However, how do you ensure your data is squeaky clean and free from errors, inconsistencies, and inaccuracies? That’s the question we’ll explore in this blog as we prepare for our upcoming webinar,” Data Assurance: The Essential Ingredient for Data-Driven Decision Making.”

The Data Dilemma

Data comes from various sources and often arrives in different formats and structures. Whether you’re a small startup or a large enterprise, managing this influx of data can be overwhelming. Many organizations face common challenges:

1. Data Inconsistencies: Data from different sources may use varying formats, units, or terminologies, making it challenging to consolidate and analyze.

2. Data Errors: Even the most careful data entry can result in occasional errors. These errors can propagate throughout your systems and lead to costly mistakes.

3. Data Security: With data breaches and cyber threats on the rise, ensuring the security of your data is paramount. Safeguarding sensitive information is a top concern.

4. Compliance: Depending on your industry, you may need to comply with specific data regulations. Non-compliance can result in hefty fines and a damaged reputation.

The Scrubbing Approach

One way to tackle data quality issues is through data scrubbing. Data scrubbing involves identifying and correcting errors and inconsistencies in your data. This process includes tasks such as:

1. Data Cleansing: Identifying and rectifying inaccuracies or inconsistencies in your data, such as misspellings, duplicate records, or missing values.

2. Data Standardization: Converting data into a consistent format or unit, making it easier to compare and analyze.

3. Data Validation: Checking data against predefined rules to ensure it meets specific criteria or business requirements.

4. Data Enrichment: Enhancing your data with additional information or context to improve its value.

While data scrubbing is a crucial step in data quality management, it often requires manual effort and can be time-consuming, especially for large datasets. Additionally, it may not address all data quality challenges, such as security or compliance concerns.

The Testing Approach

On the other hand, data testing focuses on verifying the quality of your data through systematic testing processes. This approach includes:

1. Data Profiling: Analyzing your data to understand its structure, content, and quality, helping you identify potential issues.

2. Data Validation: Executing validation checks to ensure data conforms to defined rules and criteria.

3. Data Security Testing: Assessing data security measures to identify vulnerabilities and ensure data protection.

4. Data Compliance Testing: Ensuring that data adheres to relevant regulations and compliance standards.

Data testing leverages automation and predefined test cases to efficiently evaluate data quality. It provides a proactive way to catch data issues before they impact your business operations or decision-making processes.

Dive into the world of data assurance and understand why it’s a standalone practice in data-driven success.

Data is the most valuable asset for any business in a highly competitive and fast-moving world. Maintaining the integrity and quality of your business data is therefore crucial. However, ensuring data quality assurance often comes with its own set of challenges.

Lack of data standardization: One of the biggest challenges in data quality management is that data sets are often non-standardized, coming in from disparate sources and stored in different, inconsistent formats across departments.

Data is vulnerable: Data breaches and malware are everywhere, making your important business data vulnerable. To ensure data quality is maintained well, the right tools must be used to mask, protect, and validate data assets.

Data is often too complex: With hybrid enterprise architectures on the rise, the magnitude and complexity of inter-related data is increasing, leading to further intricacies in data quality management.

Data is outdated and inaccurate: Incorrect, inconsistent, and old business data can lead to inaccurate forecasts, poor decision making, and business outcomes.

Heterogenous Data Sources We Work With Seamlessly

With iDAF, you can streamline data assurance across multiple heterogeneous data sets, avoid data quality issues arising during the production stage, completely remove the inaccuracy and inconsistency of sample-based testing, and increase 100% data coverage.

iDAF leverages the best open-source big data tools to perform base checks, data completeness, business validation, reports testing, and 100% data accuracy.

We leverage iDAF to carry out automated validation between target and source datasets for

1. Data Quality

2. Data Completeness

3. Data Integrity

4. Data Consistency

The Perfect Blend

So, should you choose data scrubbing or data testing? Well, the answer may lie in a combination of both.

1. Scrubbing for Cleanup: Use data scrubbing to clean and prepare your data initially. This step is essential for eliminating known issues and improving data consistency.

2. Testing for Ongoing Assurance: Implement data testing as an ongoing process to continuously monitor and validate your data. This ensures that data quality remains high over time.

Join us in our upcoming webinar, “Data Assurance: The Secret Sauce Behind Data-Driven Decisions,” where we’ll delve deeper into these approaches. We’ll explore real-world examples, best practices, and the role of automation in maintaining clean, reliable data. Discover how the right combination of data scrubbing and testing can empower your organization to harness the full potential of your data.

The post Scrub or Test: What Helps in Ensuring You Have the Cleanest Data appeared first on Indium.

What is Resiliency Testing

Resiliency testing is a type of testing performed to assess the ability of a system or application to recover from various types of failures and continue to operate in a degraded state without completely shutting down or losing data. The purpose of resiliency testing is to identify potential weaknesses or vulnerabilities in a system and to test how well it can recover from various types of failures, such as hardware failures, network failures, software failures, cyberattacks, and other types of disruptions.

Resiliency testing can involve simulating various types of failures or disruptions and observing how the system responds. The testing may include conducting controlled experiments in a test environment or conducting real-world simulations to assess the system’s resilience under actual operating conditions.

The goal of resiliency testing is to ensure that a system can continue to operate with minimal interruption or downtime, even in the face of unexpected events or disruptions.

What is Chaos Engineering

Chaos engineering is a software testing methodology that involves intentionally introducing controlled and carefully designed disruptions or failures into a system to observe how it responds and to identify potential weaknesses or vulnerabilities. The goal of chaos engineering is to improve the resilience and reliability of complex systems, such as distributed computing systems, cloud-based systems, and microservices architectures.

Chaos engineering typically involves the following steps:

• Identify the components of the system to be tested and the potential failure scenarios.
• Create experiments that introduce controlled disruptions, such as killing a server or disconnecting a network connection.
• Run the experiments and observe how the system responds.
• Analyse the results and identify areas where the system can be improved to better handle failures.
• Chaos engineering is based on the idea that failures and disruptions are inevitable in complex systems and that by deliberately introducing controlled failures, system designers can learn how to design more resilient and reliable systems. By identifying and addressing weaknesses before they lead to real-world failures or outages, chaos engineering can help to prevent costly downtime, data loss, and other negative impacts.

Both resilience testing and chaos engineering are important tools for improving the reliability and resilience of complex systems. By identifying and addressing weaknesses in a system, organizations can reduce the risk of downtime, data loss, and other negative impacts, and ensure that their systems can continue to operate even in the face of unexpected disruptions.

Key Benefits:

• Aid us to quickly spot, isolate & fix single point of failures in the application
• Meets Quality of Service (QoS) to higher standards
• Application Down time cost is considerably minimized. Reduce MTTD & MTTR
• Helps to enable strong resilient features with auto-healing capabilities.

Focus Areas:

• CPU, Memory, Disk, I/O attacks
• Restart/shutdown
• Network Latency Delay/Packet Loss
• Process Crash
• Black Hole/App Delays

Tool Set:

Chaos Engg:

Mangle, Simian Army (includes Chaos monkey), Gremlin, Chaos Blade, Nagarro’s Chaos framework Cloud: Fault Injection Simulator (FIS) – AWS; Chaos Data Studio Service – Azure; Gremlin from Marketplace – GCP

Also read: The Role of Digital Assurance in Accessibility and Inclusion

In the future, we can expect that chaos engineering will continue to grow in importance as more and more critical systems become increasingly complex and interconnected. As systems become more complex, they become harder to predict and harder to control, and so the risks associated with system failures increase. Chaos engineering will play a key role in helping organizations identify and mitigate these risks by allowing them to test their systems in a controlled environment and identify potential weaknesses before they become real-world problems.

Additionally, we will see a continued evolution of chaos engineering techniques and tools, including the development of new approaches to chaos engineering that consider the unique characteristics of specific systems and environments. We will also see continued integration of chaos engineering into DevOps and agile development methodologies, allowing organizations to build resilience and reliability into their systems from the ground up.

Overall, I believe that chaos engineering will continue to play an increasingly important role in ensuring the reliability and resilience of complex systems in the years to come.

The post Resiliency testing and Chaos Engineering appeared first on Indium.

What is Data Governance? A McKinsey study points to 15 to 25 percent growth and increase in EBITDA in companies that use data-driven B2B sales-growth engines.

While this is good news, what is of greater importance is to know that data collection is only Step #1. Managing and organizing data is essential to reap the expected benefits, and the larger the volume of data, the greater the need for data organization and management.

And, there is more. Data management and organization are just the tip of the iceberg. Businesses need to set in place policies and processes around making the data available and usable while ensuring its integrity and security. This is where data governance plays a crucial role. It ensures data consistency and trustworthiness while protecting data from being breached and misused. They need data governance.

What is Data Governance?

Data Governance refers to the corporate view on a variety of aspects related to data engineering services, including:

● The collection processes

● Roles and responsibilities of the employees accessing the data

● Policies and standards

● Metrics to measure data usage and ensure its effectiveness and efficiency in achieving goals

A data governance program is designed by bringing together a steering committee consisting of the executives, IT,, and data management teams. They create the policies and standards that govern data collection and usage processes, while data stewards implement and enforce the procedures.

Challenges to Data Governance

Data governance is mandatory and guided by regulatory requirements to protect data privacy and security. However, having an effective and efficiency data governance practice is a complex process. It requires a common understanding of key data entities among the different stakeholders, requiring common data definitions and formats.

Second and key challenge is in determining the business value, which can make it difficult to get the required approvals, budgetary allocation, and support of the key stakeholders.

To demonstrate the business value of data governance on an ongoing basis, quantifiable metrics need to be identified, established, and communicated to the rest of the organization.

One of the advantages of cloud-based data access and modern technologies is that data is now available to the business users for self-service BI and analytics. This has added to the complexity of data governance challenges of ensuring data is not misused or breach data privacy and security requirements. Streaming data in real-time analytics is another compilation where accuracy, privacy, and security are not impacted.

Big data analytics solutions with the amalgamation of structured, semi-structured, and unstructured data adds another layer of complexity to governance, which traditionally dealt only with structured data.

Siloed data, lack of resources, poor quality of data are among some of the other challenges that make data governance complex.

Must read: Data Governance and Security of Cloud Data Warehouse

3-Step Guide to Improve Data Governance

To ensure effective governance and overcome the challenges, businesses need a structured approach. This includes:

1. Identifying Distinct Use Cases: Understand the benefits, costs, and risks of governance to be able to make a business case and allocate the necessary resources. By identifying the use cases, engaging the stakeholders becomes easier and ensures a more comprehensive data governance framework that addresses meaningful issues.

2. Quantifying Value: Assign a quantifiable value to the key performance indicators and monitor the KPIs helps to:

– Assess the effectiveness of data governance framework

– Strengthen the need to align processes with the frameworks where it proves to be effective

– Identify areas of improvement to improve it further

3. Improve Scalable Data Capabilities: Clearly outline the capabilities users require to improve the value and usage of data based on their specific needs. Empower the users with the required technology and processes such as intuitive and searchable catalog that helps discover data assets, enhanced data security, accurate date, and understand data origin, classification, content, and use to:

● Collaborate across the functions for sharing data assets

● Improve internal and external regulatory compliance

You might be interested in: Why Data Fabric is the key to next-gen Data Management

Benefits of Data Governance

An effective data governance strategy provides many benefits to an organization, including:

● Data Consistency: Provides a consistent view of data with common terminology to different business units while enabling flexibles use based on need

● Data Quality: Provides access to accurate, complete, and consistent data

● Data Mapping: Makes it easy to location all data associated with key entities for faster access.

● Single Version of Truth: Often data siloes fragment the view of data, impacting business outcomes. Data governance enables unifying data for providing a holistic view of business operations to improve decision-making.

● Improved Compliance: Meet the requirements of regulations and standards such as the US HIPAA (Health Insurance Portability and Accountability Act), the EU General Data Protection Regulation (GDPR), and industry standards such as PCI DSS (Payment Card Industry Data Security Standards).

Indium to Help Build Data Governance Framework

Indium Software is a digital engineering expert with specialized expertise in data science services, data engineering services, and data lifecycle management services. We help businesses establish a data governance framework by understanding their business needs, identifying their data sources, and creating a centralized data repository to improve data management, organization, and stewardship while ensuring compliance, privacy, and security.

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