as observed by the McKinsey Global Institute which states that data-driven businesses are 23 times more likely to acquire customers, six times more likely to retain existing customers, and 19 times more likely to be profitable. Another study jointly conducted by Harvard and MIT School of Management found that data-driven companies had better financial performance, were more likely to survive, and were more innovative. With the balance firmly leaning towards data-driven entities, leaders wishing to join the data-driven club should ensure they have quality data to drive success. 

Data Quality Management – DQM, a Must Have for Success

Data quality management is markedly crucial in the era of big data and advanced analytics as it is a prerequisite to ensure that only high-quality data percolates from the exodus of big data coming from multiple sources. The quality of data is detrimental to draw valuable insights from the data, any pilferage of poor quality data and the focused efforts and investments in advanced analytics can fall flat on the face.

Data Quality Management Inevitable for AI Success

High-quality data is indispensable for both AI and Gen AI systems as it serves as the foundation upon which these technologies operate. In AI, the algorithms are designed to extract patterns, make predictions, and learn from the data they are provided. Therefore, the accuracy and reliability of these algorithms heavily depend on the quality of the input data. Without good data quality management, AI models may produce inaccurate results, make faulty predictions, or exhibit biased behaviors. Similarly, for Generative AI, which aims to mimic human-like intelligence and reasoning across a wide range of tasks, the need for high-quality data is even more pronounced. Gen AI systems require diverse and representative datasets to develop a comprehensive understanding of the world and to perform tasks with human-like proficiency. In essence, good data quality management forms the bedrock upon which AI and Gen AI capabilities are built, ensuring their effectiveness, reliability, and ethical use.

“Finding the right data, cleaning it, and shaping it is a skill that takes experience and understanding.” Dimitris Bertsimas, Professor of Operation Research, MIT SLOAN
Source: The Analytics Edge: How to turn data into a competitive advantage – MIT Management Sloan School

Conquer DQM Challenges with Innover’s Data Quality Management Framework

Our eBook titled, ‘Build data you can count on: Zero doubts, all confidence,’ provides you a first-hand understanding of the importance of data quality management lining out the essentials of a healthy data management process. It serves as a primer for organizations in the age of big data and advanced analytics guiding them on the best practices that safeguard their data and business interests.

Innover’s Data Quality Management Framework is the perfect antidote for the big data deluge that can help build confidence in every data byte, across all sources, for all users and enable you to spot data challenges before they become a problem. Embrace a progressive data management practice that improves data quality to propel you toward data-driven supremacy and success.


  • Innover Team  |  April 18, 2024   |  
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