Data scientists face an ever-increasing volume, variety, and velocity of data.
Processing, analyzing, and gaining value from this data is becoming increasingly challenging, given the time and effort required for every data management process. To solve this problem, more and more organizations are turning to Augmented Data Management (ADM).
This transformative approach uses artificial intelligence (AI) and automation techniques to streamline data workflows and enable advanced analytics. ADM automates time-consuming and repetitive tasks so data engineers and analysts can focus on higher-value activities that directly impact business growth.
In this article, we will explain what ADM is, why it’s an essential tool in the modern data landscape, and the best practices for implementing it.
Understanding Augmented Data Management
Augmented Data Management (ADM) is the use of AI and machine learning (ML) technologies to enhance and automate many data management processes.
It leverages AI techniques to augment the capabilities of data management systems and tools, improving efficiency, accuracy, and productivity. ADM can optimize processes involved in data integration and ETL (Extract, Transform, Load), data quality, data governance, data analysis, and more.
ADM can automate repetitive tasks, improve quality, enhance compliance, and enable faster integration and analysis. This ultimately supports better decision-making and insights.
The Need for Augmented Data Management
Traditional data management involves manually performing all tasks related to data management and analytics. While these approaches have been effective, they also have limitations that can hinder organizations. Some of the key limitations are:
- Time-consuming processes: Traditional data management often relies heavily on manual processes. These tasks can be labor-intensive, take time to complete, and are prone to errors. As datasets grow in volume and size, the manual approach becomes increasingly inefficient and less scalable.
- Limited agility and flexibility: Traditional systems are built for specific use cases. They require significant effort to adapt to evolving business needs. Adding new data sources, changing schemas, or incorporating new use cases can be complex. This lack of agility makes responding quickly to changing business requirements difficult.
- Quality control: Manual cleansing processes are susceptible to human errors, and ensuring consistent data quality becomes challenging, especially when dealing with large-scale datasets from diverse sources.
- Inadequate metadata management: Manually capturing and maintaining metadata takes more time and effort. There are also higher chances of errors, leading to difficulties in data discovery, lineage tracking, and understanding assets.
- Limited scalability: Traditional systems may face challenges in efficiently processing and storing large datasets. Scalability becomes a significant concern, especially for organizations with big data or real-time data processing requirements.
- Insufficient governance: Conventional systems also lack robust data governance mechanisms, so enforcing policies consistently and maintaining regulatory compliance becomes more challenging.
- Inefficient data integration: Manual integration processes require significant effort since engineers must manually maintain consistency and accuracy across multiple data sources. This adds to their ever-increasing workload and could slow down your data pipelines.
These factors prevent engineers and analysts from collecting insights and making improved decisions at a pace fast enough to gain a competitive edge.
Addressing these limitations requires adopting augmented data management approaches that use AI, ML, and automation techniques.
Benefits of Augmented Data Management
ADM allows organizations to harness the full potential of their data assets and improve data management processes by offering five key benefits:
#1. Improved quality and consistency
Engineers can automate tasks like anomaly detection, cleansing, standardization, deduplication, and enrichment using ADM and AI techniques. They can also implement real-time data quality monitoring mechanisms.
ADM can significantly improve the accuracy, consistency, and reliability of data. It reduces manual effort and speeds up quality assessment.
Augmented management techniques can also automate governance processes and enforce policies. This leads to enhanced data security, privacy, and regulatory adherence.
So, organizations can maintain high-quality databases that facilitate better decision-making, improved analytics outcomes, and increased trust in insights.
#2. Enhanced efficiency and productivity
ADM automates manual data management tasks, reducing the reliance on manual effort and repetitive processes. This automation leads to increased efficiency, faster processing, and improved productivity of engineering teams.
ADM also accelerates integration, which reduces the time-to-insights and enables timely decision-making.
With mundane, repetitive tasks out of the way, data professionals can focus on more strategic and high-value activities that help organizations thrive.
#3. Advanced data insights and decision making
ADM can be used to streamline the entire data pipeline. Data scientists can automate and speed up integration, preparation, visualization, and reporting.
It also supports real-time analytics and incorporates advanced analytics capabilities, including predictive modeling and ML algorithms. These techniques enable organizations to uncover patterns, correlations, and trends.
It facilitates intelligent data discovery and exploration. It assists in identifying relevant datasets, understanding contents, and discovering relationships between different data sources.
These features help leverage datasets effectively and uncover in-depth, groundbreaking insights that drive success.
Core Components of Augmented Data Management
The core components of Augmented Data Management encompass various techniques, technologies, and processes that work together to enhance data management operations. They include:
Data Quality Management with AI
Data quality management ensures data is accurate, reliable, and fit for analysis. By leveraging AI for quality and master data management, organizations can automate manual processes, improve accuracy, and enhance their data’s overall quality and reliability.
AI algorithms can automatically analyze data to profile its characteristics, identify patterns, and assess its quality. Machine learning models can learn from historical patterns and detect anomalies, outliers, and data quality issues.
This automated profiling helps organizations prioritize data quality improvement efforts.
ADM systems can also automatically fix quality issues such as inconsistencies, duplicates, missing values, and outliers.
AI-driven Data Cataloging
Cataloging enhances accessibility and accelerates discovery processes. AI-driven cataloging and master data management can create a comprehensive inventory of available assets, establish lineage, and map relationships between different datasets.
This enables users to search, discover, and understand data through intuitive interfaces and intelligent search capabilities.
Using AI algorithms, natural language processing (NLP), and machine learning, engineers can automate metadata extraction. These techniques automatically analyze content, structure, and context to derive relevant information such as types, schema, relationships, and key attributes.
ADM systems also use AI techniques, like semantic search and recommendation systems, to understand user queries and suggest relevant datasets based on user preferences, past usage, and characteristics.
This comprehensive, automated documentation ensures that information is easier to understand and use while improving governance.
AI-aided Data Governance
AI can automate data governance workflows like request and approval processes, stewardship activities, and policy enforcement.
ADM solutions have features like access controls, privacy measures, security mechanisms, and auditing to maintain integrity and protect sensitive information.
For example, AI algorithms can analyze user behavior patterns, data usage, and sensitivity to detect anomalies and potential security breaches. They can also enforce access policies, identify unauthorized access attempts, and trigger alerts or automated actions to mitigate security risks.
AI-enabled Metadata Management
Metadata provides information about the structure, context, and meaning of data, and effective metadata management is crucial for discovery and governance.
ADM systems can automate the collection, storage, and organization of metadata. AI-driven systems can automatically extract metadata from various data sources and classify and tag them.
These solutions can also incorporate user feedback, usage patterns, and quality indicators to refine search results, recommendation algorithms, and metadata extraction techniques.
AI-powered metadata management helps organizations use their data assets better and gain valuable insights.
Machine Learning in Integration and Processing
ML models can analyze data processing workflows, identify performance bottlenecks, and suggest optimizations to improve pipeline efficiency and resource utilization. This results in faster and more efficient integration and processing.
ML-driven ADM can also be used to analyze patterns and make predictions.
Augmented data integration can also automatically match data transformations across different sources and align schemas during integration.
Tools and Technologies for Augmented Data Management
There are many popular tools and platforms available that help build ADM systems. These include:
- Airbyte: Airbyte is a cloud-based, open-source integration platform that can automate your pipelines. Using connectors and integrations, Airbyte enables teams to build AI pipelines and automate integration workflows, including tasks like schema change handling, normalization, and orchestration.
- Informatica: Informatica is an AI-powered intelligent data management cloud service. It uses an AI engine named CLAIRE to automate manual tasks, reduce data classification time, and enhance discovery.
- Talend Data Fabric: Talend Data Fabric incorporates AI-powered data profiling, cleansing, enrichment, and more to streamline data management tasks.
- Microsoft Azure Synapse Analytics: Azure Synapse Analytics is a cloud-based analytics service. It works with Azure Machine Learning and Power BI to enable ML and AI-driven analytics and processing workflows.
- Oracle Analytics: The Oracle Analytics platform is a cloud-native service with embedded machine learning and NLP technologies to boost productivity and build an analytics-driven culture.
Real-World Examples of Augmented Data Management
To illustrate how ADM can improve data workflows across industries, we’ve listed two real-life case studies:
Case Study 1: How AstraZeneca accelerated data governance with Augmented Data Management
AstraZeneca, a leading global pharmaceutical and biotechnology company, wanted to shorten its drug development timeframe, and data governance was a crucial aspect of this.
Andy McPhee, Data Engineering Director at AstraZeneca, explains, “We must balance this desire to speed the process with trusted data. If we do not have the quality in our data, our drugs will not be approved, and we will be affecting the lives of potential patients.”
The company used Talend Data Fabric to load source data into a data lake and implement strict data governance measures. By implementing automated workflows and machine learning models, AstraZeneca can get 90% of its ingested data ready for analysis in 3 minutes and save significant amounts of money.
Case Study 2: How Skanska sped up reporting with ML
Skanska AB, a multinational construction and development company, wanted to build a financial reporting solution for global business users.
Instead of their IT team manually spending days finding, analyzing, and compiling data, Skanska implemented Oracle Analytics and its ML capabilities to automate report generation and improved reporting processes by 4 to 6 times. Their users can now generate analytic reports in less than five minutes.
Best Practices for Implementing Augmented Data Management
Implementing ADM comes with many challenges related to complexity, errors, and data governance. It requires careful planning, strategy, and execution. Here are some best practices to consider:
- Define clear objectives: Identify the goals you want to achieve with ADM. Align these goals with the overall business objectives. A clear vision and purpose are crucial when driving the implementation strategy since ADM implementation can be complex. It also helps measure the success of your ADM initiative.
- Assess readiness: Evaluate your data’s quality, consistency, and completeness. Identify any gaps or issues that must be addressed. Ensure you have the right infrastructure and technologies to support ADM processes.
- Establish data governance framework: Install a robust data governance framework and policies, standards, and procedures for data management. This is vital for maintaining integrity throughout the ADM lifecycle.
- Build a skilled team: Assemble a data team consisting of experts in engineering, artificial intelligence, and advanced analytics. Invest in training and upskilling your team to handle the complexities of ADM effectively. Foster collaboration between business stakeholders, data specialists, and IT teams to ensure a holistic approach.
- Prioritize data integration: Focus on integration efforts to collect information from diverse sources. Consider using integration platforms or tools that support automation.
- Start with specific use cases: Implement your ADM solution for use cases that deliver immediate value. Identify areas where ADM can have the most significant impact, such as integration challenges, quality issues, or analytics requirements.
- Measure and iterate: Continuously measure the success and impact of your ADM. Establish key performance indicators (KPIs) and metrics to track progress. Regularly assess the effectiveness of your ADM processes and make improvements.
Augmented Data Management can transform data engineering and enhance traditional data management processes.
AI and ML, implemented correctly in ADM solutions, can remove redundant, low-impact tasks from the engineering workload, allowing engineers to focus on work that makes a difference.
ADM can also streamline many data management tasks, including integration, standardization, and quality management. So, data scientists get access to the freshest information quickly, can perform in-depth analytics, and gain crucial insights faster.
Overall, it empowers engineers to effectively manage and derive value from massive datasets and drives informed decisions and innovation within organizations.
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