Augmented Data Management uses advanced technologies to optimize and improve data management processes in a company.
Data Management (DM) is an essential enabling factor for analytics, Data Science, Regulatory Reporting and digital business transformation. This practice allows collecting and storing, transforming and using the data in compliance with policies and regulations. It helps to quickly make correct decisions and maximize the benefits for the organization. There are many disciplines under the umbrella term of Data Management:
- Data Quality,
- Metadata Management,
- Data Governance,
- Master Data Management,
- Data Integration,
- Data Preparation, etc.
What are the aims of Augmented Data Management?
- it automates many DM operations that are currently done manually;
- it allows less technically savvy users to be more autonomous in using the data;
- it takes some tasks off the shoulders of such technical specialists as data technologists or data scientists. Then they can focus on the highly valuable tasks: design new use cases, manage new analysis models or solving complex problems.
What are the application modes and benefits of Augmented Data Management?
“Augmented Data management: Metadata Is the New Black” ranks fifth among the Top Trends in the world of Data&Analitycs.
According to Gartner, by 2023 IT specialists will be less engaged in managing and preparing repetitive and low-impact data. Augmented Data Management technologies will free up to 20% of their time for collaborations, education and self-education, and for high-value DM tasks.
By 2023, organizations that dynamically automate, connect and optimize their DM processes via Active Metadata, Machine Learning, and Data Fabric will spend 30% less time on Data Integration.
Today everybody needs to know what data are available, what their meaning within the organization is, how valuable and how reliable they are.
However, huge amounts of data are often distributed in multiple sources, perhaps in various cloud systems. Making this information quickly available for different users is not as easy as pie. So what can we do?
Boost up the metadata. These contain the organization’s data in the form of entities, their attributes and relationships between them. Consider a metadata management system as a corporate safe. It is usually administered by the Chief Data Officer and neatly stores all that concerns the company’s data of interest. The ability to use this information, i.e. to activate the metadata, allows the system to:
- suggest new data quality rules;
- report the availability of new metadata;
- detect the presence of sensitive data for privacy purposes;
- identify the use of the same data in different business processes and use cases;
- determine the degree of relevance, and much more.
- Create a Data Fabric,or an architecture designed as a structure of interwoven services, microservices and DM components.
Use Augmented Data Quality technologies to automatize data quality controls and resolve detected anomalies based on pre-established policies and rules.
Use a Data Catalog to register all the company’s data assets and related entities. Advanced techniques (Artificial Intelligence/Machine Learning) allow to automatically collect and organize such metadata. Then it is easy to physically locate the data, understand their semantics and assess the quality. Besides, all parties concerned get smooth and controlled access and sharing.
Take advantage of Semantic Knowledge Graphs to:
- interactively visualize the information, making evident even the non-evident connections;
- navigate the represented business processes going back to data sources, roles and responsibilities;
- analyze the impact of changes;
- detect and minimize risks;
- identify and analyze the connections between entities with the help of Data Lineage and impact analysis functions.
Irion EDM is an open, efficient, scalable, All-In-One Enterprise Data Management system. It is based on the innovative and groundbreaking “declarative” paradigm.
Irion EDM allows to implement and govern end-to-end all phases of a complex process of Data Management, such as
- data mapping and acquisition,
- profiling and normalization,
- transformation and validation,
- enrichment and publication.
It provides support to a model of work entirely and natively metadata driven.
The declarative model is more agile and intuitive. The user only needs to declare what they want to obtain. The process execution – the choice of steps to take, dynamic construction of the necessary objects and structures – is delegated entirely to the product.
Irion EDM is designed to have all that is necessary to meet the Data & Analytics requirements in one application. It brings to life such novel patterns and design concepts as the Data Fabric (cf. Gartner®).
Irion EDM is structurally designed from the perspective ofAugmented Data Management. Native integration with R and Python allows to introduce calculation logic based on Machine Learning & Artificial Intelligence models to processes at all levels.
Irion EDM is open. It quickly connects to multiple data sources and fits into the company’s landscape system as it uses only the modules necessary to achieve the goals.
Want to learn more?
Contact us! We will provide you with illustrative examples of how other organizations have already started their transformation.
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