Many organizations have already adopted Master Data Management Solutions or tools as part of their Digital Transformation journey. Yet, they struggle to maintain high-quality data consistently due to outdated, traditional methods of data management that are unable to handle ever-growing volumes of enterprise data.
On the other hand, inaccurate, incomplete, and inconsistent data affects decision-making, inflates operational costs, and weakens business agility. However, with the advent of Augmented Data Management Services powered by AI, organizations can tackle challenges encountered by Data Quality Management.
In this blog, we delve deep into how augmented data management works, its features, functionalities, and how it enhances data quality.
Let’s get started!
Augmented data services or augmented data management (ADM) is the application of Artificial Intelligence that is used to enhance and automate data management tasks such as data quality, master data management, and metadata management. It utilizes Automation, AI, ML, and Advanced Data Analytics to tackle data quality challenges. It streamlines data harmonization, cleansing, standardization, and governance processes so that organizations can get high-quality and actionable data. With this augmented intelligence, businesses can utilize the full potential of their enterprise data and drive operational efficiency, competitive edge, and innovation in the cut-throat competitive digital landscape.
Improved Data Quality
With the help of automation, data will be cleansed and enriched. Augmented data solutions utilize AI algorithms to identify data inconsistencies, errors, and missing values more efficiently. Thus, it ensures data is up-to-date and enhances data accuracy.
Streamlined Data Integration
Combining data from disparate sources will be complex, tedious, and time-consuming. Augmented data management simplifies and streamlines this process through automated data integration. AI-powered tools map, merge, and harmonize data from various sources and systems so that it has a single, unified view of an organization’s operations and business processes.
Effective Data Governance
With augmented management, Data Governance will be effective. It is proactive rather than reactive. Also, intelligent systems can monitor data usage, enforce data policies, ensure regulatory compliance, and reduce the risk of legal liability and data breaches.
Adaptive Learning
The best quality of ADM is to learn and adapt. It improves its algorithms as the system processes more data, making data management tasks even better and efficient in future. Thus, it ensures master data management practices can evolve with ever-changing business requirements.
Advanced Analytics
ADM automates routine data tasks and accelerates the time it takes to generate insights. Advanced augmented data analytics tools like PiLog use ML to swiftly analyze large volumes of data, uncover trends, and deliver actionable insights that drive business decisions.
Automated data preparation tasks include data cleaning, data transformation, and data enrichment.
ADM automatically fixes data errors or inconsistencies such as incorrect formats, duplicates, and missing values, transforming it into a usable format, and adding extra details to make it more insightful. Machine Learning algorithms can identify patterns in data-quality issues and rectify them.
Data transformation includes data normalization, data standardization, and data aggregation. These will help in converting data into the desired structure or format so that it can be analytics-ready and compatible with various systems.
Data enrichment involves augmenting data with additional information such as geolocation or demographics, in order to provide a richer context for analysis.
ADM seamlessly combines data from all your different systems, even if they're in different places (cloud, on-premise) or formats (structured, unstructured, semi-structured). This gives you a complete picture instead of scattered pieces of information. It's like merging all your toolboxes into one giant, well-organized one. This complex procedure can be simplified through
Intelligent mapping: It involves identifying relationships between data sets and accordingly mapping them. This gives organizations a holistic, unified view of data.
Providing real-time access to up-to-date information.
ADM tools monitor data quality, data usage, and data integrity continuously so that they can detect anomalies, check compliance issues, and track data lineage. It also makes sure everything is legal and compliant with regulations.
Real-time anomalies: With the help of AI, they can identify outliers or unusual patterns in data which leads to errors, issues, and fraud. Anomaly detection in real-time helps organizations resond better to potential issues.
Automated compliance check: ADM ensures data management practices adhere to regulations and standards like GDPR and maintains audit trails and automated compliance checks to reduce the risk of non-compliance.
Tracking data lineage: It provides a detailed history of data such as its origins, usage, and transformation. Thus, it helps organizations understand how the data flows and ensures transparency and accountability.
ADM tools predict future trends and easily show you important information through clear charts and graphs. You can even ask questions in plain English and get answers instantly.
Predictive analytics: It uses ML models that provide insights on historical data, forecast future trends, and predict customer behaviours, sales trends, risks, and factors.
Natural Language Processing: Through Conversational AI, users can ask queries in natural language and it makes complex data analysis accessible for non-technical users.
Data Visualization: ADM enables organizations to create intuitive and interactive visuals that help in identifying trends, patterns, and insights.
Last but not least, ensuring data security and privacy is paramount for Augmented Data Services. ADM protects your data with strong security measures, ensuring only authorized people can access sensitive information. It's like having a lock on your toolbox to keep your tools safe.
Data Encryption: It helps in preventing unauthorized access and protecting sensitive information.
Access Controls: ADM implements robust access control methods such as user authentication and role-based access controls to ensure that only authorized persons can access or modify data.
Data masking and anonymization: It’s another security mesures that hides or alters sensitive data, making it tough to gain the personal details of individuals.
ADM is transforming how industries manage and utilize data, driving efficiency and innovation. In financial services, ADM systems enhance fraud detection by analyzing transaction patterns and enable precise risk assessments for investments and loans. Healthcare providers utilize ADM to integrate patient data, streamline clinical decision-making, and improve public health monitoring.
The impact of ADM extends to retail and hospitality sectors as well. Retailers use ADM to personalize marketing, optimize inventory, and analyze market trends for better pricing strategies. Similarly, the hospitality industry benefits from ADM in recruitment and operations management, enhancing decision-making and adaptability.
In a nutshell, Augmented Data Services makes your data easier to understand, use, and trust. It streamlines the entire data management process, from data harmonization and data integration to data analysis and data protection. Also, utilizing the power of AI, ML, and Data Analytics empowers organizations to overcome challenges encountered by traditional data management, ensuring data quality and data governance at scale.
As data continues to drive the future, integrating ADM tools is no longer an option. It’s a necessity for your organization’s success. Reach us to adopt PiLog’s ADM services to unlock the full potential of data, drive sustained growth, and gain a competitive edge.
