Applied Observability: How Does It Help Redefine Data Democratization?

Why Trust Techopedia

Applied observability helps redefine data democratization by breaking down data silos and enabling organizations to distribute data to all teams and individuals. It promotes data-driven decision-making by providing real-time visibility, contextual understanding, and the ability to respond to issues promptly. With its versatility and agility, applied observability allows organizations to adapt to changing needs and systems. However, challenges such as data complexity, integrity, privacy, security, and cultural transformation need to be addressed to fully realize the benefits of data democratization with applied observability.

Embracing Data Democratization for Informed Decision-Making

The concept of data democratization has transformed the way organizations consider and act on data, making it more useful for decision-making.

Until recently, access to the data has only been made available to a smaller number of individuals with specific skills and departments within organizations. This used to create data silos and hindered collaboration, as insights and information, in that case, get confined to a small group, often leading to inefficient decision-making and missed opportunities.

Data democratization proponents emphasize the need to distribute information to all the teams in an organization. This enables individuals with diverse expertise to easily and quickly access data to gain insights. It ensures the availability of information to all those who can contribute to a certain organizational cause in any way rather than a few people.

Moreover, data democratization helps organizations promote a culture of data-driven decision-making, where everyone can make an informed choice based on accurate and timely information.

To fully benefit from the benefits of data democratization, organizations need a comprehensive strategy that makes it possible for them to observe, analyze and draw data-driven conclusions from their vast sources of information. This is where applied observability comes into play.

Applied observability is the systematic collection and analysis of data from multiple sources within an organization’s systems to offer data-backed insights to explain the possible outcomes and scenarios associated with different strategies.


What Is Applied Observability?

Observability refers to an organization’s capacity to comprehensively understand the quality of the data in the entire organizational system.  The concept of observability is rapidly evolving in the data engineering domain. A framework that enables organizations to collect, manage, and analyze data from different sources so that they can draw inferences is essential to fully benefit from their information’s potential.

To this end, the application of the concept of observability may have a significant role in ensuring that valuable information from numerous data sources is easily accessible everywhere at all times through the appropriate use of tools and practices.

An organization, regardless of whether it uses this or not, is able to make more informed decisions and unlock the true value of its data sources by applying applied observability.

Why Applied Observability?

In the digital age, applied observability is essential to exploit fully the potential of data democratization in organizations. Historically, data access and analysis have been restricted to several individuals or teams that hamper the democratization of decision-making with the help of data.

By democratizing access to real-time business intelligence, however, these barriers are removed by applied observability. It makes it easy for employees to obtain valuable information from the data across various departments and roles.

  • Data-Driven Decision Making Through Applied Observability

Observability facilitates cooperation and promotes data-driven discussions, driving innovation at all organizational levels through shared views on data and insights. In addition, it allows individuals to evaluate inefficiencies in their operations and make optimizations based on data, leading to continuous improvement. Organizations can utilize the full potential of applied observability to democratize data and cultivate a data-driven culture using the right tools, technologies, and processes.

  • Applied Observability as Enabler of Real-time Business Intelligence

Applied observability enables real-time business intelligence by providing organizations with immediate visibility into their systems, applications, and infrastructure.

It allows companies to:

  • Make informed decisions based on updated information;
  • Discover emerging trends;
  • Take advantage of timely opportunities;
  • Help businesses optimize their activities;
  • Enhance efficiency;
  • Stay up-to-date in dynamic markets.

How Does Applied Observability Differ From Traditional Data Monitoring?

There are several ways in which applied observability differs from the traditional approaches to data monitoring.

Precision and Thoroughness Basic metrics such as server performance, traffic on the network, or application uptime are typically a focus of traditional data monitoring. On the other hand, applying observability extends far beyond surface monitoring and provides a more comprehensive and detailed understanding of system behavior. It is designed to obtain a comprehensive view of the system.
Contextual Understanding Compared to the traditional monitoring approaches, applied observability emphasizes the collection and analysis of data in the context of the overall system and its business impact. It focuses not only on monitoring individual elements but also on understanding how they interact with each other to achieve the purpose of achieving business value. This contextual information enables organizations to identify patterns, correlations, and anomalies which may have otherwise been missed by traditional monitoring allowing for more accurate troubleshooting, performance optimization, and proactive decision-making.
Real-time Visibility In contrast to traditional monitoring, which provides a periodic overview of the system’s performance, applied observability offers real-time insight into its behavior and operation. As a result, organizations can respond to issues and incidents as they arise.
Versatility and Agility Traditional data monitoring is sufficiently based on standard metrics or alerts that do not provide meaningful insight into modern systems’ complex and diverse nature. In contrast, the applied observability permits capturing and analyzing custom metrics, events, or logs configured for their particular organization’s needs. Given changing business needs and systems, this flexibility enables organizations to adapt their monitoring procedures to keep the captured data consistent.

8-Step Strategy for Implementing Applied Observability

Below is an 8-step implementation strategy to implement observability:

  • Step 1: Need and Goal Assessment

Before implementing observability, the precise needs and objectives of the organization need to be assessed.

Moreover, assessing the difficulties and challenges pertinent to data availability, quality, and veracity is important for devising proactive strategies.

  • Step 2: Define Scope and Identify Use Cases

Defining scope refers to explicitly specifying the data sources, systems, and applications the observability implementation will consider. In other words, define the boundary of the data ecosystem that will be observed and analyzed.

On the other hand, the example use cases that could be considered are many, for example, customer behavior analysis, market analysis, etc.

  • Step 3: Establish Data Collection and Integration Procedures

Establish the protocols to collect and integrate the data from various data-generating sources, for example, log files, APIs, and databases.

Identify appropriate tools and technologies, such as data pipelines and data ingestion frameworks, beforehand to collect and integrate the data in the observability framework.

A comprehensive system for quality monitoring should be put in place to ensure the accurate, complete, and consistent collection of data.

Introducing automated checks and rules to identify anomalies, data gaps, or data format issues helps ensure data accuracy and completeness.

  • Step 5: Leverage Advanced Analytics Techniques

Using advanced analytics techniques, such as machine learning (ML) and other statistical techniques, obtain meaningful information from the collected data.

To present the analysis outcomes effectively, identify certain appropriate visualization tools.

  • Step 6: Continuous Improvement Initiatives 

Through data observability, develop an environment to support continuous improvement and process optimization in the organization.

Based on regular feedback, the observability strategy should be reviewed and updated.

  • Step 7: Foster Data-Driven Decision Making

Innovation in the organization can be encouraged by the insights derived from data observability to perform data-driven decisions.

Therefore, employees must be motivated to use the observability platform for accessing real-time information to make informed decisions.

  • Step 8: Continuous Performance Evaluation 

Continuous monitoring of the data observability initiative helps assess its effectiveness.

Quantifying the Key Performance Indicators (KPIs) concerning the defined use cases not only highlights the limitations of the current process but also identifies the areas for subsequent improvement.

Mapping the 8-Step Strategy on a Use Case From E-Commerce

The above-presented strategy is mapped below on a use case for an e-commerce organization.

  • Step 1: Need and Goal Assessment

The goal of the e-commerce organization is to increase the knowledge and understanding of the customer’s behavior to improve operational efficiency and optimize inventory management.

  • Step 2: Define Scope and Identify Use Cases

The scope is confined to the order management system, inventory database, and customer service interactions through the e-commerce website, while the corresponding use cases can be:

  1. Internet traffic monitoring
  2. Analyzing customer purchasing patterns
  • Step 3: Establish Data Collection and Integration Procedures

Use data pipelines to collect and integrate data from various sources for real-time analysis, such as:

  1. Website logs
  2. Sustomer service platforms
  3. Transactional databases
  • Step 4: Introduce Data Quality Monitoring Procedures

Implement automated mechanisms to ensure customer and product data accuracy and completeness.

  • Step 5: Leverage Advanced Analytics Techniques

The organization uses machine learning approaches to analyze the customer’s behavior, such as purchase intentions, advice, personalized recommendations, etc.

Moreover, the organization employs visualization tools for representing different views to marketing and operations teams.

  • Step 6: Continuous Improvement Initiatives

The e-commerce organization strengthens collaboration among various departments, such as marketing, operation, and IT, to improve data quality and overcome related challenges.

The organization continuously refines and improves data collection and integration processes based on this collaboration.

  • Step 7: Foster Data-Driven Decision Making

The marketing teams utilize real-time data to get insights into the customers’ behaviors and make decisions about the targeted marketing campaigns.

Likewise, based on demand patterns, the organization takes initiatives to optimize the inventory management processes.

  • Step 8: Continuous Performance Evaluation 

The e-commerce organization continuously monitors the performance, for example, in terms of conversion rates, customer satisfaction, etc., based on the observed data patterns and adjusts its strategies.

Challenges in Redefining Data Democratization With Applied Observability

  • Increased data complexity: Since data democratization emphasizes providing organization-wide access to data, the difficulty in managing data increases without sophisticated data observability approaches and tools.
  • Data integrity: making data accessible to a broader audience (technical and non-technical) makes it susceptible to integrity issues. Therefore, effective data observability tools are crucial to effectively monitor and track the flow of data.
  • Privacy and security: Privacy and security are among the other significant challenges of data democratization. The observability tools should enforce controlled data access and strictly adhere to data governance procedures.
  • Cultural transformation: cultural change within an organization is essential after data democratization to make stakeholders aware of the significance of data. This requires extensive training programs to adopt data democratization with applied observability successfully.
  • Data architecture: Strong technical foundations are needed to democratize data with observability. Organizations must invest in a highly flexible and reliable data platform, tools, or technologies capable of providing easy access to monitoring and analysis to enable effective democratization.

The Bottom Line

In conclusion, redefining data democratization with applied observability as a transformative approach has huge potential for organizations to take analytics to the next level.

Organizations can provide individuals at all levels with timely access to user data through efficient techniques for monitoring pipelines, ensuring a high degree of data quality and integrity, and promoting transparency.

Moreover, it encourages a culture of data-driven decision-making, enables informed decisions, and drives innovation in organizations.


Related Reading

Related Terms

Assad Abbas
Tenured Associate Professor
Assad Abbas
Tenured Associate Professor

Dr Assad Abbas received his PhD from North Dakota State University (NDSU), USA. He is a tenured Associate Professor in the Department of Computer Science at COMSATS University Islamabad (CUI), Islamabad campus, Pakistan. Dr. Abbas has been associated with COMSATS since 2004. His research interests are mainly but not limited to smart health, big data analytics, recommender systems, patent analytics and social network analysis. His research has been published in several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Systems Journal, IEEE Journal of Biomedical and Health Informatics,…