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Quality Data Is a Key Innovation Enabler and a Foundation for AI Adoption

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September 21, 2021
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September 21, 2021
Quality Data Is a Key Innovation Enabler and a Foundation for AI Adoption

Amid the craze of adopting big data and analytics for a competitiveness boost, businesses fall for the risk of omitting the assessment of the quality of information piled up on servers. Meanwhile, data quality has a direct effect on the accuracy of decisions made using that data and can lead to disastrous results for businesses, being the foundation on which they are built. Quality data drives meaningful insights even in a limited stream and is far more potent in fueling better financial services than a raw warehouse of information of questionable quality.

The poor quality of organizational data causes incorrect analytics, business process interruptions, regulatory and compliance issues. Most importantly, it can have a negative financial impact on the organization.

The quality of data translates into accurate information decisions and serves as a prerequisite for the successful adoption of advanced technologies. In particular, artificial intelligence (AI) can benefit businesses only if it's ‘fed’ accurate and quality data. Being taught on organizational data, AI is a wildcard for businesses – ‘misinformed’ machines cannot make informed, accurate decisions. Only having to assess the right and quality data AI can learn to impact businesses positively.

Nurtured on quality data, AI can increase the efficiency of business processes and boost the accuracy of decision-making. In the years to come, data quality will become a cornerstone of innovation and AI adoption. As Darian Shirazi, Co-founder and CEO of Radius, predicts: “In 2017, foundational data quality will be a prerequisite to quality AI predictions. We will see more companies focus on solving the challenge of maintaining accurate, valuable data so that AI technology lives up to its promise of driving change and improvement for businesses.”

Professionals outline significant challenges associated with big data, which validate the need for data assessment and preparation tools. Those challenges include:

  • Poor understanding of data, which calls for additional efforts to derive any significant value from it;
  • Insufficient data quality and inadequate metadata, which undermines the trust users have in the data;
  • Different varieties of data format, which hamper timely data exploration;
  • Data sharing complexities compounded by the potential of having sensitive and personal data hidden deep within the data assets.

Fortunately, for large institutions across industries, a class of data-focused technology companies has successfully addressed the need for data assessment and clean-up. Examples are represented by such players as Hexanika with its unique Data-Readiness-as-a-Service (DRaaS) solution; BackOffice Associates, which offers Data Audit service to accelerate data quality, migration, and information governance initiatives; Trillium (acquired by Syncsort, a data integration vendor), which enables capabilities for profiling, detecting and correcting errors, and enhancing the completeness of data; Experian Data Quality, which provides comprehensive data management solutions that help businesses to maintain the accuracy of their customer records, reduce errors, and avoid additional costs associated with bad data; etc.

The data quality tools market is highly diverse. It includes vendors that offer stand-alone software products to address the core functional requirements of the discipline, among which experts distinguish data profiling, measurement and visualization, parsing and standardization, generalized "cleansing," matching, monitoring, issue resolution and workflow, enrichment, usability, connectivity/adapters, subject-area-specific support, international support, metadata management, configuration environment, operations and administration facilities, service enablement and choice of deployment options.

In the end, big data can be both a positively powerful tool and a destructive mechanism. It depends on whether big data can facilitate intelligent business operations and decisions or broaden an exclusion. A wide range of decisions in marketing, pricing policy, target audience choice, etc., is based on data. The way that data is collected and handled determines the outcome for consumers, communities, and businesses.

Data of poor quality will always serve as an inhibitor of innovation adoption in the long term. It will mislead business leaders to make half-accurate decisions based on data of questionable quality. About 56% of the UK and US executives surveyed by Experian Data Quality recently shared that bad data had contributed to lost sales opportunities for their firms, while 51% also said bad data had led to wasted time and increasing company inefficiency.

Professionals from Gartner affirm that, given the scale and complexity of the data landscape across organizations of all sizes and industries, tools to help automate key elements of this discipline continue to attract more interest and grow in value.

Overall, the International Data Corporation (IDC) estimates suggest that worldwide revenues for big data and business analytics (BDA) will grow from $130.1 billion in 2016 to more than $203 billion in 2020. As Dan Vesset, Group Vice President, Analytics and Information Management, commented in the official press release, “The availability of data, a new generation of technology, and a cultural shift toward data-driven decision-making continue to drive demand for big data and analytics technology and services.”

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