Big Data

Augmented Data Management: A Guide for Businesses

Pinterest LinkedIn Tumblr

Data is one of the most important business assets today. As data increases in volume and form at unprecedented rates, organizations find it overwhelming, complicated, and time-consuming to manage data. Managing and analyzing business data for generating insights is much more complex and dynamic than getting answers from Large Language Models (LLMs) that have become popular today.

A clear data management strategy and modern data management and analytics tools that transcend the limitations of LLMs are crucial to manage business data and extract insights, while ensuring data governance, quality, and security. This is where augmented data management plays a vital role in fulfilling these requirements.

What is Augmented Data Management?

Data management is the process of collecting, organizing, classifying, storing, and securing data for the purpose of analyzing it and extracting insights for business decisions. Augmented data management is the process of applying artificial intelligence (AI), machine learning (ML) models, and automation for managing data management tasks efficiently.

According to Gartner, manual data management tasks can be reduced by 45 percent using machine learning and automation. For example, identifying data patterns and anomalies in large data sets, detecting data quality issues and correcting them, classifying different data types, enhancing metadata, and applying business rules. Augmenting these tasks can expedite time-to-insights, save valuable resources, and free data engineers from mundane data management activities.

How Augmented Data Management Works

Data management involves highly complex, time-consuming, and recurring tasks of consolidating data from various sources, cleaning and preparing data, cataloging and curating its metadata, applying data governance and security standards, and modeling it to make it ready for analysis. Augmented data management does all the heavy-lifting by automating these tasks and accelerating the processing of data.

Augmented data management employs various sophisticated AI and ML models to automate data management tasks in the following ways:

  • Processing data in real time: Human capabilities are no longer enough to handle the great influx of data. Data starts losing its value if not processed and analyzed soon. AI and ML augments the process of cleaning and preparing data so that it is ready for analysis in real time.
  • Standardizing data: By leveraging AI and ML, data can be curated and standardized automatically. Various business rules and standards can be applied to ensure accuracy and consistency. This saves considerable time and cost.
  • Enhancing and managing metadata: Classifying, organizing, and cataloging data is important for understanding underlying relationships between various data elements. With augmented data management, metadata can be effectively enriched and managed for better responses during analysis.
  • Evaluating and maintaining data quality: Augmented data management helps prevent the human errors and biases that can occur in data. It not only helps to detect quality issues faster but also works towards resolving them, so that accurate insights are generated.
  • Enforcing data governance and compliance: The dependency on data to make major decisions also makes data governance and compliances essential, if not mandatory. Augmented data analytics enables organizations to apply data governance policies, monitor security threats, and prevent data breaches on time.

Benefits of Augmented Data Management

Organizations can thrive by taking advantage of the following benefits of augmented data management.

  • Improved data quality, visibility, and reliability: According to Gartner, poor data quality costs organizations an average $12.9 million every year. Data quality directly impacts the quality of insights generated from data. Poor data quality leads to inaccurate decisions, missed opportunities, and revenue loss. Augmented data management helps identify data issues, errors, and inconsistencies in data faster. It ensures better visibility of data by providing a unified view, discovers issues automatically, and thereby helps increase the reliability of data and insights.
  • Real-time processing and accelerated insights: Augmented data management can considerably reduce the time-to-insights by processing real-time data for generating insights. Fresh data coming in can be automatically organized, curated, and made ready for users to start searching. Also, actionable insights such as anomalies, outliers, trends, and patterns can be automatically detected and highlighted to users without waiting for them to ask the right questions.
  • Efficient scalability: By leveraging the speed and efficiency of augmented data management, organizations can optimize various data workflows to keep the data updated. Depending on the influx of data, resources can be scaled efficiently to save time, avoid redundant tasks, and prevent wastage.
  • Effective resource management: Augmenting time-consuming and data-intensive tasks can help organizations manage data with the optimum amount of resources. Data engineers can allot their time and efforts on innovation, instead of getting overwhelmed with mundane tasks. Data stacks, pipelines, and infrastructure can also be managed and monitored better.
  • Better compliance and data governance: Data is being regulated more than ever with privacy and data protection laws across geographies. Augmented data management enables organizations to protect sensitive data and ensure that only the right people have access to it. Applying data governance policies and configuring secure access roles in an automated and augmented way protects organizations from data thefts, breaches, and losses.

How to implement Augmented Data Management

While implementing augmented data management, consider the following points:

  • Uncover data sources: Within an organization, every department has different sources from where they receive data. Data from one department may also be useful for other departments. For example, product development teams may get useful insights from data collected by marketing teams on market trends and customer expectations. Identify the data sources that are valuable for your business objectives and consolidate them in a single data management platform for better visibility and analysis.
  • Identify time-consuming and iterative processes: Data preparation processes that are time-consuming and iterative can be automated with ML. This helps to save significant time and cost, while ensuring that the data does not go stale.
  • Prioritize data quality: Choose an augmented data management platform that has abilities built in to measure data quality, identify issues, and provide resolution. This will ensure that you always get good quality data and in turn accurate insights for your business decisions.
  • Plan for role-based access to data: Ensure that the right people get access to the right data. This can be achieved by implementing robust data governance policies and access controls based on role, geography, and other such parameters.

Leveraging MachEye for Next-Level Data Management

MachEye’s augmented data analytics platform is a powerful solution that can significantly enhance the efficiency of data management and analytics processes. By leveraging advanced AI, ML, and automation capabilities, MachEye empowers businesses to streamline their data workflows, derive valuable insights, and make data-driven decisions faster. Here are MachEye’s core augmented data management capabilities:

  • Low-prep/No-prep Onboarding: MachEye offers data connectors to connect directly to a variety of cloud and on premise data sources. This prevents the time and efforts in copying and maintaining multiple copies of data between systems and ensures a single source of truth. Its low-prep/no-prep onboarding reduces the efforts of connecting, consolidating, and preparing data from weeks to hours. With MachEye, organizations can quickly onboard, scale, and access their real-time data without delays, enabling faster insights generation.
  • Automated Data Cataloging: MachEye’s automated data catalog automates the process of discovering, curating, and categorizing entities, attributes, and relationships. This eliminates the manual effort and time typically required for data preparation and cleansing. By leveraging machine learning algorithms, the platform identifies patterns, trends, and anomalies in the data and proactively delivers actionable recommendations. This automation of insights generation saves time and empowers users with valuable information for making informed decisions.
  • Built-in Data Quality and Observability: MachEye’s built-in data quality index measures and scores data quality across several parameters such as consistency, accuracy, completeness, uniqueness, integrity, and so on. These checks are performed at various levels, right from the workspace to the lowest level of an attribute. MachEye highlights all data issues in its data quality report and also provides recommendations to fix the issues. This ensures early identification of issues, quicker resolutions, and high quality of insights.
  • Robust Data Governance Policies: MachEye provides organizations total control over data so that they can open up access to more users and at the same time protect sensitive data. With MachEye, data governance policies become easy to author, update, and implement. Access can be enabled at granular levels to ensure that the right data is exposed to the right people through search results, insights in shared dashboards, audio-visual data stories, and business headlines.

Going beyond data management, MachEye offers augmented analytics capabilities that help organizations extract full potential from their enterprise data.

Read MachEye’s eBook on Augmented Analytics and Beyond with “What, Why and How” Analysis.