With rapid advancements in information and communication technologies, the world is witnessing an unprecedented data explosion. Today’s exponential data usage and ingestion bring both opportunities and challenges. The ever-expanding data channels increase the chances of inaccurate and unreliable organizational databases. Therefore, organizations need to be hypervigilant and proactive in adopting modern data management and data accuracy solutions to cleanse inaccurate data and maintain consistent data quality across the enterprise.
Researchers estimate that close to 1.145 trillion MB of data is generated every day, and on average, every human generates around 1.7 MB of data every second. With technologies like artificial intelligence, machine learning, and computer vision, it has become more efficient and convenient to acquire consumer data for designing effective marketing campaigns and business strategies, resulting in greater business success.
Data quality as a prerequisite
A lack of data quality undermines corporate objectives and jeopardizes the bottom line, product quality, and corporate image. Data is determined to be of high quality if it meets and fulfills its intended purpose. Additionally, data accuracy, completeness, reliability, relevance, and timeliness are often critical factors in determining quality. Establishing enterprise consistency is also another consideration for elevating confidence in the data and can benefit the organization in many ways.
– High data quality generates higher levels of confidence in products and systems. Increased quality results in reduced risks and often improved data-driven decision-making and conclusions.
– Quality data enhances employees’ productivity who otherwise spend valuable time validating and fixing data.
– High-quality data helps focus marketing strategies and campaigns by accurately identifying target markets and personalizing advertising and promotions around the customers.
The problem with legacy data quality management
Erroneous data is commonly the root cause for the failure of many data-driven and data-dependent initiatives. Historically, many tools and techniques to maintain data quality were based on predefined sets of rules and criteria. However, with today’s more complex and diverse datasets, data combinations and permutations expand exponentially, including the user’s interactions with the data. Rules-based systems quickly become unwieldy and increase businesses’ costs and effort.
Maintaining data quality via AI and ML
The power of data is unleashed when it is transformed into a high-quality, accurate state. The processes surrounding this transformation involve quality assessment, cleansing, and deduplication to name a few. Innovative technologies like AI and machine learning have contributed toward a swift and smooth transition in big data quality processes from a static rule-based approach to a dynamic, machine learning-powered approach. This technology can offer businesses many unique advantages
- – Unlike legacy approaches, machine learning models eliminate the heavy burden of rules-based maintenance. Further, implementing an ML approach avoids scaling issues common with complex and diverse dataset implementations.
- – Machine learning algorithms quickly, effectively, and efficiently discover hidden data issues.
- – The self-learning and self-adjusting algorithms of machine learning automatically keep up with changes in business processes, saving time and substantial business costs.
- – Machine learning-based matching algorithms enable data ingestion for standardization at scale, which simplifies de-duplication challenges and eliminates potential data quality problems.
Many enterprises are incorporating machine learning and AI capabilities to improve accuracy, consistency, and quality. These powerful tools can help with missing or erroneous value identification, automated data cleansing, and data transformation as algorithms learn in real-time from training datasets and human decision processes. Thus, implementing AI and ML can improve data quality through various efforts including:
Data capture automation – According to Gartner, organizations lose an average of between $10 to $14 million annually in connection to inaccurate data. These losses can be averted by implementing intelligent data capture, ensuring that all relevant information is seamlessly and accurately captured.
Duplicate data elimination – Automatically detect and eliminate duplicate data in organizational repositories.
Simplify data ingestion and integration – Create more robust and high-quality data solutions easily incorporating third-party sources.
Ensuring that quality processes are in place to effectively capture, cleanse and maintain the accuracy of data help businesses thrive. AI and machine learning-based processes and systems provide a modern approach to satisfy this growing business need, providing self-learning data solutions that increase accuracy, quality, and efficiency while streamlining operations and improving business ROI.
If you would like to learn more about implementing artificial intelligence and machine learning to improve data quality in your business, send us your query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.